TQTune / data_prep.py

Model save

9564ed2 verified over 1 year ago

15.8 kB

import typing as T
import os
import sys
import argparse
import json
import nflx_copilot as ncp
import pandas as pd
import re

sys.path.append("/root/workspace")

from timedtext.adapters.translation.generation.pldl import TimedTextAdapter, ConverterDialogContext
from timedtext.manager import TimedTextManager
from timedtext.handlers import OriginalLanguagePivotLanguageHandler, EnglishTemplateSubtitleHandler
from timedprompts.evaluation.pldl_prompt_one.prompt import (
    ReferenceFreeFeedbackTransform,
    ContextFreeFeedbackTransform,
    ReferenceFreeDirectTransform,
    ReferenceBasedFeedbackTransform,
    ReferenceFreeExampleTransform,
)
from tqdm import tqdm
from timedtune.convert.tq_for_pldl.pldl_train_one import PldlTrainOneReferenceFreeTransform
from timedtext.adapters.translation.evaluation import compute_score_delta

def compute_32_point_score(response, generation):
    parsed, score = {}, -1
    try:
        score = (
            int(response["Accuracy Score"])
            + int(response["Readability Score"])
            + compute_score_delta(response, "Accuracy Issues", generation)
            + compute_score_delta(response, "Readability Issues", generation)
        )
        score = score * 4
    except:
        score = -1
    return parsed, score

# Your existing TimedTextAdapter and helper classes
class TimedTextAdapterFromCache_PLDL(TimedTextAdapter):
    def __init__(
        self,
        data_dir: str,
        cache_size: int = 0,
        ol_dialog_list_version: str = "",
        pl_dialog_list_version: str = "",
        ol_dialog_list_pl_dialog_list_version: str = "",
        num_prev_events: int = 16,
        num_next_events: int = 16,
    ) -> None:
        super().__init__(num_prev_events, num_next_events)
        self.timed_text_manager = TimedTextManager(
            data_dir,
            cache_size=cache_size,
            ol_dialog_list_version=ol_dialog_list_version,
            pl_dialog_list_version=pl_dialog_list_version,
            ol_dialog_list_pl_dialog_list_version=ol_dialog_list_pl_dialog_list_version,
        )

    def _get_timed_text(
        self, movie_id: int, start_frame: int, end_frame: int, src_lang: str, tgt_lang: str
    ) -> T.Dict[str, T.Union[T.Dict, T.List[T.Dict]]]:
        results = self.timed_text_manager.match_and_get_timed_text(
            handler_class=OriginalLanguagePivotLanguageHandler,
            movie_id=movie_id,
            start_frame=start_frame,
            end_frame=end_frame,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            mid_lang="",
            **self.timed_text_kwargs,
        )

        curr_srcs = [result["curr"]["src"]["txt"] for result in results]
        curr_tgts = [result["curr"]["tgt"]["txt"] for result in results]

        return {
            "curr": {"src": {"txt": "\n\n".join(curr_srcs)}, "tgt": {"txt": "\n\n".join(curr_tgts)}},
            "prev": results[0]["prev"],
            "next": results[-1]["next"],
        }

class TimedTextAdapterFromCache_SUBS(TimedTextAdapter):
    def __init__(
        self,
        data_dir: str,
        cache_size: int = 0,
        num_prev_events: int = 16,
        num_next_events: int = 16,
    ) -> None:
        super().__init__(num_prev_events, num_next_events)
        self.timed_text_manager = TimedTextManager(
            data_dir,
            cache_size=cache_size,
        )

    def _get_timed_text(
        self, movie_id: int, start_frame: int, end_frame: int, src_lang: str, tgt_lang: str
    ) -> T.Dict[str, T.Union[T.Dict, T.List[T.Dict]]]:
        results = self.timed_text_manager.match_and_get_timed_text(
            handler_class=EnglishTemplateSubtitleHandler,
            movie_id=movie_id,
            start_frame=start_frame,
            end_frame=end_frame,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            mid_lang="",
            **self.timed_text_kwargs,
        )

        curr_srcs = [result["curr"]["src"]["txt"] for result in results]
        curr_tgts = [result["curr"]["tgt"]["txt"] for result in results]

        return {
            "curr": {"src": {"txt": "\n\n".join(curr_srcs)}, "tgt": {"txt": "\n\n".join(curr_tgts)}},
            "prev": results[0]["prev"],
            "next": results[-1]["next"],
        }


# Function to fetch contextual information using TimedTextAdapter
def fetch_contextual_information(timed_text_adapter, row):
    """
    Fetches the required context information for each sample using timed_text_adapter.
    
    Args:
    timed_text_adapter (TimedTextAdapterFromCache): Adapter to fetch data from.
    row (dict): Row containing the necessary information to fetch the context.
    
    Returns:
    dict: Contextual information containing src_text, tgt_text, prev_context, next_context, src_prev, src_next, tgt_prev, tgt_next.
    """
    # Fetching the actual translation context
    src_text, tgt_text, prev_context, next_context = timed_text_adapter.get_timed_text(
        movie_id=row["movie_id"],
        start_frame=row["start_frame"],
        end_frame=row["end_frame"],
        src_lang=row["src_lang"],
        tgt_lang=row["tgt_lang"],
    )

    timed_text_converter = ConverterDialogContext(timed_text_adapter)
    
    # Converting context to the format expected by the prompt
    src_prev, src_next, tgt_prev, tgt_next, _ = timed_text_converter.__context__(
        row["src_lang"], row["tgt_lang"], prev_context, next_context, None
    )
    
    return {
        "tt_src_text": src_text,
        "tt_tgt_text": tgt_text,
        "tt_src_prev": src_prev,
        "tt_src_next": src_next,
        "tt_tgt_prev": tgt_prev,
        "tt_tgt_next": tgt_next,
    }

def transform_json(input_json):
    # Get the first project key
    project_key = list(input_json['projects'].keys())[0]
    project = input_json['projects'][project_key]
    
    final_output = {"labelers": []}
    # Process each label
    for index, label in enumerate(project['labels']):
        # Initialize output structure
        output = {
            "annotation": {
                "Accuracy Issues": [],
                "Readability Issues": [],
                "Accuracy Score": "",
                "Readability Score": "",
                "Confidence Level": "",
                "Main Vs Alternate": "",
                "Score": "-1"  # initalized -1, will be updated in next steps
            },
        }        
        # Process annotations/objects (issues)
        if 'objects' in label['annotations']:
            for obj in label['annotations']['objects']:
                issue = {
                    "Error Location": obj['conversational_location']['message_id'],
                    "Error Span": [
                        obj['conversational_location']['location']['start'],
                        obj['conversational_location']['location']['end']
                    ],
                    "Error Explanation": "",
                    "Error Quality Category": obj['name'],
                    "Error Quality Tags": [],
                    "Error Severity": ""
                }
                
                # Process classifications within object
                for classification in obj['classifications']:
                    if classification['name'] == 'Explanation':
                        issue["Error Explanation"] = classification['text_answer']['content']
                    elif classification['name'] == 'Quality Tag':
                        issue["Error Quality Tags"] = [ans['name'].lower() for ans in classification['checklist_answers']]
                    elif classification['name'] == 'Quality SubCategory':
                        severity = classification['radio_answer']['name']
                        if 'Major' in severity:
                            issue["Error Severity"] = "Major"
                        else:
                            issue["Error Severity"] = "Minor"
                
                # Add to appropriate issues list
                if obj['name'] == 'Style':
                    output['annotation']['Readability Issues'].append(issue)
                else:
                    output['annotation']['Accuracy Issues'].append(issue)

        # Process classifications
        for classification in label['annotations']['classifications']:
            if classification['name'] == 'Accuracy Score':
                output['annotation']['Accuracy Score'] = classification['radio_answer']['name'].split(' - ')[0]
            elif classification['name'] == 'Readability Score':
                output['annotation']['Readability Score'] = classification['radio_answer']['name'].split(' - ')[0]
            elif classification['name'] == 'Confidence Level':
                output['annotation']['Confidence Level'] = classification['radio_answer']['value']
            elif classification['name'] == 'Main vs Alternate':
                output['annotation']['Main Vs Alternate'] = classification['radio_answer']['name']
        final_output["labelers"].append(output)
    return final_output

# Function to load the relevant meta json for a given key
def load_meta_json(priority_key, data_row_key, meta_path):
    """
    Loads and validates metadata json from the specified path based on the priority key and data row key.
    Args:
    priority_key (str): Priority key from the label metadata.
    data_row_key (str): Data row key to find the relevant file.
    meta_path (str): Path to the metadata folder.
    Returns:
    dict: Loaded metadata.
    """
    with open(os.path.join(meta_path, f'{priority_key}.json')) as fread:
        meta_dict = json.load(fread)

        _, movie_id, start_end_frame, _, _, _, _ = data_row_key.split('.')
        start_frame, end_frame = start_end_frame.split('_')

        if int(meta_dict['movie_id']) != int(movie_id):
            print("Movie Ids didn't match:", int(meta_dict['movie_id']), int(movie_id), os.path.join(meta_path, f'{priority_key}.json'), data_row_key)
            exit(0)
        assert int(meta_dict['start_frame']) == int(start_frame)
        assert int(meta_dict['end_frame']) == int(end_frame)

        return meta_dict

# Main function that processes the data
def process_json(timed_text_adapter, example_row, meta_path, conv_path):
    """
    Takes the full input json, converts it to the required format, and adds context using metadata.
    Args:
    timed_text_adapter (TimedTextAdapterFromCache): Adapter to fetch context.
    example_row (dict): The full input JSON (like the example_row you provided).
    meta_path (str): Path to the metadata folder to fetch meta json.
    Returns:
    dict: The enriched annotation format with context and annotation data.
    """
    # Step 1: Convert the full input JSON to the required annotation format
    annotation_result = transform_json(example_row)
            
    # Extracting the necessary data_row_key and priority_key
    data_row_key = example_row['data_row']['global_key']
    priority_key = example_row['projects'][list(example_row["projects"].keys())[0]]['project_details']['priority']
    
    annotation_result["Data_Row_Key"] = data_row_key
    key = ".".join(data_row_key.split(".")[:3])
    with open(conv_path + "/" + key + ".json") as file:
        data = json.load(file)
        annotation_result["main_tgt_text"]  = data["messages"][0]["content"]
        annotation_result["src_text"]       = data["messages"][1]["content"]
        annotation_result["alt_tgt_text"]   = data["messages"][2]["content"]

    # Load the metadata using the keys from the json
    meta_dict = load_meta_json(priority_key, data_row_key, meta_path)
    
    # Step 2: Add the metadata fields (e.g., title_id, start_frame, end_frame, src_lang, tgt_lang)
    annotation_result.update({
        "title_id": meta_dict['movie_id'],
        "start_frame": meta_dict['start_frame'],
        "end_frame": meta_dict['end_frame'],
        "src_lang": meta_dict['src_lang'],
        "tgt_lang": meta_dict['tgt_lang'],
    })
    
    # Step 3: Fetch contextual information using the given timed_text_adapter
    context_info = fetch_contextual_information(timed_text_adapter, meta_dict)

    annotation_result.update(context_info)

    # Update error spans with actual text for each labeler
    for labeler in annotation_result["labelers"]:
        # Process Accuracy Issues
        for issue in labeler["annotation"]["Accuracy Issues"]:
            error_location = issue["Error Location"]
            start, end = issue["Error Span"][0], issue["Error Span"][1] 
            
            # Get the actual text based on error location
            if error_location == "src":
                actual_text = annotation_result["src_text"][start:end]
            else:  # tgt
                actual_text = annotation_result["main_tgt_text"][start:end]
            
            # Update the error span with actual text
            issue["Error Span"] = actual_text
    
        # Process Readability Issues
        for issue in labeler["annotation"]["Readability Issues"]:
            error_location = issue["Error Location"]
            start, end = issue["Error Span"]
            
            # Get the actual text based on error location
            if error_location == "src":
                actual_text = annotation_result["src_text"][start:end]
            else:  # tgt
                actual_text = annotation_result["main_tgt_text"][start:end]
            
            # Update the error span with actual text
            issue["Error Span"] = actual_text

    return annotation_result

# Example usage
def main():
    base_path  = "MT_TQ/Caches/May2025/tquality.annotated.data/"
    json_files = [base_path + "raw/" + f for f in os.listdir(base_path + "raw/") if f.endswith('.json')]
    
    for json_file in tqdm(json_files):
        if "calibration" in json_file:
            print("Warning: Skipping Calibration Data, Remove this if you want to use Calibration data")
            continue

        if "PLDL" in json_file:
            folder = "pldl"
            timed_text_adapter = TimedTextAdapterFromCache_PLDL(
                data_dir="/fsx_l10n/l10n_dse_timedtext/cache", num_prev_events=32, num_next_events=32
            )
        elif "SUBS" in json_file:
            folder = "subs"
            timed_text_adapter = TimedTextAdapterFromCache_SUBS(
                data_dir="/fsx_l10n/l10n_dse_timedtext/cache", num_prev_events=32, num_next_events=32
            )
        else:
            folder = ""
            assert "invalid json file"
        
        langs_type   = json_file.split("/")[-1].split("-")[1].replace("_",".")
        phase        = json_file.split("/")[-1].split("-")[3]
        phase_number = int(''.join(re.findall(r'\d+', phase))) if re.findall(r'\d+', phase) else None
        phase_date   = json_file.split("/")[-1].split("-")[4].replace(".json", "")

        zzmetapath   = f"/root/notebooks/MT_TQ/Caches/labelspace/tquality.zzmeta.data/{folder}/{langs_type}/phase {phase_number} - {phase_date}"
        
        meta_path = zzmetapath + "/meta"
        conv_path = zzmetapath + "/conv"
        
        with open(json_file) as file:
            data = json.load(file)
    
        output_data = []
        for data_point in tqdm(data):
            annotation_result = process_json(timed_text_adapter, data_point, meta_path, conv_path)
            for labeler in annotation_result["labelers"]:
                _, score = compute_32_point_score(labeler["annotation"], annotation_result["main_tgt_text"])
                labeler["annotation"]["Score"] = score
                
            output_data.append(annotation_result)
    
        with open(base_path + "parsed/" + json_file.split("/")[-1], 'w') as json_file:
            json.dump({"data": output_data}, json_file, indent=4)

if __name__ == "__main__":
    main()

	import typing as T
	import os
	import sys
	import argparse
	import json
	import nflx_copilot as ncp
	import pandas as pd
	import re

	sys.path.append("/root/workspace")

	from timedtext.adapters.translation.generation.pldl import TimedTextAdapter, ConverterDialogContext
	from timedtext.manager import TimedTextManager
	from timedtext.handlers import OriginalLanguagePivotLanguageHandler, EnglishTemplateSubtitleHandler
	from timedprompts.evaluation.pldl_prompt_one.prompt import (
	ReferenceFreeFeedbackTransform,
	ContextFreeFeedbackTransform,
	ReferenceFreeDirectTransform,
	ReferenceBasedFeedbackTransform,
	ReferenceFreeExampleTransform,
	)
	from tqdm import tqdm
	from timedtune.convert.tq_for_pldl.pldl_train_one import PldlTrainOneReferenceFreeTransform
	from timedtext.adapters.translation.evaluation import compute_score_delta

	def compute_32_point_score(response, generation):
	parsed, score = {}, -1
	try:
	score = (
	int(response["Accuracy Score"])
	+ int(response["Readability Score"])
	+ compute_score_delta(response, "Accuracy Issues", generation)
	+ compute_score_delta(response, "Readability Issues", generation)
	)
	score = score * 4
	except:
	score = -1
	return parsed, score

	# Your existing TimedTextAdapter and helper classes
	class TimedTextAdapterFromCache_PLDL(TimedTextAdapter):
	def __init__(
	self,
	data_dir: str,
	cache_size: int = 0,
	ol_dialog_list_version: str = "",
	pl_dialog_list_version: str = "",
	ol_dialog_list_pl_dialog_list_version: str = "",
	num_prev_events: int = 16,
	num_next_events: int = 16,
	) -> None:
	super().__init__(num_prev_events, num_next_events)
	self.timed_text_manager = TimedTextManager(
	data_dir,
	cache_size=cache_size,
	ol_dialog_list_version=ol_dialog_list_version,
	pl_dialog_list_version=pl_dialog_list_version,
	ol_dialog_list_pl_dialog_list_version=ol_dialog_list_pl_dialog_list_version,
	)

	def _get_timed_text(
	self, movie_id: int, start_frame: int, end_frame: int, src_lang: str, tgt_lang: str
	) -> T.Dict[str, T.Union[T.Dict, T.List[T.Dict]]]:
	results = self.timed_text_manager.match_and_get_timed_text(
	handler_class=OriginalLanguagePivotLanguageHandler,
	movie_id=movie_id,
	start_frame=start_frame,
	end_frame=end_frame,
	src_lang=src_lang,
	tgt_lang=tgt_lang,
	mid_lang="",
	**self.timed_text_kwargs,
	)

	curr_srcs = [result["curr"]["src"]["txt"] for result in results]
	curr_tgts = [result["curr"]["tgt"]["txt"] for result in results]

	return {
	"curr": {"src": {"txt": "\n\n".join(curr_srcs)}, "tgt": {"txt": "\n\n".join(curr_tgts)}},
	"prev": results[0]["prev"],
	"next": results[-1]["next"],
	}

	class TimedTextAdapterFromCache_SUBS(TimedTextAdapter):
	def __init__(
	self,
	data_dir: str,
	cache_size: int = 0,
	num_prev_events: int = 16,
	num_next_events: int = 16,
	) -> None:
	super().__init__(num_prev_events, num_next_events)
	self.timed_text_manager = TimedTextManager(
	data_dir,
	cache_size=cache_size,
	)

	def _get_timed_text(
	self, movie_id: int, start_frame: int, end_frame: int, src_lang: str, tgt_lang: str
	) -> T.Dict[str, T.Union[T.Dict, T.List[T.Dict]]]:
	results = self.timed_text_manager.match_and_get_timed_text(
	handler_class=EnglishTemplateSubtitleHandler,
	movie_id=movie_id,
	start_frame=start_frame,
	end_frame=end_frame,
	src_lang=src_lang,
	tgt_lang=tgt_lang,
	mid_lang="",
	**self.timed_text_kwargs,
	)

	curr_srcs = [result["curr"]["src"]["txt"] for result in results]
	curr_tgts = [result["curr"]["tgt"]["txt"] for result in results]

	return {
	"curr": {"src": {"txt": "\n\n".join(curr_srcs)}, "tgt": {"txt": "\n\n".join(curr_tgts)}},
	"prev": results[0]["prev"],
	"next": results[-1]["next"],
	}


	# Function to fetch contextual information using TimedTextAdapter
	def fetch_contextual_information(timed_text_adapter, row):
	"""
	Fetches the required context information for each sample using timed_text_adapter.

	Args:
	timed_text_adapter (TimedTextAdapterFromCache): Adapter to fetch data from.
	row (dict): Row containing the necessary information to fetch the context.

	Returns:
	dict: Contextual information containing src_text, tgt_text, prev_context, next_context, src_prev, src_next, tgt_prev, tgt_next.
	"""
	# Fetching the actual translation context
	src_text, tgt_text, prev_context, next_context = timed_text_adapter.get_timed_text(
	movie_id=row["movie_id"],
	start_frame=row["start_frame"],
	end_frame=row["end_frame"],
	src_lang=row["src_lang"],
	tgt_lang=row["tgt_lang"],
	)

	timed_text_converter = ConverterDialogContext(timed_text_adapter)

	# Converting context to the format expected by the prompt
	src_prev, src_next, tgt_prev, tgt_next, _ = timed_text_converter.__context__(
	row["src_lang"], row["tgt_lang"], prev_context, next_context, None
	)

	return {
	"tt_src_text": src_text,
	"tt_tgt_text": tgt_text,
	"tt_src_prev": src_prev,
	"tt_src_next": src_next,
	"tt_tgt_prev": tgt_prev,
	"tt_tgt_next": tgt_next,
	}

	def transform_json(input_json):
	# Get the first project key
	project_key = list(input_json['projects'].keys())[0]
	project = input_json['projects'][project_key]

	final_output = {"labelers": []}
	# Process each label
	for index, label in enumerate(project['labels']):
	# Initialize output structure
	output = {
	"annotation": {
	"Accuracy Issues": [],
	"Readability Issues": [],
	"Accuracy Score": "",
	"Readability Score": "",
	"Confidence Level": "",
	"Main Vs Alternate": "",
	"Score": "-1" # initalized -1, will be updated in next steps
	},
	}
	# Process annotations/objects (issues)
	if 'objects' in label['annotations']:
	for obj in label['annotations']['objects']:
	issue = {
	"Error Location": obj['conversational_location']['message_id'],
	"Error Span": [
	obj['conversational_location']['location']['start'],
	obj['conversational_location']['location']['end']
	],
	"Error Explanation": "",
	"Error Quality Category": obj['name'],
	"Error Quality Tags": [],
	"Error Severity": ""
	}

	# Process classifications within object
	for classification in obj['classifications']:
	if classification['name'] == 'Explanation':
	issue["Error Explanation"] = classification['text_answer']['content']
	elif classification['name'] == 'Quality Tag':
	issue["Error Quality Tags"] = [ans['name'].lower() for ans in classification['checklist_answers']]
	elif classification['name'] == 'Quality SubCategory':
	severity = classification['radio_answer']['name']
	if 'Major' in severity:
	issue["Error Severity"] = "Major"
	else:
	issue["Error Severity"] = "Minor"

	# Add to appropriate issues list
	if obj['name'] == 'Style':
	output['annotation']['Readability Issues'].append(issue)
	else:
	output['annotation']['Accuracy Issues'].append(issue)

	# Process classifications
	for classification in label['annotations']['classifications']:
	if classification['name'] == 'Accuracy Score':
	output['annotation']['Accuracy Score'] = classification['radio_answer']['name'].split(' - ')[0]
	elif classification['name'] == 'Readability Score':
	output['annotation']['Readability Score'] = classification['radio_answer']['name'].split(' - ')[0]
	elif classification['name'] == 'Confidence Level':
	output['annotation']['Confidence Level'] = classification['radio_answer']['value']
	elif classification['name'] == 'Main vs Alternate':
	output['annotation']['Main Vs Alternate'] = classification['radio_answer']['name']
	final_output["labelers"].append(output)
	return final_output

	# Function to load the relevant meta json for a given key
	def load_meta_json(priority_key, data_row_key, meta_path):
	"""
	Loads and validates metadata json from the specified path based on the priority key and data row key.

	Args:
	priority_key (str): Priority key from the label metadata.
	data_row_key (str): Data row key to find the relevant file.
	meta_path (str): Path to the metadata folder.

	Returns:
	dict: Loaded metadata.
	"""
	with open(os.path.join(meta_path, f'{priority_key}.json')) as fread:
	meta_dict = json.load(fread)

	_, movie_id, start_end_frame, _, _, _, _ = data_row_key.split('.')
	start_frame, end_frame = start_end_frame.split('_')

	if int(meta_dict['movie_id']) != int(movie_id):
	print("Movie Ids didn't match:", int(meta_dict['movie_id']), int(movie_id), os.path.join(meta_path, f'{priority_key}.json'), data_row_key)
	exit(0)
	assert int(meta_dict['start_frame']) == int(start_frame)
	assert int(meta_dict['end_frame']) == int(end_frame)

	return meta_dict

	# Main function that processes the data
	def process_json(timed_text_adapter, example_row, meta_path, conv_path):
	"""
	Takes the full input json, converts it to the required format, and adds context using metadata.

	Args:
	timed_text_adapter (TimedTextAdapterFromCache): Adapter to fetch context.
	example_row (dict): The full input JSON (like the example_row you provided).
	meta_path (str): Path to the metadata folder to fetch meta json.

	Returns:
	dict: The enriched annotation format with context and annotation data.
	"""
	# Step 1: Convert the full input JSON to the required annotation format
	annotation_result = transform_json(example_row)

	# Extracting the necessary data_row_key and priority_key
	data_row_key = example_row['data_row']['global_key']
	priority_key = example_row['projects'][list(example_row["projects"].keys())[0]]['project_details']['priority']

	annotation_result["Data_Row_Key"] = data_row_key
	key = ".".join(data_row_key.split(".")[:3])
	with open(conv_path + "/" + key + ".json") as file:
	data = json.load(file)
	annotation_result["main_tgt_text"] = data["messages"][0]["content"]
	annotation_result["src_text"] = data["messages"][1]["content"]
	annotation_result["alt_tgt_text"] = data["messages"][2]["content"]

	# Load the metadata using the keys from the json
	meta_dict = load_meta_json(priority_key, data_row_key, meta_path)

	# Step 2: Add the metadata fields (e.g., title_id, start_frame, end_frame, src_lang, tgt_lang)
	annotation_result.update({
	"title_id": meta_dict['movie_id'],
	"start_frame": meta_dict['start_frame'],
	"end_frame": meta_dict['end_frame'],
	"src_lang": meta_dict['src_lang'],
	"tgt_lang": meta_dict['tgt_lang'],
	})

	# Step 3: Fetch contextual information using the given timed_text_adapter
	context_info = fetch_contextual_information(timed_text_adapter, meta_dict)

	annotation_result.update(context_info)

	# Update error spans with actual text for each labeler
	for labeler in annotation_result["labelers"]:
	# Process Accuracy Issues
	for issue in labeler["annotation"]["Accuracy Issues"]:
	error_location = issue["Error Location"]
	start, end = issue["Error Span"][0], issue["Error Span"][1]

	# Get the actual text based on error location
	if error_location == "src":
	actual_text = annotation_result["src_text"][start:end]
	else: # tgt
	actual_text = annotation_result["main_tgt_text"][start:end]

	# Update the error span with actual text
	issue["Error Span"] = actual_text

	# Process Readability Issues
	for issue in labeler["annotation"]["Readability Issues"]:
	error_location = issue["Error Location"]
	start, end = issue["Error Span"]

	# Get the actual text based on error location
	if error_location == "src":
	actual_text = annotation_result["src_text"][start:end]
	else: # tgt
	actual_text = annotation_result["main_tgt_text"][start:end]

	# Update the error span with actual text
	issue["Error Span"] = actual_text

	return annotation_result

	# Example usage
	def main():
	base_path = "MT_TQ/Caches/May2025/tquality.annotated.data/"
	json_files = [base_path + "raw/" + f for f in os.listdir(base_path + "raw/") if f.endswith('.json')]

	for json_file in tqdm(json_files):
	if "calibration" in json_file:
	print("Warning: Skipping Calibration Data, Remove this if you want to use Calibration data")
	continue

	if "PLDL" in json_file:
	folder = "pldl"
	timed_text_adapter = TimedTextAdapterFromCache_PLDL(
	data_dir="/fsx_l10n/l10n_dse_timedtext/cache", num_prev_events=32, num_next_events=32
	)
	elif "SUBS" in json_file:
	folder = "subs"
	timed_text_adapter = TimedTextAdapterFromCache_SUBS(
	data_dir="/fsx_l10n/l10n_dse_timedtext/cache", num_prev_events=32, num_next_events=32
	)
	else:
	folder = ""
	assert "invalid json file"

	langs_type = json_file.split("/")[-1].split("-")[1].replace("_",".")
	phase = json_file.split("/")[-1].split("-")[3]
	phase_number = int(''.join(re.findall(r'\d+', phase))) if re.findall(r'\d+', phase) else None
	phase_date = json_file.split("/")[-1].split("-")[4].replace(".json", "")

	zzmetapath = f"/root/notebooks/MT_TQ/Caches/labelspace/tquality.zzmeta.data/{folder}/{langs_type}/phase {phase_number} - {phase_date}"

	meta_path = zzmetapath + "/meta"
	conv_path = zzmetapath + "/conv"

	with open(json_file) as file:
	data = json.load(file)

	output_data = []
	for data_point in tqdm(data):
	annotation_result = process_json(timed_text_adapter, data_point, meta_path, conv_path)
	for labeler in annotation_result["labelers"]:
	_, score = compute_32_point_score(labeler["annotation"], annotation_result["main_tgt_text"])
	labeler["annotation"]["Score"] = score

	output_data.append(annotation_result)

	with open(base_path + "parsed/" + json_file.split("/")[-1], 'w') as json_file:
	json.dump({"data": output_data}, json_file, indent=4)

	if __name__ == "__main__":
	main()