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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image/Text processor class for ALIGN
"""

from ...processing_utils import ProcessingKwargs, ProcessorMixin


class AlignProcessorKwargs(ProcessingKwargs, total=False):
    # see processing_utils.ProcessingKwargs documentation for usage.
    _defaults = {
        "text_kwargs": {
            "padding": "max_length",
            "max_length": 64,
        },
    }


class AlignProcessor(ProcessorMixin):
    r"""
    Constructs an ALIGN processor which wraps [`EfficientNetImageProcessor`] and
    [`BertTokenizer`]/[`BertTokenizerFast`] into a single processor that inherits both the image processor and
    tokenizer functionalities. See the [`~AlignProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more
    information.
    The preferred way of passing kwargs is as a dictionary per modality, see usage example below.
        ```python
        from transformers import AlignProcessor
        from PIL import Image
        model_id = "kakaobrain/align-base"
        processor = AlignProcessor.from_pretrained(model_id)
        processor(
            images=your_pil_image,
            text=["What is that?"],
            images_kwargs = {"crop_size": {"height": 224, "width": 224}},
            text_kwargs = {"padding": "do_not_pad"},
            common_kwargs = {"return_tensors": "pt"},
        )
        ```
    Args:
        image_processor ([`EfficientNetImageProcessor`]):
            The image processor is a required input.
        tokenizer ([`BertTokenizer`, `BertTokenizerFast`]):
            The tokenizer is a required input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "EfficientNetImageProcessor"
    tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
    valid_processor_kwargs = AlignProcessorKwargs

    def __init__(self, image_processor, tokenizer):
        super().__init__(image_processor, tokenizer)


__all__ = ["AlignProcessor"]

	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""
	Image/Text processor class for ALIGN
	"""

	from ...processing_utils import ProcessingKwargs, ProcessorMixin


	class AlignProcessorKwargs(ProcessingKwargs, total=False):
	# see processing_utils.ProcessingKwargs documentation for usage.
	_defaults = {
	"text_kwargs": {
	"padding": "max_length",
	"max_length": 64,
	},
	}


	class AlignProcessor(ProcessorMixin):
	r"""
	Constructs an ALIGN processor which wraps [`EfficientNetImageProcessor`] and
	[`BertTokenizer`]/[`BertTokenizerFast`] into a single processor that inherits both the image processor and
	tokenizer functionalities. See the [`~AlignProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more
	information.
	The preferred way of passing kwargs is as a dictionary per modality, see usage example below.
	```python
	from transformers import AlignProcessor
	from PIL import Image
	model_id = "kakaobrain/align-base"
	processor = AlignProcessor.from_pretrained(model_id)

	processor(
	images=your_pil_image,
	text=["What is that?"],
	images_kwargs = {"crop_size": {"height": 224, "width": 224}},
	text_kwargs = {"padding": "do_not_pad"},
	common_kwargs = {"return_tensors": "pt"},
	)
	```

	Args:
	image_processor ([`EfficientNetImageProcessor`]):
	The image processor is a required input.
	tokenizer ([`BertTokenizer`, `BertTokenizerFast`]):
	The tokenizer is a required input.

	"""

	attributes = ["image_processor", "tokenizer"]
	image_processor_class = "EfficientNetImageProcessor"
	tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
	valid_processor_kwargs = AlignProcessorKwargs

	def __init__(self, image_processor, tokenizer):
	super().__init__(image_processor, tokenizer)


	__all__ = ["AlignProcessor"]