"""Generate the human-facing documentation bundle from live pipeline results."""
from __future__ import annotations

from datetime import datetime, timezone

from common import human_bytes

WORKS_COLUMNS = [
    ("work_id", "string", "OpenAlex work ID (short form, e.g. W2146512944). Primary key."),
    ("doi", "string", "Normalized DOI (lowercase, no https://doi.org/ prefix). Null if absent."),
    ("title", "string", "Work title. Rows with empty titles are removed during cleaning."),
    ("publication_date", "string", "ISO date (YYYY-MM-DD). Null when OpenAlex has no exact date."),
    ("publication_year", "int16", "Publication year (2015-2025)."),
    ("publication_month", "int8", "Month extracted from publication_date."),
    ("type", "string", "OpenAlex work type (article, review, book-chapter, ...)."),
    ("primary_topic_id", "string", "OpenAlex topic ID of the work's primary topic."),
    ("primary_topic_name", "string", "Display name of the primary topic."),
    ("primary_topic_score", "float32", "OpenAlex confidence score for the primary topic."),
    ("field_id", "string", "OpenAlex field ID (top-level discipline) of the primary topic."),
    ("field_name", "string", "Field name, e.g. Medicine, Neuroscience."),
    ("subfield_id", "string", "OpenAlex subfield ID of the primary topic."),
    ("subfield_name", "string", "Subfield name, e.g. Oncology, Cardiology."),
    ("domain_id", "string", "OpenAlex domain ID (Life Sciences=1, Health Sciences=4)."),
    ("domain_name", "string", "Domain name."),
    ("topic_ids", "list<string>", "All OpenAlex topic IDs assigned to the work (max 3)."),
    ("topic_names", "list<string>", "Names aligned positionally with topic_ids."),
    ("topic_count", "int16", "Number of topics on the work."),
    ("author_count", "int32", "Number of authorships."),
    ("author_names", "list<string>", "Author display names in authorship order."),
    ("author_ids", "list<string>", "OpenAlex author IDs aligned with author_names."),
    ("author_orcids", "list<string>", "ORCIDs aligned with author_names (null where absent)."),
    ("institution_count", "int32", "Number of distinct institutions on the work."),
    ("institution_ids", "list<string>", "Distinct OpenAlex institution IDs."),
    ("institution_names", "list<string>", "Institution display names."),
    ("institution_countries", "list<string>", "Distinct ISO-3166 alpha-2 codes of institutions."),
    ("country_codes", "list<string>", "All countries involved (authors + institutions)."),
    ("country_count", "int16", "Number of distinct countries."),
    ("source_id", "string", "OpenAlex source (journal/venue) ID."),
    ("source_name", "string", "Source display name."),
    ("source_type", "string", "Source type (journal, conference, repository, ...)."),
    ("issn_l", "string", "Linking ISSN of the source."),
    ("issns", "list<string>", "All ISSNs of the source."),
    ("publisher", "string", "Host organisation / publisher name."),
    ("cited_by_count", "int32", "OpenAlex citation count at retrieval time."),
    ("recent_citations_3y", "int32", "Citations accrued in the last 3 years (2023-2025)."),
    ("referenced_works_count", "int32", "Number of references (per OpenAlex)."),
    ("referenced_work_ids", "list<string>", "OpenAlex IDs of referenced works (short form)."),
    ("is_oa", "bool", "Open-access flag."),
    ("oa_status", "string", "OA status: gold, green, hybrid, bronze, diamond, closed."),
    ("oa_url", "string", "Best OA location URL."),
    ("license", "string", "License string of the primary/best OA location (e.g. cc-by)."),
    ("license_id", "string", "License identifier where provided."),
    ("language", "string", "Language code of the work."),
    ("abstract", "string", "Abstract reconstructed from OpenAlex's inverted index (null if none)."),
    ("has_abstract", "bool", "Whether an abstract could be reconstructed."),
    ("is_retracted", "bool", "Retraction flag."),
    ("is_paratext", "bool", "Paratext flag (front matter, editorials) - excluded by default."),
    ("fwci", "float32", "Field-Weighted Citation Impact (OpenAlex supplied)."),
    ("openalex_citation_percentile", "float32", "OpenAlex-supplied normalized citation percentile."),
    ("created_date", "string", "Date the record was created in OpenAlex."),
    ("updated_date", "string", "Date the record was last updated in OpenAlex."),
    ("mesh_terms", "list<string>", "MeSH descriptor/qualifier terms where OpenAlex supplies them."),
    ("keywords", "list<string>", "OpenAlex keyword tags."),
    ("paper_age_years", "float32", "DERIVED: reference_year - publication_year."),
    ("citations_per_year", "float32", "DERIVED: cited_by_count / max(paper_age_years, 1)."),
    ("citation_velocity", "float32", "DERIVED: recent_citations_3y / 3."),
    ("international_collaboration", "bool", "DERIVED: country_count >= 2."),
    ("multi_institutional", "bool", "DERIVED: institution_count >= 2."),
    ("publication_recency_score", "float32", "DERIVED: exp(-paper_age_years / halflife)."),
    ("citation_percentile", "float32", "DERIVED: percentile of cited_by_count within publication_year x field."),
    ("high_impact_flag", "bool", "DERIVED: citation_percentile >= configured threshold (default 90)."),
    ("research_growth_score", "float32", "DERIVED: late/early publication ratio of the work's field."),
    ("topic_growth_score", "float32", "DERIVED: late/early publication ratio of the primary topic."),
    ("emerging_topic_flag", "bool", "DERIVED: primary topic is emerging (growth ratio >= threshold, sufficient volume)."),
]

RELATION_COLUMNS = {
    "work_authors.parquet": [
        ("work_id", "FK to works.work_id"), ("author_id", "OpenAlex author ID"),
        ("author_name", "author display name"), ("orcid", "ORCID if available"),
        ("author_position", "first / middle / last"), ("is_corresponding", "corresponding-author flag"),
        ("institution_ids", "institutions for this authorship"),
        ("country_codes", "countries for this authorship"),
    ],
    "work_institutions.parquet": [
        ("work_id", "FK to works"), ("institution_id", "OpenAlex institution ID"),
        ("institution_name", "name"), ("country_code", "ISO alpha-2"),
        ("institution_type", "education / company / government / facility / other"),
    ],
    "work_topics.parquet": [
        ("work_id", "FK to works"), ("topic_id", "OpenAlex topic ID"), ("topic_name", "topic name"),
        ("score", "relevance score"), ("is_primary", "primary-topic flag"),
        ("subfield_id", "subfield"), ("subfield_name", "subfield name"),
        ("field_id", "field"), ("field_name", "field name"),
        ("domain_id", "domain"), ("domain_name", "domain name"),
    ],
    "citation_edges.parquet": [
        ("citing_work_id", "work that cites"), ("cited_work_id", "work that is cited"),
        ("cited_in_dataset", "always true: edges are restricted to pairs inside this dataset"),
    ],
    "authors.parquet": [
        ("author_id", "PK"), ("author_name", "name"), ("orcid", "ORCID"),
        ("works_count", "works in dataset"), ("first_year", "earliest year"), ("last_year", "latest year"),
        ("total_citations", "sum of citations"), ("mean_citations", "mean citations per work"),
        ("institution_ids", "affiliations seen"), ("country_codes", "countries seen"),
        ("primary_field_id", "most frequent field"), ("primary_field_name", "field name"),
    ],
    "institutions.parquet": [
        ("institution_id", "PK"), ("institution_name", "name"), ("country_code", "ISO alpha-2"),
        ("works_count", "works in dataset"), ("first_year", "earliest year"), ("last_year", "latest year"),
        ("total_citations", "sum of citations"), ("mean_citations", "mean citations"),
        ("primary_field_id", "most frequent field"), ("primary_field_name", "field name"),
    ],
    "topics.parquet": [
        ("topic_id", "PK"), ("topic_name", "name"), ("subfield_id", "subfield"), ("subfield_name", "subfield name"),
        ("field_id", "field"), ("field_name", "field name"), ("domain_id", "domain"), ("domain_name", "domain name"),
        ("works_count", "works in dataset"), ("first_year", "earliest year"), ("last_year", "latest year"),
        ("total_citations", "sum of citations"), ("mean_citations", "mean citations"),
    ],
    "sources.parquet": [
        ("source_id", "PK"), ("source_name", "venue name"), ("source_type", "journal/conference/..."),
        ("issn_l", "linking ISSN"), ("issns", "all ISSNs"), ("publisher", "publisher"),
        ("is_oa", "venue OA flag"), ("works_count", "works in dataset"),
        ("total_citations", "sum of citations"), ("mean_citations", "mean citations"),
    ],
}

ANALYTICS_COLUMNS = {
    "topic_year_counts.parquet": [("topic_id", ""), ("topic_name", ""), ("publication_year", ""), ("works_count", "")],
    "topic_growth.parquet": [("topic_id", ""), ("topic_name", ""), ("first_year", ""), ("last_year", ""),
                              ("early_count", "sum in first half of range"), ("late_count", "sum in second half"),
                              ("growth_ratio", "(late+1)/(early+1)"), ("cagr", "compound annual growth of averages"),
                              ("trend_slope", "OLS slope of yearly counts"), ("emerging_topic_flag", "growth and volume threshold")],
    "country_year_counts.parquet": [("country_code", ""), ("publication_year", ""), ("works_count", "")],
    "country_collaboration.parquet": [("country_a", ""), ("country_b", ""), ("collaboration_count", "co-authored works")],
    "institution_activity.parquet": [("institution_id", ""), ("institution_name", ""), ("country_code", ""),
                                     ("publication_year", ""), ("works_count", ""), ("total_citations", "")],
}


def _now():
    return datetime.now(timezone.utc).strftime("%Y-%m-%d")


def readme(cfg, stats, manifest) -> str:
    t = stats.get("totals", {})
    yrs = stats.get("year_range") or [None, None]
    size = manifest.get("package_size_bytes", 0)
    cit = stats.get("citation_statistics", {})
    return f"""# {cfg['project']['title']}

A curated, normalized, enriched and packaged **biomedical research intelligence
dataset** derived from OpenAlex metadata, provided in AI-ready formats
(Parquet + JSONL) with full provenance and reproducible build tooling.

This is a **derivative dataset**: it is not a re-upload of OpenAlex. The value
added here is biomedical filtering by taxonomy, cleaning, normalization,
relational restructuring, quality control and derived analytical features.

## What it is

- **Source:** OpenAlex work metadata, retrieved through the OpenAlex REST API.
- **Scope:** biomedical and health-related research only, selected by OpenAlex
  **taxonomy IDs** (fields 13, 24, 27, 28, 29, 30, 35, 36), not keyword search.
- **Coverage:** publication years {yrs[0]}-{yrs[1]}.
- **Selection:** stratified random sample drawn per (year x field) shard with a
  fixed random seed, so every year and every biomedical field is represented
  proportionally and the result is reproducible.
- **Records:** {t.get('total_records', 0):,} works, {t.get('unique_works', 0):,} unique
  OpenAlex IDs, {t.get('unique_dois', 0):,} unique DOIs.
- **Languages / OA:** see `metadata/dataset_statistics.json`.
- **Package size (uncompressed):** {human_bytes(size)}.

## Intended uses

Machine-learning and analytical research on the biomedical literature:
trend and growth analysis, collaboration-network analysis, bibliometric
modeling, topic classification, retrieval/ranking experiments, and as a
ready-to-load table for LLM and data-science tooling.

## Formats and layout

```
data/       works + dimensions + relations (Parquet), JSONL exports, splits
analytics/  pre-aggregated time-series and collaboration tables (Parquet)
metadata/   manifest, statistics, schema, source queries, build info
```

Parquet is the primary format. JSONL exports (gzip) are provided for
streaming/LLM use. See `DATA_DICTIONARY.md` for every column.

## Key derived features

`paper_age_years`, `citations_per_year`, `citation_velocity`,
`citation_percentile` (within year x field), `high_impact_flag`,
`publication_recency_score`, `international_collaboration`,
`multi_institutional`, `research_growth_score`, `topic_growth_score`,
`emerging_topic_flag`. Definitions are in `METHODOLOGY.md`.

## Train / validation / test splits

Chronological to avoid leakage:
train <= {cfg['splits']['train_max_year']},
validation {cfg['splits']['validation_years']},
test >= {cfg['splits']['test_min_year']}.
The complete unsplit dataset is retained separately.

## Limitations

See `QUALITY_REPORT.md`. In short: metadata-only (no full text), citation
counts are OpenAlex's at retrieval time, institutional/author disambiguation
is inherited from OpenAlex, and derived growth signals are estimates that scale
with sample size.

## Rights

Base metadata originates from OpenAlex (CC0). Derived fields and packaging are
original. No copyrighted full text, figures or supplementary files are included.
See `LICENSE_AND_RIGHTS.md`.

## Reproduce

```bash
pip install -r requirements.txt
python run-all.py
```

Retrieval dates and exact filters are recorded in `PROVENANCE.md` and
`metadata/source_queries.json`.

_Generated {_now()} by pipeline v{cfg['project']['version']}._
"""


def data_dictionary(cfg, stats) -> str:
    out = [f"# Data Dictionary - {cfg['project']['title']}", "",
           "All columns of every delivered table. Types are Arrow/Parquet types.", ""]
    out += ["## data/works.parquet", "", "| Column | Type | Description |", "|---|---|---|"]
    for name, typ, desc in WORKS_COLUMNS:
        out.append(f"| `{name}` | {typ} | {desc} |")
    out.append("")
    for fname, cols in RELATION_COLUMNS.items():
        out += [f"## data/{fname}", "", "| Column | Description |", "|---|---|"]
        for name, desc in cols:
            out.append(f"| `{name}` | {desc} |")
        out.append("")
    for fname, cols in ANALYTICS_COLUMNS.items():
        out += [f"## analytics/{fname}", "", "| Column | Description |", "|---|---|"]
        for name, desc in cols:
            out.append(f"| `{name}` | {desc} |")
        out.append("")
    out += ["## Splits", "",
            "`data/biomedical_works_train.jsonl.gz`, `..._validation.jsonl.gz`, "
            "`..._test.jsonl.gz` contain the same columns as `data/works.parquet`, "
            "split chronologically by publication year.", ""]
    return "\n".join(out)


def methodology(cfg) -> str:
    f = cfg["features"]
    return f"""# Methodology

## 1. Collection

Metadata is retrieved from the OpenAlex REST API (`/works`) using OAuth-free
polite-pool access with a `mailto` identifier. Retrieval is resumable: each
(year, field) shard keeps its own checkpoint, requests are retried with
exponential backoff and jitter, and 429/5xx responses honour `Retry-After`.

## 2. Taxonomy filtering (no keyword search)

The biomedical scope is defined by OpenAlex **field IDs**:

| Field ID | Field |
|---|---|
| 13 | Biochemistry, Genetics and Molecular Biology |
| 24 | Immunology and Microbiology |
| 27 | Medicine |
| 28 | Neuroscience |
| 29 | Nursing |
| 30 | Pharmacology, Toxicology and Pharmaceutics |
| 35 | Dentistry |
| 36 | Health Professions |

These cover the requested areas (oncology, cardiology, public health,
epidemiology, infectious disease, genetics, surgery, psychiatry, pediatrics,
obstetrics & gynecology, radiology, anesthesiology, health policy, global
health, and more) through their subfields and topics. The taxonomy snapshot is
frozen under `metadata/taxonomy/` so normalization is deterministic.

## 3. Selection / sampling

The scope is partitioned into (publication_year x field) shards. A global target
row count is distributed across shards **in proportion to each shard's true
size**, and works are drawn with OpenAlex's **seeded random `sample`** operator.

Seeded sampling is essential: the default listing order returns the most-cited
works first (median ~1,300 citations), whereas the seeded sample reproduces the
true citation distribution (median 0-1). Each shard uses a deterministic seed,
so re-running reproduces the identical set. No record is duplicated to inflate
size.

## 4. Cleaning and normalization

- **Deduplication** by OpenAlex work ID and by normalized DOI.
- **DOI normalization**: lowercase, scheme/prefix stripped.
- **Date normalization** to ISO `YYYY-MM-DD`; invalid dates dropped.
- **Abstract reconstruction** from OpenAlex's inverted index into text.
- **Author normalization**: names, OpenAlex IDs, ORCIDs (bare form), positions,
  corresponding flags; affiliations flattened.
- **Institution normalization**: IDs, names, ISO-3166 alpha-2 country codes.
- **Country normalization**: union of authorship and institution countries.
- **Topic normalization** to a frozen topic index (topic/subfield/field/domain).
- **Malformed removal**: rows without an ID or with empty titles are dropped.
- **Missing values** represented consistently as null.

## 5. Derived metrics (deterministic, reproducible)

Let `ref_year = max(publication_year)`, halflife = {f['recency_halflife_years']}y.

- `paper_age_years = ref_year - publication_year`
- `citations_per_year = cited_by_count / max(paper_age_years, 1)`
- `citation_velocity = recent_citations_3y / 3`
- `publication_recency_score = exp(-paper_age_years / halflife)`
- `citation_percentile` = percentile rank of `cited_by_count` **within
  publication_year x field** (groups: {f['citation_percentile_group']}).
- `high_impact_flag = citation_percentile >= {f['high_impact_percentile']}`
- `international_collaboration = country_count >= 2`
- `multi_institutional = institution_count >= 2`
- Growth: each topic/field's yearly counts are split into an early and a late
  half of the year range; `growth_ratio = (late+1)/(early+1)`, `cagr` from the
  half-averages, `trend_slope` = OLS slope of yearly counts.
- `emerging_topic_flag = growth_ratio >= {f['emerging_growth_threshold']} and
  late_count >= 50 and late > early`.

No derived field is generated by a language model; all values are computed from
OpenAlex metadata by the versioned code in `src/`.

## 6. Train / validation / test splitting

Chronological, to prevent leakage: a work appears in exactly one split
(works are unique and DOIs are deduplicated, so the same paper or DOI cannot
cross splits). Train <= {cfg['splits']['train_max_year']}, validation
{cfg['splits']['validation_years']}, test >= {cfg['splits']['test_min_year']}.
The complete unsplit dataset is retained as `data/works.parquet`.

## 7. Validation and packaging

Every Parquet table is validated against its canonical Arrow schema (column
names, types, required-column nulls, primary-key uniqueness). SHA-256 checksums
are written for all delivered files in `checksums.sha256`.
"""


def provenance(cfg, source_queries, stats, manifest) -> str:
    queries = source_queries or []
    rows = []
    for q in queries:
        rows.append(f"- retrieval started **{q.get('retrieval_started')}**, finished "
                    f"**{q.get('retrieval_finished')}**, {q.get('rows_collected'):,} rows, "
                    f"filters field IDs {q.get('field_ids')}, years {q.get('year_range')}, "
                    f"endpoint `{q.get('endpoint')}`")
    qtext = "\n".join(rows) if rows else "- see metadata/source_queries.json"
    return f"""# Provenance

## Source

The base metadata in this package originates from **OpenAlex**
(https://openalex.org), an open scholarly-metadata catalogue. OpenAlex data is
dedicated to the public domain under **CC0 1.0** (see `LICENSE_AND_RIGHTS.md`).

- API base: `{cfg['source']['base_url']}`
- Endpoint: `/works` (plus `/domains`, `/fields`, `/subfields`, `/topics` for taxonomy)
- Access: polite pool via `mailto`
- Selection operator: seeded random `sample` per (year x field) shard

## Retrieval records

{qtext}

## Transformations

1. Raw JSON pages are streamed to gzip-JSONL shards per (year, field).
2. `normalize.py` flattens nested objects, reconstructs abstracts, normalizes
   identifiers and writes Parquet parts.
3. `derive_features.py` attaches derived metrics.
4. `build_relations.py` builds dimension tables and the internal citation graph.
5. `analyze.py` builds the analytics aggregates and statistics.
6. `validate.py` checks schemas; `package.py` writes the manifest, checksums
   and archive.

Every stage is checkpointed and deterministic given the same config and seeds.

## Build

- Pipeline version: {cfg['project']['version']}
- Build timestamp: {manifest.get('build_timestamp')}
- Config file: `config.yaml` (frozen values in `metadata/build_info.json`)

## Reproducibility

Because sampling uses fixed seeds derived from shard keys, re-running the
collection reproduces the same works (subject to OpenAlex's own record
additions/updates between runs). Citation counts reflect OpenAlex at the
retrieval date above.
"""


def license_and_rights(cfg) -> str:
    return f"""# License and Rights

This document separates three layers of rights so that nothing is misrepresented.

## 1. Original OpenAlex metadata (source layer)

The base bibliographic metadata is sourced from **OpenAlex**, which is made
available under **CC0 1.0 Universal (Public Domain Dedication)**.
OpenAlex aggregates metadata from Crossref, PubMed, MAG, institutional
repositories and others; it publishes the aggregated metadata openly.

- No exclusive ownership of OpenAlex source data is claimed.
- The underlying public metadata is **not** proprietary.
- Attribution to OpenAlex is given as a courtesy and is recorded in
  `PROVENANCE.md`.

## 2. Derived content (this package's contribution)

The following are original to this project: the biomedical taxonomy-based
selection and stratification, cleaning and normalization, relational
restructuring, derived analytical features, quality control, documentation and
packaging.

This package is offered as a **curated, normalized, enriched and packaged
derivative dataset**. It does not claim exclusive rights over the source data.

## 3. Content intentionally excluded

The following are **not** included, because their redistribution rights are
unclear or restricted:

- Full-text articles, PDFs, HTML and publisher landing pages.
- Figures, tables, images and supplementary files.
- Any content flagged non-commercial or otherwise restricted.
- Any dataset with unclear commercial reuse rights.

Only OpenAlex metadata fields and abstracts that OpenAlex itself distributes
through its API are included. Abstracts are reconstructed from OpenAlex's
inverted-index metadata representation, which OpenAlex provides as part of its
open API; no publisher scraping or access-control circumvention was performed.

## Practical guidance for downstream users

- Cite OpenAlex as the metadata source.
- Verify the licence of any individual work before redistributing its abstract,
  where a specific licence is required.
- `works.license` / `works.license_id` record the OpenAlex-reported licence of
  the article's OA location where available.

_This document is not legal advice._
"""


def quality_report(cfg, validation, stats, norm_stats, derive_stats, files) -> str:
    t = stats.get("totals", {})
    missing = stats.get("missing_value_percent", {})
    worst = sorted(missing.items(), key=lambda kv: -kv[1])[:15]
    missing_rows = "\n".join(f"| `{k}` | {v}% |" for k, v in worst)
    tables = []
    for rel, info in (validation.get("tables") or {}).items():
        tables.append(f"| `{rel}` | {info.get('rows', 'n/a')} | {info.get('status')} |")
    tables_txt = "\n".join(tables)
    return f"""# Quality Report

_Generated {_now()} by pipeline v{cfg['project']['version']}._

## Schema validation

Status: **{validation.get('status', 'unknown')}**
({len(validation.get('errors', []))} errors, {len(validation.get('warnings', []))} warnings)

| Table | Rows | Status |
|---|---|---|
{tables_txt}

## Malformed records

| Category | Count |
|---|---|
| Raw rows read | {norm_stats.get('raw_rows', 'n/a')} |
| Malformed / dropped (no ID or empty title) | {norm_stats.get('malformed', 'n/a')} |
| Duplicate work IDs removed | {norm_stats.get('dup_id', 'n/a')} |
| Duplicate DOIs removed | {norm_stats.get('dup_doi', 'n/a')} |
| Records kept | {norm_stats.get('kept', 'n/a')} |

## Missingness (top columns, by percent null)

| Column | % null |
|---|---|
{missing_rows}

## Citation statistics

`{stats.get('citation_statistics', {})}`

## International collaboration rate

{stats.get('international_collaboration_rate')}

## Suspicious / notable values

- Citation counts are OpenAlex's at retrieval time and can be 0 for recent works.
- `citation_velocity` is null where OpenAlex supplies no `counts_by_year`.
- `citation_percentile` is computed within (year x field); small shards in the
  least-populated (year, field) cells have coarser percentiles.
- `emerging_topic_flag` requires >= 50 late-period works; small samples flag few
  topics.
- OpenAlex primary-topic assignment is model-predicted and inherits its error.

## Coverage

- Year range: {stats.get('year_range')}
- Fields covered: {len(stats.get('works_per_field', {}))}
- Subfields covered: {len(stats.get('works_per_subfield', {}))}
- Countries covered: {len(stats.get('works_per_country', {}))}
- OpenAlex-supplied citation percentile and FWCI retained for cross-checks.

## Known limitations

1. Metadata-only: abstracts where OpenAlex provides them; no full text.
2. Sampling: this is a proportional stratified sample, not the full corpus;
   rare cells are represented with fewer rows.
3. Author/institution identity is inherited from OpenAlex disambiguation.
4. Citation-based features are time-sensitive (snapshot at retrieval date).
5. Derived growth scores are estimates and scale with the sample size.

## Files validated

{len(files)} delivered files hashed in `checksums.sha256`.
"""