






import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__magic_name__:  Optional[int]  	=			"src/diffusers"
__magic_name__:  Optional[Any]  	=			"docs/source/en"
__magic_name__:  int  	=			"."

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						with open(_A,					"""r""",					encoding="""utf-8""",					newline="""\n"""				) as f:
												__magic_name__    :     Dict     		=      f.readlines()
						# Find the start prompt.
						__magic_name__    :     Optional[Any]     		=      0
						while not lines[start_index].startswith(_A				):
												start_index += 1
						start_index += 1

						__magic_name__    :     List[Any]     		=      start_index
						while not lines[end_index].startswith(_A				):
												end_index += 1
						end_index -= 1

						while len(lines[start_index]				) <= 1:
												start_index += 1
						while len(lines[end_index]				) <= 1:
												end_index -= 1
						end_index += 1
						return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__magic_name__:  List[Any]  	=			"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
__magic_name__:  Dict  	=			re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__magic_name__:  Union[str, Any]  	=			re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__magic_name__:  Union[str, Any]  	=			re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the diffusers module imported is the one in the repo.
__magic_name__:  List[str]  	=			importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__magic_name__:  Optional[int]  	=			spec.loader.load_module()

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     Tuple     		=      re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",					_A				)
						return [m.group(0				) for m in matches]

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     int     		=      2 if text == """✅""" or text == """❌""" else len(_A				)
						__magic_name__    :     Any     		=      (width - text_length) // 2
						__magic_name__    :     int     		=      width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						__magic_name__    :     Tuple     		=      {
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						__magic_name__    :     Dict     		=      {name: config.replace("""ConfigMixin""",					""""""				) for name, config in model_name_to_config.items()}

						# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
						__magic_name__    :     int     		=      collections.defaultdict(_A				)
						__magic_name__    :     Any     		=      collections.defaultdict(_A				)
						__magic_name__    :     Union[str, Any]     		=      collections.defaultdict(_A				)
						__magic_name__    :     Any     		=      collections.defaultdict(_A				)
						__magic_name__    :     List[str]     		=      collections.defaultdict(_A				)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(_A				):
												__magic_name__    :     Any     		=      None
												if attr_name.endswith("""Tokenizer"""				):
																		__magic_name__    :     List[str]     		=      slow_tokenizers
																		__magic_name__    :     List[Any]     		=      attr_name[:-9]
												elif attr_name.endswith("""TokenizerFast"""				):
																		__magic_name__    :     List[str]     		=      fast_tokenizers
																		__magic_name__    :     List[Any]     		=      attr_name[:-13]
												elif _re_tf_models.match(_A				) is not None:
																		__magic_name__    :     List[Any]     		=      tf_models
																		__magic_name__    :     Optional[int]     		=      _re_tf_models.match(_A				).groups()[0]
												elif _re_flax_models.match(_A				) is not None:
																		__magic_name__    :     Tuple     		=      flax_models
																		__magic_name__    :     Union[str, Any]     		=      _re_flax_models.match(_A				).groups()[0]
												elif _re_pt_models.match(_A				) is not None:
																		__magic_name__    :     List[Any]     		=      pt_models
																		__magic_name__    :     Optional[Any]     		=      _re_pt_models.match(_A				).groups()[0]

												if lookup_dict is not None:
																		while len(_A				) > 0:
																								if attr_name in model_name_to_prefix.values():
																														__magic_name__    :     str     		=      True
																														break
																								# Try again after removing the last word in the name
																								__magic_name__    :     List[Any]     		=      """""".join(camel_case_split(_A				)[:-1]				)

    # Let's build that table!
						__magic_name__    :     Union[str, Any]     		=      list(model_name_to_config.keys()				)
						model_names.sort(key=str.lower				)
						__magic_name__    :     Any     		=      ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
						# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
						__magic_name__    :     str     		=      [len(_A				) + 2 for c in columns]
						__magic_name__    :     List[Any]     		=      max([len(_A				) for name in model_names]				) + 2

						# Build the table per se
						__magic_name__    :     Tuple     		=      """|""" + """|""".join([_center_text(_A,					_A				) for c, w in zip(_A,					_A				)]				) + """|\n"""
						# Use ":-----:" format to center-aligned table cell texts
						table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]				) + "|\n"

						__magic_name__    :     Tuple     		=      {True: """✅""", False: """❌"""}
						for name in model_names:
												__magic_name__    :     Tuple     		=      model_name_to_prefix[name]
												__magic_name__    :     Tuple     		=      [
												    name,
												    check[slow_tokenizers[prefix]],
												    check[fast_tokenizers[prefix]],
												    check[pt_models[prefix]],
												    check[tf_models[prefix]],
												    check[flax_models[prefix]],
												]
												table += "|" + "|".join([_center_text(_A,					_A				) for l, w in zip(_A,					_A				)]				) + "|\n"
						return table

def 					UpperCamelCase		( _A=False				):






						"""simple docstring"""







						__magic_name__		,__magic_name__		,__magic_name__		,__magic_name__    :     Dict     		=      _find_text_in_file(
						    filename=os.path.join(_A,					"""index.mdx"""				),					start_prompt="""<!--This table is updated automatically from the auto modules""",					end_prompt="""<!-- End table-->""",					)
						__magic_name__    :     List[Any]     		=      get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(_A,					"""index.mdx"""				),					"""w""",					encoding="""utf-8""",					newline="""\n"""				) as f:
																								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
												else:
																		raise ValueError(
																		    """The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."""				)


if __name__ == "__main__":
		__magic_name__:  Optional[Any]  	=			argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		__magic_name__:  Optional[int]  	=			parser.parse_args()

		check_model_table(args.fix_and_overwrite)








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__magic_name__:  int  	=			"\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__magic_name__:  List[Any]  	=			"\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
													raise ImportWarning(
													    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
													    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,     citation=_CITATION    ,     homepage="""https://github.com/mjpost/sacreBLEU"""    ,     inputs_description=_KWARGS_DESCRIPTION    ,     features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""    ,     id="""sequence"""				),
							            """references""": datasets.Sequence(datasets.Value("""string"""    ,     id="""sequence"""				)    ,     id="""references"""				),
							        }				)    ,     codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]    ,     reference_urls=[
							        """https://github.com/mjpost/sacreBLEU""",
							        """https://en.wikipedia.org/wiki/BLEU""",
							        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
							    ]    ,     )







	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      len(references[0]				)
							if any(len(lowerCAmelCase__				) != references_per_prediction for refs in references				):
													raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     List[str]     		=      {
							    """score""": output.score,
							    """counts""": output.counts,
							    """totals""": output.totals,
							    """precisions""": output.precisions,
							    """bp""": output.bp,
							    """sys_len""": output.sys_len,
							    """ref_len""": output.ref_len,
							}
							return output_dict
