'''simple docstring'''


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowerCAmelCase	:       int						       =	pytest.mark.integration


__lowerCAmelCase	:       Dict						       =	{"comet"}
__lowerCAmelCase	:       str						       =	importlib.util.find_spec("fairseq") is not None

__lowerCAmelCase	:       Dict						       =	{"code_eval"}
__lowerCAmelCase	:       Any						       =	os.name == "nt"

__lowerCAmelCase	:       Optional[Any]						       =	{"bertscore", "frugalscore", "perplexity"}
__lowerCAmelCase	:       str						       =	importlib.util.find_spec("transformers") is not None





def       lowerCAmelCase					(		UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	@wraps(UpperCamelCase__       )
	def wrapper(self				:   List[Any]    ,					UpperCamelCase__				:   Dict       ):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest('''"test requires Fairseq"'''       )
		else:
			test_case(self    ,					UpperCamelCase__       )

	return wrapper





def       lowerCAmelCase					(		UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	@wraps(UpperCamelCase__       )
	def wrapper(self				:   List[Any]    ,					UpperCamelCase__				:   Any       ):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest('''"test requires transformers"'''       )
		else:
			test_case(self    ,					UpperCamelCase__       )

	return wrapper





def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	@wraps(UpperCamelCase__       )
	def wrapper(self				:   Union[str, Any]    ,					UpperCamelCase__				:   Tuple       ):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest('''"test not supported on Windows"'''       )
		else:
			test_case(self    ,					UpperCamelCase__       )

	return wrapper





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			[metric_dir.split(os.sep       )[-2] for metric_dir in glob.glob('''./metrics/*/'''       )]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished






@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase   )
@local
class 						A   (   parameterized.TestCase   ):
							a_        =      {}
							a_        =      None





							@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''	)
							@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''	)
							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Union[str, Any]	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''[...]'''
								__UpperCAmelCase							=			importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''     ,		__a	)	).module_path	)
								__UpperCAmelCase							=			datasets.load.import_main_class(metric_module.__name__     ,		dataset=__a	)
								# check parameters
								__UpperCAmelCase							=			inspect.signature(metric._compute	).parameters
								self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()	)	)  # no **kwargs
								# run doctest
								with self.patch_intensive_calls(__a     ,		metric_module.__name__	):
									with self.use_local_metrics():
										try:
											__UpperCAmelCase							=			doctest.testmod(__a     ,		verbose=__a     ,		raise_on_error=__a	)
										except doctest.UnexpectedException as e:
											raise e.exc_info[1]  # raise the exception that doctest caught
								self.assertEqual(results.failed     ,		0	)
								self.assertGreater(results.attempted     ,		1	)





							@slow
							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			'''[...]'''
								__UpperCAmelCase							=			importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''     ,		__a	)	).module_path	)
								# run doctest
								with self.use_local_metrics():
									__UpperCAmelCase							=			doctest.testmod(__a     ,		verbose=__a     ,		raise_on_error=__a	)
								self.assertEqual(results.failed     ,		0	)
								self.assertGreater(results.attempted     ,		1	)





							@contextmanager
							def       snake_case__						(      self				:							Any     ,		__a				:							List[Any]     ,		__a				:							Tuple	)							->	Dict:
								if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__a	):
										yield
								else:
									yield





							@contextmanager
							def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
								def load_local_metric(__a				:							str     ,		*__a				:							str     ,		**__a				:							Any	):
									return load_metric(os.path.join('''metrics'''     ,		__a	)     ,		*__a     ,		**__a	)

								with patch('''datasets.load_metric'''	) as mock_load_metric:
									__UpperCAmelCase							=			load_local_metric
									yield






							@classmethod
							def       snake_case__						(      cls				:							str     ,		__a				:							Optional[Any]	)							->	Optional[Any]:
								def wrapper(__a				:							str	):
									__UpperCAmelCase							=			contextmanager(__a	)
									__UpperCAmelCase							=			patcher
									return patcher

								return wrapper





@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''       )
def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string('''sv'''    ,					''''''    ,					''''''       )  # handle pytest cli flags






	class 						A   (   UpperCAmelCase   ):





								def       snake_case__						(      self				:							str     ,		__a				:							List[Any]	)							->	List[str]:
									assert len(input_dict['''input_ids''']	) == 2
									return np.array([1.0_3, 1.0_4]	)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch('''bleurt.score._create_predictor'''       ) as mock_create_predictor:
		__UpperCAmelCase							=			MockedPredictor()
		yield





@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	import torch

	def bert_cos_score_idf(UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Union[str, Any]    ,					*UpperCamelCase__				:   Any    ,					**UpperCamelCase__				:   str       ):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase__       )       )

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch('''bert_score.scorer.get_model'''       ), patch(
	    '''bert_score.scorer.bert_cos_score_idf'''       ) as mock_bert_cos_score_idf:
		__UpperCAmelCase							=			bert_cos_score_idf
		yield





@LocalMetricTest.register_intensive_calls_patcher('''comet'''       )
def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	def load_from_checkpoint(UpperCamelCase__				:   Tuple       ):






		class 						A   :





									def       snake_case__						(      self				:							List[Any]     ,		__a				:							Optional[Any]     ,		*__a				:							Any     ,		**__a				:							List[Any]	)							->	Optional[Any]:
										assert len(__a	) == 2
										__UpperCAmelCase							=			[0.1_9, 0.9_2]
										return scores, sum(__a	) / len(__a	)

		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch('''comet.download_model'''       ) as mock_download_model:
		__UpperCAmelCase							=			None
		with patch('''comet.load_from_checkpoint'''       ) as mock_load_from_checkpoint:
			__UpperCAmelCase							=			load_from_checkpoint
			yield





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			load_metric(os.path.join('''metrics'''    ,					'''seqeval'''       )       )
	__UpperCAmelCase							=			'''ERROR'''
	__UpperCAmelCase							=			f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
	with pytest.raises(UpperCamelCase__    ,					match=re.escape(UpperCamelCase__       )       ):
		metric.compute(predictions=[]    ,					references=[]    ,					scheme=UpperCamelCase__       )

'''simple docstring'''


import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCAmelCase	:       List[Any]						       =	"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase	:       str						       =	direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase	:       int						       =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase	:       List[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.
__lowerCAmelCase	:       List[str]						       =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase	:       Optional[int]						       =	[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]





def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,					UpperCamelCase__       )
	return [m.group(0       ) for m in matches]





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__UpperCAmelCase							=			{
	    config.replace('''Config'''    ,					''''''       ): model_type for model_type, config in config_maping_names.items()
	}

	# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )
	__UpperCAmelCase							=			collections.defaultdict(UpperCamelCase__       )

	# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
	for attr_name in dir(UpperCamelCase__       ):
		__UpperCAmelCase							=			None
		if _re_tf_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			tf_models
			__UpperCAmelCase							=			_re_tf_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_flax_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			flax_models
			__UpperCAmelCase							=			_re_flax_models.match(UpperCamelCase__       ).groups()[0]
		elif _re_pt_models.match(UpperCamelCase__       ) is not None:
			__UpperCAmelCase							=			pt_models
			__UpperCAmelCase							=			_re_pt_models.match(UpperCamelCase__       ).groups()[0]

		if lookup_dict is not None:
			while len(UpperCamelCase__       ) > 0:
				if attr_name in model_prefix_to_model_type:
					__UpperCAmelCase							=			True
					break
				# Try again after removing the last word in the name
				__UpperCAmelCase							=			''''''.join(camel_case_split(UpperCamelCase__       )[:-1]       )

	__UpperCAmelCase							=			set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
	__UpperCAmelCase							=			list(UpperCamelCase__       )
	all_models.sort()

	__UpperCAmelCase							=			{'''model_type''': all_models}
	__UpperCAmelCase							=			[pt_models[t] for t in all_models]
	__UpperCAmelCase							=			[tf_models[t] for t in all_models]
	__UpperCAmelCase							=			[flax_models[t] for t in all_models]

	# Now let's use the auto-mapping names to make sure
	__UpperCAmelCase							=			{}
	for t in all_models:
		if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoProcessor'''
		elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoTokenizer'''
		elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
			__UpperCAmelCase							=			'''AutoFeatureExtractor'''
		else:
			# Default to AutoTokenizer if a model has nothing, for backward compatibility.
			__UpperCAmelCase							=			'''AutoTokenizer'''

	__UpperCAmelCase							=			[processors[t] for t in all_models]

	return pd.DataFrame(UpperCamelCase__       )





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[
	    transformers_module.models.auto.modeling_auto,
	    transformers_module.models.auto.modeling_tf_auto,
	    transformers_module.models.auto.modeling_flax_auto,
	]
	for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
		__UpperCAmelCase							=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
		__UpperCAmelCase							=			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
		# Loop through all three frameworks
		for module, cls, mapping in zip(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       ):
			# The type of pipeline may not exist in this framework
			if not hasattr(UpperCamelCase__    ,					UpperCamelCase__       ):
				continue
			# First extract all model_names
			__UpperCAmelCase							=			[]
			for name in getattr(UpperCamelCase__    ,					UpperCamelCase__       ).values():
				if isinstance(UpperCamelCase__    ,					UpperCamelCase__       ):
					model_names.append(UpperCamelCase__       )
				else:
					model_names.extend(list(UpperCamelCase__       )       )

            # Add pipeline tag and auto model class for those models
			table.update({model_name: (pipeline_tag, cls) for model_name in model_names}       )

	return table





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_frameworks_table()
	__UpperCAmelCase							=			Dataset.from_pandas(UpperCamelCase__       )

	__UpperCAmelCase							=			hf_hub_download(
	    '''huggingface/transformers-metadata'''    ,					'''pipeline_tags.json'''    ,					repo_type='''dataset'''    ,					token=UpperCamelCase__       )
	__UpperCAmelCase							=			Dataset.from_json(UpperCamelCase__       )
	__UpperCAmelCase							=			{
	    tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
	    for i in range(len(UpperCamelCase__       )       )
	}
	__UpperCAmelCase							=			update_pipeline_and_auto_class_table(UpperCamelCase__       )

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__UpperCAmelCase							=			sorted(table.keys()       )
	__UpperCAmelCase							=			pd.DataFrame(
	    {
	        '''model_class''': model_classes,
	        '''pipeline_tag''': [table[m][0] for m in model_classes],
	        '''auto_class''': [table[m][1] for m in model_classes],
	    }       )
	__UpperCAmelCase							=			Dataset.from_pandas(UpperCamelCase__       )

	with tempfile.TemporaryDirectory() as tmp_dir:
		frameworks_dataset.to_json(os.path.join(UpperCamelCase__    ,					'''frameworks.json'''       )       )
		tags_dataset.to_json(os.path.join(UpperCamelCase__    ,					'''pipeline_tags.json'''       )       )

		if commit_sha is not None:
			__UpperCAmelCase							=			(
			    f"""Update with commit {commit_sha}\n\nSee: """
			    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
			)
		else:
			__UpperCAmelCase							=			'''Update'''

		upload_folder(
		    repo_id='''huggingface/transformers-metadata'''    ,					folder_path=UpperCamelCase__    ,					repo_type='''dataset'''    ,					token=UpperCamelCase__    ,					commit_message=UpperCamelCase__    ,					)





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	__UpperCAmelCase							=			transformers_module.pipelines.SUPPORTED_TASKS
	__UpperCAmelCase							=			[]
	for key in pipeline_tasks:
		if key not in in_table:
			__UpperCAmelCase							=			pipeline_tasks[key]['''pt''']
			if isinstance(UpperCamelCase__    ,					(list, tuple)       ):
				__UpperCAmelCase							=			model[0]
			__UpperCAmelCase							=			model.__name__
			if model not in in_table.values():
				missing.append(UpperCamelCase__       )

	if len(UpperCamelCase__       ) > 0:
		__UpperCAmelCase							=			''', '''.join(UpperCamelCase__       )
		raise ValueError(
		    '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
		    f"""`utils/update_metadata.py`: {msg}. Please add them!"""       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Union[str, Any]						       =	argparse.ArgumentParser()
					parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
					parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
					parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
					__lowerCAmelCase	:       Tuple						       =	parser.parse_args()

					if args.check_only:
										check_pipeline_tags()
					else:
										update_metadata(args.token, args.commit_sha)
