

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      				=     '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
lowerCamelCase      				=     re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCamelCase      				=     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      				=     re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase      				=     [
    ('''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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					__lowerCAmelCase					=							{
					    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.
					__lowerCAmelCase					=							collections.defaultdict(UpperCAmelCase__      )
					__lowerCAmelCase					=							collections.defaultdict(UpperCAmelCase__      )
					__lowerCAmelCase					=							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__      ):
										__lowerCAmelCase					=							None
										if _re_tf_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							tf_models
															__lowerCAmelCase					=							_re_tf_models.match(UpperCAmelCase__      ).groups()[0]
										elif _re_flax_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							flax_models
															__lowerCAmelCase					=							_re_flax_models.match(UpperCAmelCase__      ).groups()[0]
										elif _re_pt_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							pt_models
															__lowerCAmelCase					=							_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:
																									__lowerCAmelCase					=							True
																									break
																				# Try again after removing the last word in the name
																				__lowerCAmelCase					=							''.join(camel_case_split(UpperCAmelCase__      )[:-1]      )

					__lowerCAmelCase					=							set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					all_models.sort()

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

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

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

					return pd.DataFrame(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[
					    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:
										__lowerCAmelCase					=							[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
										__lowerCAmelCase					=							[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
															__lowerCAmelCase					=							[]
															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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							get_frameworks_table()
					__lowerCAmelCase					=							Dataset.from_pandas(UpperCAmelCase__      )

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

					# Sort the model classes to avoid some nondeterministic updates to create false update commits.
					__lowerCAmelCase					=							sorted(table.keys()      )
					__lowerCAmelCase					=							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],
					    }      )
					__lowerCAmelCase					=							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:
															__lowerCAmelCase					=							(
															    F"""Update with commit {commit_sha}\n\nSee: """
															    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
															)
										else:
															__lowerCAmelCase					=							'Update'

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


def        __lowercase    (  ):

					"""simple docstring"""






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

					if len(UpperCAmelCase__      ) > 0:
										__lowerCAmelCase					=							', '.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      				=     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      				=     parser.parse_args()

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









import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												import datasets

												__lowerCAmelCase					=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,						'image'					,						split='test'							)
												__lowerCAmelCase					=							depth_estimator(
												    [
												        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
												        'http://images.cocodataset.org/val2017/000000039769.jpg',
												        # RGBA
												        dataset[0]['file'],
												        # LA
												        dataset[1]['file'],
												        # L
												        dataset[2]['file'],
												    ]							)
												self.assertEqual(
												    [
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__lowerCAmelCase					=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
												__lowerCAmelCase					=							hashimage(outputs['depth']							)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)					,						29.304							)
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)					,						2.662							)




							@require_torch
							def        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






