



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
lowercase_						=       """src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase_						=       [
    ("""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	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"  ,					SCREAMING_SNAKE_CASE_					)
			return [m.group(0					) for m in matches]



def __lowerCAmelCase	(       ):
			lowercase__   					=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			lowercase__   					=			{
			    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.
			lowercase__   					=			collections.defaultdict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			collections.defaultdict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			collections.defaultdict(SCREAMING_SNAKE_CASE_					)

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

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

			lowercase__   					=			set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
			lowercase__   					=			list(SCREAMING_SNAKE_CASE_					)
			all_models.sort()

			lowercase__   					=			{"model_type": all_models}
			lowercase__   					=			[pt_models[t] for t in all_models]
			lowercase__   					=			[tf_models[t] for t in all_models]
			lowercase__   					=			[flax_models[t] for t in all_models]

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

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

			return pd.DataFrame(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[
			    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:
						lowercase__   					=			[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
						lowercase__   					=			[auto_class, f'''TF_{auto_class}''', f'''Flax_{auto_class}''']
						# Loop through all three frameworks
						for module, cls, mapping in zip(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
									# The type of pipeline may not exist in this framework
									if not hasattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
												continue
									# First extract all model_names
									lowercase__   					=			[]
									for name in getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					).values():
												if isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
															model_names.append(SCREAMING_SNAKE_CASE_					)
												else:
															model_names.extend(list(SCREAMING_SNAKE_CASE_					)					)

            # 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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			get_frameworks_table()
			lowercase__   					=			Dataset.from_pandas(SCREAMING_SNAKE_CASE_					)

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

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			lowercase__   					=			sorted(table.keys()					)
			lowercase__   					=			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],
			    }					)
			lowercase__   					=			Dataset.from_pandas(SCREAMING_SNAKE_CASE_					)

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

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

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



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

			if len(SCREAMING_SNAKE_CASE_					) > 0:
						lowercase__   					=			", ".join(SCREAMING_SNAKE_CASE_					)
						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__":
							lowercase_						=       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.""")
							lowercase_						=       parser.parse_args()

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









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			for i in range(SCREAMING_SNAKE_CASE_					):
						for j in range(i + 1  ,					SCREAMING_SNAKE_CASE_					):
									if numbers[j] < numbers[i]:
												lowercase__					,    lowercase__   					=			numbers[j], numbers[i]
			return numbers


if __name__ == "__main__":
							lowercase_						=       input("""Enter numbers separated by a comma:\n""").strip()
							lowercase_						=       [int(item) for item in user_input.split(""",""")]
							print(exchange_sort(unsorted))




