


'''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      :Any    	=			'''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase      :List[Any]    	=			re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase      :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      :Dict    	=			re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase      :Tuple    	=			[
    ('''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			(			lowerCAmelCase				:	List[str]		):





	"""simple docstring"""







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





def lowerCamelCase			(			):





	"""simple docstring"""







	__magic_name__       :    Union[str, Any]     	=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__magic_name__       :    Union[str, Any]     	=     {
	    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.
	__magic_name__       :    Tuple     	=     collections.defaultdict(__A		)
	__magic_name__       :    Optional[Any]     	=     collections.defaultdict(__A		)
	__magic_name__       :    Union[str, Any]     	=     collections.defaultdict(__A		)

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

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

	__magic_name__       :    Optional[int]     	=     set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
	__magic_name__       :    Any     	=     list(__A		)
	all_models.sort()

	__magic_name__       :    str     	=     {'model_type': all_models}
	__magic_name__       :    Optional[Any]     	=     [pt_models[t] for t in all_models]
	__magic_name__       :    Union[str, Any]     	=     [tf_models[t] for t in all_models]
	__magic_name__       :    Any     	=     [flax_models[t] for t in all_models]

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

	__magic_name__       :    Optional[Any]     	=     [processors[t] for t in all_models]

	return pd.DataFrame(__A		)





def lowerCamelCase			(			lowerCAmelCase				:	Tuple		):





	"""simple docstring"""







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

            # 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			(			lowerCAmelCase				:	Optional[int]		,		lowerCAmelCase				:	int		):





	"""simple docstring"""







	__magic_name__       :    List[str]     	=     get_frameworks_table()
	__magic_name__       :    Union[str, Any]     	=     Dataset.from_pandas(__A		)

	__magic_name__       :    str     	=     hf_hub_download(
	    'huggingface/transformers-metadata'		,		'pipeline_tags.json'		,		repo_type='dataset'		,		token=__A		)
	__magic_name__       :    List[Any]     	=     Dataset.from_json(__A		)
	__magic_name__       :    Tuple     	=     {
	    tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
	    for i in range(len(__A		)		)
	}
	__magic_name__       :    int     	=     update_pipeline_and_auto_class_table(__A		)

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__magic_name__       :    Any     	=     sorted(table.keys()		)
	__magic_name__       :    Optional[Any]     	=     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],
	    }		)
	__magic_name__       :    Dict     	=     Dataset.from_pandas(__A		)

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

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

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





def lowerCamelCase			(			):





	"""simple docstring"""







	__magic_name__       :    Any     	=     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	__magic_name__       :    Optional[int]     	=     transformers_module.pipelines.SUPPORTED_TASKS
	__magic_name__       :    List[Any]     	=     []
	for key in pipeline_tasks:
		if key not in in_table:
			__magic_name__       :    List[Any]     	=     pipeline_tasks[key]['pt']
			if isinstance(__A		,		(list, tuple)		):
				__magic_name__       :    Union[str, Any]     	=     model[0]
			__magic_name__       :    Optional[Any]     	=     model.__name__
			if model not in in_table.values():
				missing.append(__A		)

	if len(__A		) > 0:
		__magic_name__       :    Optional[Any]     	=     ', '.join(__A		)
		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      :Tuple    	=			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      :List[str]    	=			parser.parse_args()

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




'''simple docstring'''





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf






class __A			(  enum.Enum						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       0
 __lowerCamelCase    :       Any          =       1
 __lowerCamelCase    :       List[Any]          =       2






@add_end_docstrings(A						)
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '



 def __init__(self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      super().__init__(*A ,   **A       )
      self.check_model_type(
          TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING       )
      if "prefix" not in self._preprocess_params:
           # This is very specific. The logic is quite complex and needs to be done
           # as a "default".
           # It also defines both some preprocess_kwargs and generate_kwargs
           # which is why we cannot put them in their respective methods.
           _a      =  None
           if self.model.config.prefix is not None:
                _a      =  self.model.config.prefix
           if prefix is None and self.model.__class__.__name__ in [
               "XLNetLMHeadModel",
               "TransfoXLLMHeadModel",
               "TFXLNetLMHeadModel",
               "TFTransfoXLLMHeadModel",
           ]:
                # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                _a      =  self.XL_PREFIX
           if prefix is not None:
                # Recalculate some generate_kwargs linked to prefix.
                _a  ,    _a  ,    _a      =  self._sanitize_parameters(prefix=A ,   **self._forward_params       )
                _a      =  {**self._preprocess_params, **preprocess_params}
                _a      =  {**self._forward_params, **forward_params}



 def 					a__  (self ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   **A ,   )    ->     str:


      """simple docstring"""



      _a      =  {}
      if prefix is not None:
           _a      =  prefix
      if prefix:
           _a      =  self.tokenizer(
               A ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
           _a      =  prefix_inputs['''input_ids'''].shape[-1]

      if handle_long_generation is not None:
           if handle_long_generation not in {"hole"}:
                raise ValueError(
                    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                    ''' [None, \'hole\']'''       )
           _a      =  handle_long_generation

      preprocess_params.update(A       )
      _a      =  generate_kwargs

      _a      =  {}
      if return_full_text is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''       )
           if return_tensors is not None:
                raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
      if return_tensors is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.TENSORS
      if return_type is not None:
           _a      =  return_type
      if clean_up_tokenization_spaces is not None:
           _a      =  clean_up_tokenization_spaces

      if stop_sequence is not None:
           _a      =  self.tokenizer.encode(A ,   add_special_tokens=A       )
           if len(A       ) > 1:
                warnings.warn(
                    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                    ''' the stop sequence will be used as the stop sequence string in the interim.'''       )
           _a      =  stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params



 def 					a__  (self ,   *A ,   **A       )    ->     List[Any]:


      """simple docstring"""



      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
           kwargs.update({'''add_space_before_punct_symbol''': True}       )

      return super()._parse_and_tokenize(*A ,   **A       )



 def __call__(self ,   A ,   **A       )    ->     int:


      """simple docstring"""



      return super().__call__(A ,   **A       )



 def 					a__  (self ,   A ,   A="" ,   A=None ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  self.tokenizer(
          prefix + prompt_text ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
      _a      =  prompt_text

      if handle_long_generation == "hole":
           _a      =  inputs['''input_ids'''].shape[-1]
           if "max_new_tokens" in generate_kwargs:
                _a      =  generate_kwargs['''max_new_tokens''']
           else:
                _a      =  generate_kwargs.get('''max_length''' ,   self.model.config.max_length       ) - cur_len
                if new_tokens < 0:
                     raise ValueError('''We cannot infer how many new tokens are expected'''       )
           if cur_len + new_tokens > self.tokenizer.model_max_length:
                _a      =  self.tokenizer.model_max_length - new_tokens
                if keep_length <= 0:
                     raise ValueError(
                         '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
                         ''' models max length'''       )

                _a      =  inputs['''input_ids'''][:, -keep_length:]
                if "attention_mask" in inputs:
                     _a      =  inputs['''attention_mask'''][:, -keep_length:]

      return inputs



 def 					a__  (self ,   A ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  model_inputs['''input_ids''']
      _a      =  model_inputs.get('''attention_mask''' ,   A       )
      # Allow empty prompts
      if input_ids.shape[1] == 0:
           _a      =  None
           _a      =  None
           _a      =  1
      else:
           _a      =  input_ids.shape[0]
      _a      =  model_inputs.pop('''prompt_text'''       )

      # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
      # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
      _a      =  generate_kwargs.pop('''prefix_length''' ,   0       )
      if prefix_length > 0:
           _a      =  '''max_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].max_new_tokens is not None
           )
           if not has_max_new_tokens:
                _a      =  generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
                generate_kwargs["max_length"] += prefix_length
           _a      =  '''min_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].min_new_tokens is not None
           )
           if not has_min_new_tokens and "min_length" in generate_kwargs:
                generate_kwargs["min_length"] += prefix_length

        # BS x SL
      _a      =  self.model.generate(input_ids=A ,   attention_mask=A ,   **A       )
      _a      =  generated_sequence.shape[0]
      if self.framework == "pt":
           _a      =  generated_sequence.reshape(A ,   out_b // in_b ,   *generated_sequence.shape[1:]       )
      elif self.framework == "tf":
           _a      =  tf.reshape(A ,   (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
      return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



 def 					a__  (self ,   A ,   A=ReturnType.FULL_TEXT ,   A=True       )    ->     str:


      """simple docstring"""



      _a      =  model_outputs['''generated_sequence'''][0]
      _a      =  model_outputs['''input_ids''']
      _a      =  model_outputs['''prompt_text''']
      _a      =  generated_sequence.numpy().tolist()
      _a      =  []
      for sequence in generated_sequence:
           if return_type == ReturnType.TENSORS:
                _a      =  {'''generated_token_ids''': sequence}
           elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                # Decode text
                _a      =  self.tokenizer.decode(
                    A ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )

                # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                if input_ids is None:
                     _a      =  0
                else:
                     _a      =  len(
                         self.tokenizer.decode(
                             input_ids[0] ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )       )

                if return_type == ReturnType.FULL_TEXT:
                     _a      =  prompt_text + text[prompt_length:]
                else:
                     _a      =  text[prompt_length:]

                _a      =  {'''generated_text''': all_text}
           records.append(A       )

      return records



