
"""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
UpperCamelCase			:       List[Any]									=				"src/transformers"


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


# Regexes that match TF/Flax/PT model names.
UpperCamelCase			:       Tuple									=				re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
UpperCamelCase			:       Optional[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.
UpperCamelCase			:       Optional[int]									=				re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCamelCase			:       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 							A		(       snake_case   :Tuple	)   ->			Dict:
 __UpperCamelCase       						= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'       ,      snake_case	)
 return [m.group(0	) for m in matches]






def 							A		(       )   ->			Union[str, Any]:
 __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(snake_case	)
 __UpperCamelCase       						= collections.defaultdict(snake_case	)
 __UpperCamelCase       						= collections.defaultdict(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(snake_case	):
  __UpperCamelCase       						= None
  if _re_tf_models.match(snake_case	) is not None:
   __UpperCamelCase       						= tf_models
   __UpperCamelCase       						= _re_tf_models.match(snake_case	).groups()[0]
  elif _re_flax_models.match(snake_case	) is not None:
   __UpperCamelCase       						= flax_models
   __UpperCamelCase       						= _re_flax_models.match(snake_case	).groups()[0]
  elif _re_pt_models.match(snake_case	) is not None:
   __UpperCamelCase       						= pt_models
   __UpperCamelCase       						= _re_pt_models.match(snake_case	).groups()[0]

  if lookup_dict is not None:
   while len(snake_case	) > 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(snake_case	)[:-1]	)

 __UpperCamelCase       						= set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
 __UpperCamelCase       						= list(snake_case	)
 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(snake_case	)






def 							A		(       snake_case   :Tuple	)   ->			Dict:
 __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(snake_case       ,      snake_case       ,      snake_case	):
   # The type of pipeline may not exist in this framework
   if not hasattr(snake_case       ,      snake_case	):
    continue
   # First extract all model_names
   __UpperCamelCase       						= []
   for name in getattr(snake_case       ,      snake_case	).values():
    if isinstance(snake_case       ,      snake_case	):
     model_names.append(snake_case	)
    else:
     model_names.extend(list(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 							A		(       snake_case   :Any       ,      snake_case   :Union[str, Any]	)   ->			Optional[int]:
 __UpperCamelCase       						= get_frameworks_table()
 __UpperCamelCase       						= Dataset.from_pandas(snake_case	)

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

 # 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(snake_case	)

 with tempfile.TemporaryDirectory() as tmp_dir:
  frameworks_dataset.to_json(os.path.join(snake_case       ,      'frameworks.json'	)	)
  tags_dataset.to_json(os.path.join(snake_case       ,      '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=snake_case       ,      repo_type='dataset'       ,      token=snake_case       ,      commit_message=snake_case       ,      )






def 							A		(       )   ->			Any:
 __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(snake_case       ,      (list, tuple)	):
    __UpperCamelCase       						= model[0]
   __UpperCamelCase       						= model.__name__
   if model not in in_table.values():
    missing.append(snake_case	)

 if len(snake_case	) > 0:
  __UpperCamelCase       						= ', '.join(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__":
      UpperCamelCase			:       Optional[int]									=				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.")
      UpperCamelCase			:       Optional[Any]									=				parser.parse_args()

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





"""simple docstring"""

import argparse
import struct
import unittest

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= data

      # Initialize hash values
      __UpperCamelCase       						= [
          0x6a_09_e6_67,
          0xbb_67_ae_85,
          0x3c_6e_f3_72,
          0xa5_4f_f5_3a,
          0x51_0e_52_7f,
          0x9b_05_68_8c,
          0x1f_83_d9_ab,
          0x5b_e0_cd_19,
      ]

      # Initialize round constants
      __UpperCamelCase       						= [
          0x42_8a_2f_98,
          0x71_37_44_91,
          0xb5_c0_fb_cf,
          0xe9_b5_db_a5,
          0x39_56_c2_5b,
          0x59_f1_11_f1,
          0x92_3f_82_a4,
          0xab_1c_5e_d5,
          0xd8_07_aa_98,
          0x12_83_5b_01,
          0x24_31_85_be,
          0x55_0c_7d_c3,
          0x72_be_5d_74,
          0x80_de_b1_fe,
          0x9b_dc_06_a7,
          0xc1_9b_f1_74,
          0xe4_9b_69_c1,
          0xef_be_47_86,
          0x0f_c1_9d_c6,
          0x24_0c_a1_cc,
          0x2d_e9_2c_6f,
          0x4a_74_84_aa,
          0x5c_b0_a9_dc,
          0x76_f9_88_da,
          0x98_3e_51_52,
          0xa8_31_c6_6d,
          0xb0_03_27_c8,
          0xbf_59_7f_c7,
          0xc6_e0_0b_f3,
          0xd5_a7_91_47,
          0x06_ca_63_51,
          0x14_29_29_67,
          0x27_b7_0a_85,
          0x2e_1b_21_38,
          0x4d_2c_6d_fc,
          0x53_38_0d_13,
          0x65_0a_73_54,
          0x76_6a_0a_bb,
          0x81_c2_c9_2e,
          0x92_72_2c_85,
          0xa2_bf_e8_a1,
          0xa8_1a_66_4b,
          0xc2_4b_8b_70,
          0xc7_6c_51_a3,
          0xd1_92_e8_19,
          0xd6_99_06_24,
          0xf4_0e_35_85,
          0x10_6a_a0_70,
          0x19_a4_c1_16,
          0x1e_37_6c_08,
          0x27_48_77_4c,
          0x34_b0_bc_b5,
          0x39_1c_0c_b3,
          0x4e_d8_aa_4a,
          0x5b_9c_ca_4f,
          0x68_2e_6f_f3,
          0x74_8f_82_ee,
          0x78_a5_63_6f,
          0x84_c8_78_14,
          0x8c_c7_02_08,
          0x90_be_ff_fa,
          0xa4_50_6c_eb,
          0xbe_f9_a3_f7,
          0xc6_71_78_f2,
      ]

      __UpperCamelCase       						= self.preprocessing(self.data   )
      self.final_hash()





     @staticmethod
     def 		UpperCAmelCase      (			__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= b'\x80' + (b'\x00' * (63 - (len(__UpperCAmelCase   ) + 8) % 64))
      __UpperCamelCase       						= struct.pack('>Q'			,  (len(__UpperCAmelCase   ) * 8)   )
      return data + padding + big_endian_integer





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [
          self.preprocessed_data[x : x + 64]
          for x in range(0			,  len(self.preprocessed_data   )			,  64   )
      ]

      for block in self.blocks:
       # Convert the given block into a list of 4 byte integers
       __UpperCamelCase       						= list(struct.unpack('>16L'			,  __UpperCAmelCase   )   )
       # add 48 0-ed integers
       words += [0] * 48

       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= self.hashes

       for index in range(0			,  64   ):
        if index > 15:
         # modify the zero-ed indexes at the end of the array
         __UpperCamelCase       						= (
             self.ror(words[index - 15]			,  7   )
             ^ self.ror(words[index - 15]			,  18   )
             ^ (words[index - 15] >> 3)
         )
         __UpperCamelCase       						= (
             self.ror(words[index - 2]			,  17   )
             ^ self.ror(words[index - 2]			,  19   )
             ^ (words[index - 2] >> 10)
         )

         __UpperCamelCase       						= (
             words[index - 16] + sa + words[index - 7] + sa
         ) % 0x1_00_00_00_00

        # Compression
        __UpperCamelCase       						= self.ror(__UpperCAmelCase			,  6   ) ^ self.ror(__UpperCAmelCase			,  11   ) ^ self.ror(__UpperCAmelCase			,  25   )
        __UpperCamelCase       						= (e & f) ^ ((~e & 0xff_ff_ff_ff) & g)
        __UpperCamelCase       						= (
            h + sa + ch + self.round_constants[index] + words[index]
        ) % 0x1_00_00_00_00
        __UpperCamelCase       						= self.ror(__UpperCAmelCase			,  2   ) ^ self.ror(__UpperCAmelCase			,  13   ) ^ self.ror(__UpperCAmelCase			,  22   )
        __UpperCamelCase       						= (a & b) ^ (a & c) ^ (b & c)
        __UpperCamelCase       						= (sa + maj) % 0x1_00_00_00_00

        __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= (
            g,
            f,
            e,
            ((d + tempa) % 0x1_00_00_00_00),
            c,
            b,
            a,
            ((tempa + tempa) % 0x1_00_00_00_00),
        )

       __UpperCamelCase       						= [a, b, c, d, e, f, g, h]

       # Modify final values
       __UpperCamelCase       						= [
           ((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
           for index, element in enumerate(self.hashes   )
       ]

      __UpperCamelCase       						= ''.join([hex(__UpperCAmelCase   )[2:].zfill(8   ) for value in self.hashes]   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return 0xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)


class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      import hashlib

      __UpperCamelCase       						= bytes('Test String'			,  'utf-8'   )
      self.assertEqual(SHAaaa(__UpperCAmelCase   ).hash			,  hashlib.shaaaa(__UpperCAmelCase   ).hexdigest()   )






def 							A		(       )   ->			None:
 import doctest

 doctest.testmod()

 __UpperCamelCase       						= argparse.ArgumentParser()
 parser.add_argument(
     '-s'       ,      '--string'       ,      dest='input_string'       ,      default='Hello World!! Welcome to Cryptography'       ,      help='Hash the string'       ,      )
 parser.add_argument(
     '-f'       ,      '--file'       ,      dest='input_file'       ,      help='Hash contents of a file'	)

 __UpperCamelCase       						= parser.parse_args()

 __UpperCamelCase       						= args.input_string

 # hash input should be a bytestring
 if args.input_file:
  with open(args.input_file       ,      'rb'	) as f:
   __UpperCamelCase       						= f.read()
 else:
  __UpperCamelCase       						= bytes(snake_case       ,      'utf-8'	)

 print(SHAaaa(snake_case	).hash	)


if __name__ == "__main__":
      main()



