




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 lowerCamelCase    :		Any			   =       "platform"

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[Any]     ,							lowercase     :	List[str]=None     ,							lowercase     :	List[str]=None     ,							lowercase     :	str=None     ,							lowercase     :	Tuple=None     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	Union[str, Any]=None     ,							):


      '''simple docstring'''



      if attention_mask is None:
            lowerCamelCase_							=   np.where(input_ids != config.pad_token_id     ,							1     ,							0					)
      if decoder_attention_mask is None:
            lowerCamelCase_							=   np.where(decoder_input_ids != config.pad_token_id     ,							1     ,							0					)
      if head_mask is None:
            lowerCamelCase_							=   np.ones((config.encoder_layers, config.encoder_attention_heads)					)
      if decoder_head_mask is None:
            lowerCamelCase_							=   np.ones((config.decoder_layers, config.decoder_attention_heads)					)
      if cross_attn_head_mask is None:
            lowerCamelCase_							=   np.ones((config.decoder_layers, config.decoder_attention_heads)					)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }







class   A:



 '''simple docstring'''


 def __init__(    self :  List[Any]						,							A_ :  Tuple						,							A_ :  List[str]=13						,							A_ :  Optional[Any]=7						,							A_ :  int=True						,							A_ :  List[Any]=False						,							A_ :  str=99						,							A_ :  str=16						,							A_ :  Dict=2						,							A_ :  List[str]=4						,							A_ :  Optional[int]=4						,							A_ :  Optional[int]="gelu"						,							A_ :  List[str]=0.1						,							A_ :  Tuple=0.1						,							A_ :  List[Any]=32						,							A_ :  Dict=2						,							A_ :  Dict=1						,							A_ :  Tuple=0						,							A_ :  str=0.02						,							)       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   seq_length
       lowerCamelCase_							=   is_training
       lowerCamelCase_							=   use_labels
       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   eos_token_id
       lowerCamelCase_							=   pad_token_id
       lowerCamelCase_							=   bos_token_id
       lowerCamelCase_							=   initializer_range

 def     a__			(    self :  Tuple  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size  )						,							3						,							self.vocab_size  )
       lowerCamelCase_							=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,							dtype=np.intaa  ))						,							-1  )

       lowerCamelCase_							=   shift_tokens_right(A_						,							1						,							2  )

       lowerCamelCase_							=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size						,							d_model=self.hidden_size						,							encoder_layers=self.num_hidden_layers						,							decoder_layers=self.num_hidden_layers						,							encoder_attention_heads=self.num_attention_heads						,							decoder_attention_heads=self.num_attention_heads						,							encoder_ffn_dim=self.intermediate_size						,							decoder_ffn_dim=self.intermediate_size						,							dropout=self.hidden_dropout_prob						,							attention_dropout=self.attention_probs_dropout_prob						,							max_position_embeddings=self.max_position_embeddings						,							eos_token_id=self.eos_token_id						,							bos_token_id=self.bos_token_id						,							pad_token_id=self.pad_token_id						,							initializer_range=self.initializer_range						,							use_cache=A_						,							)
       lowerCamelCase_							=   prepare_blenderbot_inputs_dict(A_						,							A_						,							A_  )
       return config, inputs_dict

 def     a__			(    self :  Optional[Any]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.prepare_config_and_inputs()
       return config, inputs_dict

 def     a__			(    self :  int						,							A_ :  Optional[int]						,							A_ :  str						,							A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   20
       lowerCamelCase_							=   model_class_name(A_  )

       lowerCamelCase_							=   model.encode(inputs_dict['input_ids']  )

       lowerCamelCase_					,    lowerCamelCase_							=   (
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       lowerCamelCase_							=   model.init_cache(decoder_input_ids.shape[0]						,							A_						,							A_  )
       lowerCamelCase_							=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,							dtype='i4'  )

       lowerCamelCase_							=   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]						,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,							)
       lowerCamelCase_							=   model.decode(
           decoder_input_ids[:, :-1]						,							A_						,							decoder_attention_mask=A_						,							past_key_values=A_						,							decoder_position_ids=A_						,							)

       lowerCamelCase_							=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,							dtype='i4'  )
       lowerCamelCase_							=   model.decode(
           decoder_input_ids[:, -1:]						,							A_						,							decoder_attention_mask=A_						,							past_key_values=outputs_cache.past_key_values						,							decoder_position_ids=A_						,							)

       lowerCamelCase_							=   model.decode(A_						,							A_  )

       lowerCamelCase_							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
       self.parent.assertTrue(diff < 1E-3						,							msg=f"""Max diff is {diff}"""  )





 def     a__			(    self :  Optional[Any]						,							A_ :  Dict						,							A_ :  Tuple						,							A_ :  Union[str, Any]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   20
       lowerCamelCase_							=   model_class_name(A_  )

       lowerCamelCase_							=   model.encode(inputs_dict['input_ids']  )

       lowerCamelCase_					,    lowerCamelCase_							=   (
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       lowerCamelCase_							=   jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
           ]						,							axis=-1						,							)

       lowerCamelCase_							=   model.init_cache(decoder_input_ids.shape[0]						,							A_						,							A_  )
       lowerCamelCase_							=   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]						,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,							)

       lowerCamelCase_							=   model.decode(
           decoder_input_ids[:, :-1]						,							A_						,							decoder_attention_mask=A_						,							past_key_values=A_						,							decoder_position_ids=A_						,							)
       lowerCamelCase_							=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,							dtype='i4'  )
       lowerCamelCase_							=   model.decode(
           decoder_input_ids[:, -1:]						,							A_						,							past_key_values=outputs_cache.past_key_values						,							decoder_attention_mask=A_						,							decoder_position_ids=A_						,							)

       lowerCamelCase_							=   model.decode(A_						,							A_						,							decoder_attention_mask=A_  )

       lowerCamelCase_							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
       self.parent.assertTrue(diff < 1E-3						,							msg=f"""Max diff is {diff}"""  )







@require_flax
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						99

 def     a__			(    self :  int  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]						,							dtype=np.intaa						,							)

       lowerCamelCase_							=   input_ids.shape[0]
       lowerCamelCase_							=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size						,							d_model=24						,							encoder_layers=2						,							decoder_layers=2						,							encoder_attention_heads=2						,							decoder_attention_heads=2						,							encoder_ffn_dim=32						,							decoder_ffn_dim=32						,							max_position_embeddings=48						,							eos_token_id=2						,							pad_token_id=1						,							bos_token_id=0						,							)
       return config, input_ids, batch_size

 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   self._get_config_and_data()
       lowerCamelCase_							=   FlaxBlenderbotSmallForConditionalGeneration(A_  )
       lowerCamelCase_							=   lm_model(input_ids=A_  )
       lowerCamelCase_							=   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   BlenderbotSmallConfig(
           vocab_size=self.vocab_size						,							d_model=14						,							encoder_layers=2						,							decoder_layers=2						,							encoder_attention_heads=2						,							decoder_attention_heads=2						,							encoder_ffn_dim=8						,							decoder_ffn_dim=8						,							max_position_embeddings=48						,							)
       lowerCamelCase_							=   FlaxBlenderbotSmallForConditionalGeneration(A_  )
       lowerCamelCase_							=   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   lm_model(input_ids=A_						,							decoder_input_ids=A_  )
       lowerCamelCase_							=   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,							A_  )





 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,							dtype=np.intaa  )
       lowerCamelCase_							=   shift_tokens_right(A_						,							1						,							2  )
       lowerCamelCase_							=   np.equal(A_						,							1  ).astype(np.floataa  ).sum()
       lowerCamelCase_							=   np.equal(A_						,							1  ).astype(np.floataa  ).sum()
       self.assertEqual(shifted.shape						,							input_ids.shape  )
       self.assertEqual(A_						,							n_pad_before - 1  )
       self.assertTrue(np.equal(shifted[:, 0]						,							2  ).all()  )







@require_flax
class   A(						UpperCamelCase							,				unittest.TestCase							,				UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						True
 UpperCamelCase						       =						(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 UpperCamelCase						       =						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

 def     a__			(    self :  Dict  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   FlaxBlenderbotSmallModelTester(self  )

 def     a__			(    self :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(A_						,							A_						,							A_  )

 def     a__			(    self :  Tuple  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(A_						,							A_						,							A_  )

 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__  ):
                   lowerCamelCase_							=   self._prepare_for_class(A_						,							A_  )
                   lowerCamelCase_							=   model_class(A_  )

                   @jax.jit
                   def encode_jitted(A_ :  Optional[Any]						,							A_ :  int=None						,							**A_ :  List[Any]  ):
                         return model.encode(input_ids=A_						,							attention_mask=A_  )

                   with self.subTest('JIT Enabled'  ):
                         lowerCamelCase_							=   encode_jitted(**A_  ).to_tuple()

                   with self.subTest('JIT Disabled'  ):
                         with jax.disable_jit():
                               lowerCamelCase_							=   encode_jitted(**A_  ).to_tuple()

                   self.assertEqual(len(A_  )						,							len(A_  )  )
                   for jitted_output, output in zip(A_						,							A_  ):
                         self.assertEqual(jitted_output.shape						,							output.shape  )

 def     a__			(    self :  Union[str, Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__  ):
                   lowerCamelCase_							=   model_class(A_  )
                   lowerCamelCase_							=   model.encode(inputs_dict['input_ids']						,							inputs_dict['attention_mask']  )

                   lowerCamelCase_							=   {
                       'decoder_input_ids': inputs_dict['decoder_input_ids'],
                       'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                       'encoder_outputs': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(A_ :  Union[str, Any]						,							A_ :  Optional[Any]						,							A_ :  Optional[Any]  ):
                         return model.decode(
                             decoder_input_ids=A_						,							decoder_attention_mask=A_						,							encoder_outputs=A_						,							)

                   with self.subTest('JIT Enabled'  ):
                         lowerCamelCase_							=   decode_jitted(**A_  ).to_tuple()

                   with self.subTest('JIT Disabled'  ):
                         with jax.disable_jit():
                               lowerCamelCase_							=   decode_jitted(**A_  ).to_tuple()

                   self.assertEqual(len(A_  )						,							len(A_  )  )
                   for jitted_output, output in zip(A_						,							A_  ):
                         self.assertEqual(jitted_output.shape						,							output.shape  )





 @slow
 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_							=   model_class_name.from_pretrained('facebook/blenderbot_small-90M'  )
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             lowerCamelCase_							=   np.ones((1, 1)  ) * model.config.eos_token_id
             lowerCamelCase_							=   model(A_  )
             self.assertIsNotNone(A_  )









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    :		List[Any]			   =       direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCamelCase    :		Optional[Any]			   =       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    :		Union[str, Any]			   =       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       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							):


      '''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(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)

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

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

      lowerCamelCase_							=   set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
      lowerCamelCase_							=   list(lowercase					)
      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(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''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(lowercase     ,							lowercase     ,							lowercase					):
                  # The type of pipeline may not exist in this framework
                  if not hasattr(lowercase     ,							lowercase					):
                        continue
                  # First extract all model_names
                  lowerCamelCase_							=   []
                  for name in getattr(lowercase     ,							lowercase					).values():
                        if isinstance(lowercase     ,							lowercase					):
                              model_names.append(lowercase					)
                        else:
                              model_names.extend(list(lowercase					)					)

            # 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       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      lowerCamelCase_							=   get_frameworks_table()
      lowerCamelCase_							=   Dataset.from_pandas(lowercase					)

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

      # 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(lowercase					)

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

def       _SCREAMING_SNAKE_CASE      (							):


      '''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(lowercase     ,							(list, tuple)					):
                        lowerCamelCase_							=   model[0]
                  lowerCamelCase_							=   model.__name__
                  if model not in in_table.values():
                        missing.append(lowercase					)

      if len(lowercase					) > 0:
            lowerCamelCase_							=   ', '.join(lowercase					)
            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    :		List[str]			   =       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    :		Optional[int]			   =       parser.parse_args()

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



