






'''simple docstring'''





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch






def    lowerCamelCase_     (    lowercase__):  # picklable for multiprocessing
 return x.sum()






def    lowerCamelCase_     (    lowercase__):  # picklable for multiprocessing
 return i + 1
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							42
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




     lowerCamelCase__  =     {}
     lowerCamelCase__  =     []
     lowerCamelCase__  =     1
     lowerCamelCase__  =     [1, 2]
     lowerCamelCase__  =     {"a": 1, "b": 2}
     lowerCamelCase__  =     {"a": [1, 2], "b": [3, 4]}
     lowerCamelCase__  =     {"a": {"1": 1}, "b": 2}
     lowerCamelCase__  =     {"a": 1, "b": 2, "c": 3, "d": 4}
     lowerCamelCase__  =     {}
     lowerCamelCase__  =     []
     lowerCamelCase__  =     2
     lowerCamelCase__  =     [2, 3]
     lowerCamelCase__  =     {"a": 2, "b": 3}
     lowerCamelCase__  =     {"a": [2, 3], "b": [4, 5]}
     lowerCamelCase__  =     {"a": {"1": 2}, "b": 3}
     lowerCamelCase__  =     {"a": 2, "b": 3, "c": 4, "d": 5}
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase     )				,     __lowerCamelCase     )

     lowerCamelCase__  =     2
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )

     lowerCamelCase__  =     {"a": np.eye(2     ), "b": np.zeros(3     ), "c": np.ones(2     )}
     lowerCamelCase__  =     {"a": 2, "b": 0, "c": 2}
     lowerCamelCase__  =     {
         "a": np.eye(2     ).astype(__lowerCamelCase     ),
         "b": np.zeros(3     ).astype(__lowerCamelCase     ),
         "c": np.ones(2     ).astype(__lowerCamelCase     ),
     }
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     map_numpy=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(
         {k: v.tolist() for k, v in map_nested(__lowerCamelCase				,     __lowerCamelCase				,     map_numpy=__lowerCamelCase     ).items()}				,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,     )
     self.assertEqual(map_nested(__lowerCamelCase				,     __lowerCamelCase				,     map_numpy=__lowerCamelCase				,     num_proc=__lowerCamelCase     )				,     __lowerCamelCase     )
     self.assertEqual(
         {k: v.tolist() for k, v in map_nested(__lowerCamelCase				,     __lowerCamelCase				,     map_numpy=__lowerCamelCase				,     num_proc=__lowerCamelCase     ).items()}				,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,     )
     with self.assertRaises(__lowerCamelCase     ):  # can't pickle a local lambda
      map_nested(lambda __lowerCamelCase     : x + 1				,     __lowerCamelCase				,     num_proc=__lowerCamelCase     )

    def    a__				(					self     :     str     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     {"a": 1, "b": 2}
     lowerCamelCase__  =     {"a": 3, "b": 4}
     lowerCamelCase__  =     {"a": 5, "b": 6}
     lowerCamelCase__  =     sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]     )
     self.assertEqual(sorted(zip_dict(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )     )				,     __lowerCamelCase     )




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

     '''simple docstring'''




     class 				lowercase       :


         '''simple docstring'''

         lowerCAmelCase__      				=							"bar"
     lowerCamelCase__  =     Foo()
     self.assertEqual(foo.my_attr				,     "bar"     )
     with temporary_assignment(__lowerCamelCase				,     "my_attr"				,     "BAR"     ):
      self.assertEqual(foo.my_attr				,     "BAR"     )
     self.assertEqual(foo.my_attr				,     "bar"     )






@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"				,     [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]				,     )
def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 with patch("datasets.utils.py_utils._single_map_nested") as mock_single_map_nested, patch(
     "datasets.parallel.parallel.Pool") as mock_multiprocessing_pool:
  lowerCamelCase__  =     {F'''{i}''': i for i in range(lowercase__)}
  lowerCamelCase__  =     map_nested(lambda lowercase__: x + 10				,     lowercase__				,     num_proc=lowercase__				,     parallel_min_length=16)
  if expected_num_proc == 1:
   assert mock_single_map_nested.called
   assert not mock_multiprocessing_pool.called
  else:
   assert not mock_single_map_nested.called
   assert mock_multiprocessing_pool.called
   assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @require_tf
    def    a__				(					self     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     import tensorflow as tf
     from tensorflow.keras import layers

     lowerCamelCase__  =     layers.Dense(2     )

     def gen_random_output():
      lowerCamelCase__  =     tf.random.uniform((1, 3)     )
      return model(__lowerCamelCase     ).numpy()

     with temp_seed(42				,     set_tensorflow=__lowerCamelCase     ):
      lowerCamelCase__  =     gen_random_output()
     with temp_seed(42				,     set_tensorflow=__lowerCamelCase     ):
      lowerCamelCase__  =     gen_random_output()
     lowerCamelCase__  =     gen_random_output()

     np.testing.assert_equal(__lowerCamelCase				,     __lowerCamelCase     )
     self.assertGreater(np.abs(outa - outa     ).sum()				,     0     )

    @require_torch
    def    a__				(					self     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     import torch

     def gen_random_output():
      lowerCamelCase__  =     torch.nn.Linear(3				,     2     )
      lowerCamelCase__  =     torch.rand(1				,     3     )
      return model(__lowerCamelCase     ).detach().numpy()

     with temp_seed(42				,     set_pytorch=__lowerCamelCase     ):
      lowerCamelCase__  =     gen_random_output()
     with temp_seed(42				,     set_pytorch=__lowerCamelCase     ):
      lowerCamelCase__  =     gen_random_output()
     lowerCamelCase__  =     gen_random_output()

     np.testing.assert_equal(__lowerCamelCase				,     __lowerCamelCase     )
     self.assertGreater(np.abs(outa - outa     ).sum()				,     0     )




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

     '''simple docstring'''




     def gen_random_output():
      return np.random.rand(1				,     3     )

     with temp_seed(42     ):
      lowerCamelCase__  =     gen_random_output()
     with temp_seed(42     ):
      lowerCamelCase__  =     gen_random_output()
     lowerCamelCase__  =     gen_random_output()

     np.testing.assert_equal(__lowerCamelCase				,     __lowerCamelCase     )
     self.assertGreater(np.abs(outa - outa     ).sum()				,     0     )






@pytest.mark.parametrize("input_data"				,     [{}])
def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     NestedDataStructure(lowercase__).data
 assert output_data == input_data






@pytest.mark.parametrize(
    "data, expected_output"				,     [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]				,     )
def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     NestedDataStructure(lowercase__).flatten()
 assert output == expected_output






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     A(x=1				,     y="foobar")
 lowerCamelCase__  =     {"x": 1, "y": "foobar"}
 assert asdict(lowercase__) == expected_output

 lowerCamelCase__  =     {"a": {"b": A(x=10				,     y="foo")}, "c": [A(x=20				,     y="bar")]}
 lowerCamelCase__  =     {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
 assert asdict(lowercase__) == expected_output

 with pytest.raises(lowercase__):
  asdict([1, A(x=10				,     y="foo")])






def    lowerCamelCase_     (    lowercase__):
 return text.split()






def    lowerCamelCase_     (    lowercase__):
 yield (time.time(), content)
 time.sleep(2)
 yield (time.time(), content)






def    lowerCamelCase_     (    ):
 with Pool(2) as pool:
  lowerCamelCase__  =     list(iflatmap_unordered(lowercase__				,     _split_text				,     kwargs_iterable=[{"text": "hello there"}] * 10))
  assert out.count("hello") == 10
  assert out.count("there") == 10
  assert len(lowercase__) == 20

 # check multiprocess from pathos (uses dill for pickling)
 with multiprocess.Pool(2) as pool:
  lowerCamelCase__  =     list(iflatmap_unordered(lowercase__				,     _split_text				,     kwargs_iterable=[{"text": "hello there"}] * 10))
  assert out.count("hello") == 10
  assert out.count("there") == 10
  assert len(lowercase__) == 20

 # check that we get items as fast as possible
 with Pool(2) as pool:
  lowerCamelCase__  =     []
  for yield_time, content in iflatmap_unordered(
      lowercase__				,     _aseconds_generator_of_aitems_with_timing				,     kwargs_iterable=[{"content": "a"}, {"content": "b"}]):
   assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
   out.append(lowercase__)
  assert out.count("a") == 2
  assert out.count("b") == 2
  assert len(lowercase__) == 4









'''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
__A							:							List[Any]  							=		"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
__A							:							List[Any]  							=		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__A							:							Union[str, 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.
__A							:							int  							=		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def    lowerCamelCase_     (    ):
 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    lowerCamelCase_     (    lowercase__):
 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    lowerCamelCase_     (    lowercase__				,     lowercase__):
 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    lowerCamelCase_     (    ):
 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__":
 __A							:							Any  							=		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.""")
 __A							:							Dict  							=		parser.parse_args()

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

