
"""simple docstring"""


import inspect
import re

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/check_config_docstrings.py
A_    							=					"""src/transformers"""


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

A_    							=					transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_    							=					re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


A_    							=					{
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  None

			# source code of `config_class`
			lowerCamelCase_					=  inspect.getsource(lowerCAmelCase__						)
			lowerCamelCase_					=  _re_checkpoint.findall(lowerCAmelCase__						)

			# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
			# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
			for ckpt_name, ckpt_link in checkpoints:
						# allow the link to end with `/`
						if ckpt_link.endswith('''/'''						):
									lowerCamelCase_					=  ckpt_link[:-1]

						# verify the checkpoint name corresponds to the checkpoint link
						lowerCamelCase_					=  f"https://huggingface.co/{ckpt_name}"
						if ckpt_link == ckpt_link_from_name:
									lowerCamelCase_					=  ckpt_name
									break

			return checkpoint






def  lowercase						(	):
			lowerCamelCase_					=  []

			for config_class in list(CONFIG_MAPPING.values()						):
						# Skip deprecated models
						if "models.deprecated" in config_class.__module__:
									continue
						lowerCamelCase_					=  get_checkpoint_from_config_class(lowerCAmelCase__						)

						lowerCamelCase_					=  config_class.__name__
						if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
									configs_without_checkpoint.append(lowerCAmelCase__						)

			if len(lowerCAmelCase__						) > 0:
						lowerCamelCase_					=  '''\n'''.join(sorted(lowerCAmelCase__						)						)
						raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"						)


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()





"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



