


import os
import sys

A	:		Dict    =       os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A	:		List[Any]    =       [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							int   ,   **__magic_name__					:							List[Any]					)       ->  str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__   ,   **__magic_name__					)






@add_start_docstrings(AutoTokenizer.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							str   ,   **__magic_name__					:							Tuple					)       ->  Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__   ,   **__magic_name__					)






@add_start_docstrings(AutoModel.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							Dict   ,   **__magic_name__					:							int					)       ->  Union[str, Any]:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__   ,   **__magic_name__					)






@add_start_docstrings(AutoModelForCausalLM.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							str   ,   **__magic_name__					:							Dict					)       ->  Any:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__   ,   **__magic_name__					)






@add_start_docstrings(AutoModelForMaskedLM.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							Any   ,   **__magic_name__					:							Optional[int]					)       ->  Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__   ,   **__magic_name__					)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							Tuple   ,   **__magic_name__					:							Union[str, Any]					)       ->  Tuple:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__   ,   **__magic_name__					)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__					)
def 			UpperCamelCase (     *__magic_name__					:							int   ,   **__magic_name__					:							Union[str, Any]					)       ->  Optional[int]:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__   ,   **__magic_name__					)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

