



import os
import sys

lowerCAmelCase__					  =  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,
)


lowerCAmelCase__					  =  [
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]


@add_start_docstrings(AutoConfig.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoTokenizer.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoModel.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoModelForCausalLM.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoModelForMaskedLM.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoModelForSequenceClassification.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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


@add_start_docstrings(AutoModelForQuestionAnswering.__doc__						)
def  __lowerCamelCase (       *lowerCamelCase__					,			**lowerCamelCase__						):

				"""simple docstring"""


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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
									lowercase__					:   Dict													=					AutoTokenizer.from_pretrained("google/mt5-small"					)

									lowercase__					:   Optional[Any]													=					tokenizer("Hello there"				,       return_tensors="np"					).input_ids
									lowercase__					:   Optional[Any]													=					tokenizer("Hi I am"				,       return_tensors="np"					).input_ids

									lowercase__					:   int													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       model.config.pad_token_id				,       model.config.decoder_start_token_id					)

									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					).logits
									lowercase__					:   Dict													=					optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE				,       onehot(SCREAMING_SNAKE_CASE				,       logits.shape[-1]					)					).mean()

									lowercase__					:   Union[str, Any]													=					-(labels.shape[-1] * loss.item())

									lowercase__					:   Union[str, Any]													=					-84.9_127
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




