


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 tensorflow as tf

 from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Dict 						=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small')
           _lowercase			:				str 						=						AutoTokenizer.from_pretrained('google/mt5-small')

           _lowercase			:				Optional[int] 						=						tokenizer('Hello there', return_tensors='tf').input_ids
           _lowercase			:				List[str] 						=						tokenizer('Hi I am', return_tensors='tf').input_ids

           _lowercase			:				List[str] 						=						model(lowerCamelCase, labels=lowerCamelCase).loss
           _lowercase			:				Tuple 						=						-tf.math.reduce_mean(lowerCamelCase).numpy()

           _lowercase			:				Any 						=						-2_1.2_2_8_1_6_8
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2E-4)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			: str				   =					{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: int				   =					[
     "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTBigCodeForSequenceClassification",
     "GPTBigCodeForTokenClassification",
     "GPTBigCodeForCausalLM",
     "GPTBigCodeModel",
     "GPTBigCodePreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


