'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowercase		:				Optional[int]	       =		{
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LongT5EncoderModel",
						    "LongT5ForConditionalGeneration",
						    "LongT5Model",
						    "LongT5PreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Tuple	       =		[
						    "FlaxLongT5ForConditionalGeneration",
						    "FlaxLongT5Model",
						    "FlaxLongT5PreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longta import (
												    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongTaEncoderModel,
												    LongTaForConditionalGeneration,
												    LongTaModel,
												    LongTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_longta import (
												    FlaxLongTaForConditionalGeneration,
												    FlaxLongTaModel,
												    FlaxLongTaPreTrainedModel,
												)


else:
						import sys

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






'''simple docstring'''




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 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





