






from typing import TYPE_CHECKING

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


lowerCamelCase       :       Dict        =				{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase       :       str        =				[
	    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AdaptiveEmbedding',
	    'TransfoXLForSequenceClassification',
	    'TransfoXLLMHeadModel',
	    'TransfoXLModel',
	    'TransfoXLPreTrainedModel',
	    'load_tf_weights_in_transfo_xl',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase       :       Tuple        =				[
	    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAdaptiveEmbedding',
	    'TFTransfoXLForSequenceClassification',
	    'TFTransfoXLLMHeadModel',
	    'TFTransfoXLMainLayer',
	    'TFTransfoXLModel',
	    'TFTransfoXLPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
	from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_transfo_xl import (
		    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AdaptiveEmbedding,
		    TransfoXLForSequenceClassification,
		    TransfoXLLMHeadModel,
		    TransfoXLModel,
		    TransfoXLPreTrainedModel,
		    load_tf_weights_in_transfo_xl,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_transfo_xl import (
		    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAdaptiveEmbedding,
		    TFTransfoXLForSequenceClassification,
		    TFTransfoXLLMHeadModel,
		    TFTransfoXLMainLayer,
		    TFTransfoXLModel,
		    TFTransfoXLPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase       :       Union[str, Any]        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





