from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFTransfoXLForSequenceClassification,
							    TFTransfoXLLMHeadModel,
							    TFTransfoXLModel,
							)


class 						lowercase    :




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					List[Any]	,							)					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       13
												SCREAMING_SNAKE_CASE    			=       7
												SCREAMING_SNAKE_CASE    			=       30
												SCREAMING_SNAKE_CASE    			=       self.seq_length + self.mem_len
												SCREAMING_SNAKE_CASE    			=       15
												SCREAMING_SNAKE_CASE    			=       True
												SCREAMING_SNAKE_CASE    			=       True
												SCREAMING_SNAKE_CASE    			=       99
												SCREAMING_SNAKE_CASE    			=       [10, 50, 80]
												SCREAMING_SNAKE_CASE    			=       32
												SCREAMING_SNAKE_CASE    			=       32
												SCREAMING_SNAKE_CASE    			=       4
												SCREAMING_SNAKE_CASE    			=       8
												SCREAMING_SNAKE_CASE    			=       128
												SCREAMING_SNAKE_CASE    			=       2
												SCREAMING_SNAKE_CASE    			=       2
												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       1
												SCREAMING_SNAKE_CASE    			=       0
												SCREAMING_SNAKE_CASE    			=       3
												SCREAMING_SNAKE_CASE    			=       self.vocab_size - 1
												SCREAMING_SNAKE_CASE    			=       0.0_1




							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size      )
												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size      )

												SCREAMING_SNAKE_CASE    			=       TransfoXLConfig(
												    vocab_size=self.vocab_size	,							mem_len=self.mem_len	,							clamp_len=self.clamp_len	,							cutoffs=self.cutoffs	,							d_model=self.hidden_size	,							d_embed=self.d_embed	,							n_head=self.num_attention_heads	,							d_head=self.d_head	,							d_inner=self.d_inner	,							div_val=self.div_val	,							n_layer=self.num_hidden_layers	,							eos_token_id=self.eos_token_id	,							pad_token_id=self.vocab_size - 1	,							init_range=self.init_range	,							num_labels=self.num_labels	,							)

												return (config, input_ids_a, input_ids_a, lm_labels)




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												random.seed(self.seed      )
												tf.random.set_seed(self.seed      )




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFTransfoXLModel(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      ).to_tuple()

												SCREAMING_SNAKE_CASE    			=       {"input_ids": input_ids_a, "mems": mems_a}

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      ).to_tuple()

												self.parent.assertEqual(hidden_states_a.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )
												self.parent.assertEqual(hidden_states_a.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]	,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers	,							)
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]	,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers	,							)




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					int      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFTransfoXLLMHeadModel(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      ).to_tuple()

												SCREAMING_SNAKE_CASE    			=       {"input_ids": input_ids_a, "labels": lm_labels}
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      ).to_tuple()

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model([input_ids_a, mems_a]      ).to_tuple()

												SCREAMING_SNAKE_CASE    			=       {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      ).to_tuple()

												self.parent.assertEqual(lm_logits_a.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]	,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers	,							)

												self.parent.assertEqual(lm_logits_a.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]	,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers	,							)




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Tuple      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFTransfoXLForSequenceClassification(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )





							def 	__snake_case(						self   :					Tuple      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												((SCREAMING_SNAKE_CASE)			,   (SCREAMING_SNAKE_CASE)			,   (SCREAMING_SNAKE_CASE)			,   (SCREAMING_SNAKE_CASE))    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"input_ids": input_ids_a}
												return config, inputs_dict



@require_tf
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Union[str, Any]								=							(
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowercase__						:       str								=							() if is_tf_available() else ()
							lowercase__						:       Any								=							(
							    {
							        """feature-extraction""": TFTransfoXLModel,
							        """text-classification""": TFTransfoXLForSequenceClassification,
							        """text-generation""": TFTransfoXLLMHeadModel,
							        """zero-shot""": TFTransfoXLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
							lowercase__						:       Any								=							False
							lowercase__						:       List[str]								=							False
							lowercase__						:       Tuple								=							False
							lowercase__						:       Any								=							False




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												if pipeline_test_casse_name == "TextGenerationPipelineTests":
																	# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																	# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																	# tokenizer.
																	return True

												return False




							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFTransfoXLModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							d_embed=37      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 	__snake_case(						self   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												self.model_tester.set_seed()
												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												self.model_tester.set_seed()
												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE    			=       [TFTransfoXLForSequenceClassification]

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	assert isinstance(model.get_input_embeddings()	,							tf.keras.layers.Layer      )
																	if model_class in list_other_models_with_output_ebd:
																						SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																						assert isinstance(_UpperCamelCase	,							tf.keras.layers.Layer      )
																						SCREAMING_SNAKE_CASE    			=       model.get_bias()
																						assert name is None
																	else:
																						SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																						assert x is None
																						SCREAMING_SNAKE_CASE    			=       model.get_bias()
																						assert name is None




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												pass




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



												'''simple docstring'''

												for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       TFTransfoXLModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )





							@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."      )
							def 	__snake_case(						self   :					List[Any]      )					->				List[Any]:



												'''simple docstring'''

												pass



@require_tf
class 						lowercase    (     unittest.TestCase     ):




							@unittest.skip("Skip test until #12651 is resolved."      )
							@slow
							def 	__snake_case(						self   :					Any      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"      )
												# fmt: off
												SCREAMING_SNAKE_CASE    			=       tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]	,							dtype=tf.intaa      )  # noqa: E231
												# fmt: on
												#  In 1991 , the remains of Russian Tsar Nicholas II and his family
												#  ( except for Alexei and Maria ) are discovered .
												#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
												#  remainder of the story . 1883 Western Siberia ,
												#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
												#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
												#  father initially slaps him for making such an accusation , Rasputin watches as the
												#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
												#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
												#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

												# fmt: off
												SCREAMING_SNAKE_CASE    			=       [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
												# fmt: on
												#  In 1991, the remains of Russian Tsar Nicholas II and his family (
												#  except for Alexei and Maria ) are discovered. The voice of young son,
												#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
												#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
												#  and a group of men to perform magic. Rasputin has a vision and
												#  denounces one of the men as a horse thief. Although his father initially
												#  slaps him for making such an accusation, Rasputin watches as the man
												#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
												#  of the Virgin Mary, prompting him to become a priest.
												#  Rasputin quickly becomes famous, with people, even a bishop, begging for
												#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
												# Nicholas II and his family were discovered. The voice of <unk> young son,
												# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

												SCREAMING_SNAKE_CASE    			=       model.generate(_UpperCamelCase	,							max_length=200	,							do_sample=_UpperCamelCase      )
												self.assertListEqual(output_ids[0].numpy().tolist()	,							_UpperCamelCase      )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase					:   Optional[int]							=		{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[Any]							=		['''BlenderbotTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Tuple							=		[
							    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BlenderbotForCausalLM''',
							    '''BlenderbotForConditionalGeneration''',
							    '''BlenderbotModel''',
							    '''BlenderbotPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[str]							=		[
							    '''TFBlenderbotForConditionalGeneration''',
							    '''TFBlenderbotModel''',
							    '''TFBlenderbotPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		[
							    '''FlaxBlenderbotForConditionalGeneration''',
							    '''FlaxBlenderbotModel''',
							    '''FlaxBlenderbotPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							_lowerCamelCase					:   Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




