



"""simple docstring"""







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       _a  :



			def __init__(			self							:				Optional[Any]   ,	_lowercase							:				int   ,	)							->					Tuple:
									snake_case   :			str						    = parent
									snake_case   :			List[str]						    = 13
									snake_case   :			Any						    = 7
									snake_case   :			Tuple						    = 30
									snake_case   :			List[str]						    = self.seq_length + self.mem_len
									snake_case   :			Optional[Any]						    = 15
									snake_case   :			List[Any]						    = True
									snake_case   :			List[Any]						    = True
									snake_case   :			Optional[int]						    = 99
									snake_case   :			int						    = [10, 50, 80]
									snake_case   :			Optional[int]						    = 32
									snake_case   :			Optional[int]						    = 32
									snake_case   :			str						    = 4
									snake_case   :			str						    = 8
									snake_case   :			Optional[Any]						    = 128
									snake_case   :			Optional[Any]						    = 2
									snake_case   :			Tuple						    = 2
									snake_case   :			str						    = None
									snake_case   :			Tuple						    = 1
									snake_case   :			Optional[int]						    = 0
									snake_case   :			Optional[Any]						    = 3
									snake_case   :			List[str]						    = self.vocab_size - 1
									snake_case   :			str						    = 0.01



			def 				__lowercase					(			self							:				Dict		)							->					Optional[Any]:
									snake_case   :			int						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)
									snake_case   :			int						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case   :			Any						    = None
									if self.use_labels:
															snake_case   :			List[str]						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case   :			Any						    = 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 				__lowercase					(			self							:				str		)							->					Optional[Any]:
									random.seed(self.seed		)
									tf.random.set_seed(self.seed		)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				str   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				int		)							->					Dict:
									snake_case   :			Optional[Any]						    = TFTransfoXLModel(A_		)

									snake_case   :			Dict						    = model(A_		).to_tuple()

									snake_case   :			str						    = {'''input_ids''': input_ids_a, '''mems''': mems_a}

									snake_case   :			Any						    = model(A_		).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 				__lowercase					(			self							:				Any   ,	_lowercase							:				Tuple   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				str   ,	_lowercase							:				str		)							->					Union[str, Any]:
									snake_case   :			int						    = TFTransfoXLLMHeadModel(A_		)

									snake_case   :			Optional[int]						    = model(A_		).to_tuple()

									snake_case   :			Optional[Any]						    = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
									snake_case   :			int						    = model(A_		).to_tuple()

									snake_case   :			Dict						    = model([input_ids_a, mems_a]		).to_tuple()

									snake_case   :			Union[str, Any]						    = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

									snake_case   :			Dict						    = model(A_		).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 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Dict		)							->					str:
									snake_case   :			List[Any]						    = TFTransfoXLForSequenceClassification(A_		)
									snake_case   :			int						    = model(A_		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)



			def 				__lowercase					(			self							:				Dict		)							->					Optional[int]:
									snake_case   :			Any						    = self.prepare_config_and_inputs()
									(snake_case)   :			Any						    = config_and_inputs
									snake_case   :			Union[str, Any]						    = {'''input_ids''': input_ids_a}
									return config, inputs_dict



@require_tf
class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     (
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			__magic_name__     					=     () if is_tf_available() else ()
			__magic_name__     					=     (
			    {
			        """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
			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Any   ,	_lowercase							:				Any   ,	_lowercase							:				int		)							->					Union[str, Any]:




									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 				__lowercase					(			self							:				Tuple		)							->					Optional[Any]:
									snake_case   :			List[Any]						    = TFTransfoXLModelTester(self		)
									snake_case   :			List[Any]						    = ConfigTester(self   ,	config_class=A_   ,	d_embed=37		)



			def 				__lowercase					(			self							:				int		)							->					str:
									self.config_tester.run_common_tests()



			def 				__lowercase					(			self							:				str		)							->					Tuple:
									self.model_tester.set_seed()
									snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*A_		)



			def 				__lowercase					(			self							:				Tuple		)							->					List[str]:
									self.model_tester.set_seed()
									snake_case   :			Tuple						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*A_		)



			def 				__lowercase					(			self							:				int		)							->					Optional[int]:
									snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A_		)



			def 				__lowercase					(			self							:				int		)							->					int:
									snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs_for_common()
									snake_case   :			List[str]						    = [TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
															snake_case   :			Union[str, Any]						    = model_class(A_		)
															assert isinstance(model.get_input_embeddings()   ,	tf.keras.layers.Layer		)
															if model_class in list_other_models_with_output_ebd:
																					snake_case   :			Tuple						    = model.get_output_embeddings()
																					assert isinstance(A_   ,	tf.keras.layers.Layer		)
																					snake_case   :			List[str]						    = model.get_bias()
																					assert name is None
															else:
																					snake_case   :			Optional[int]						    = model.get_output_embeddings()
																					assert x is None
																					snake_case   :			List[str]						    = model.get_bias()
																					assert name is None



			def 				__lowercase					(			self							:				Tuple		)							->					List[str]:
									pass



			@slow
			def 				__lowercase					(			self							:				List[Any]		)							->					List[str]:




									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case   :			Union[str, Any]						    = TFTransfoXLModel.from_pretrained(A_		)
															self.assertIsNotNone(A_		)



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



@require_tf
class       _a  ( unittest.TestCase):



			@unittest.skip("Skip test until #12651 is resolved."		)
			@slow
			def 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			List[str]						    = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"		)
									# fmt: off
									snake_case   :			Union[str, Any]						    = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]   ,	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
									snake_case   :			List[str]						    = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

									snake_case   :			List[Any]						    = model.generate(A_   ,	max_length=200   ,	do_sample=A_		)
									self.assertListEqual(output_ids[0].numpy().tolist()   ,	A_		)





"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
           '''text to read (in English) and returns a waveform object containing the sound.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.pre_processor(text=A_			,					return_tensors='''pt'''			,					truncation=A_							)

         if speaker_embeddings is None:
           if not is_datasets_available():
             raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''							)

           _lowercase:      Union[str, Any]   			=  load_dataset('''Matthijs/cmu-arctic-xvectors'''			,					split='''validation'''							)
           _lowercase:      Optional[int]   			=  torch.tensor(embeddings_dataset[7305]['''xvector''']							).unsqueeze(0							)

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         with torch.no_grad():
           return self.model.generate_speech(**A_							)



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         with torch.no_grad():
           return self.post_processor(A_							).cpu().detach()
