

import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import BertGenerationDecoder, BertGenerationEncoder






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case=13					,  __snake_case=7					,  __snake_case=True					,  __snake_case=True					,  __snake_case=99					,  __snake_case=32					,  __snake_case=5					,  __snake_case=4					,  __snake_case=37					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=50					,  __snake_case=0.02					,  __snake_case=True					,  __snake_case=None					,  )   -> Tuple:




						'''simple docstring'''
						__a        =parent
						__a        =batch_size
						__a        =seq_length
						__a        =is_training
						__a        =use_input_mask
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =use_labels
						__a        =scope





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

						__a        =None
						if self.use_input_mask:
									__a        =random_attention_mask([self.batch_size, self.seq_length]					)

						if self.use_labels:
									__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

						__a        =self.get_config()

						return config, input_ids, input_mask, token_labels





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						return BertGenerationConfig(
						    vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  is_decoder=__snake_case					,  initializer_range=self.initializer_range					,  )





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




						'''simple docstring'''
						(
						    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,     
						)        =self.prepare_config_and_inputs()

						__a        =True
						__a        =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						__a        =ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2					)

						return (
						    config,
						    input_ids,
						    input_mask,
						    token_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						__a        =BertGenerationEncoder(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					)
						__a        =model(__snake_case					)
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





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




						'''simple docstring'''
						__a        =True
						__a        =BertGenerationEncoder(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  )
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  )
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						__a        =True
						__a        =True
						__a        =BertGenerationDecoder(config=__snake_case					).to(__snake_case					).eval()

						# first forward pass
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  use_cache=__snake_case					,  )
						__a        =outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						__a        =ids_tensor((self.batch_size, 3)					,  config.vocab_size					)
						__a        =ids_tensor((self.batch_size, 3)					,  vocab_size=2					)

						# append to next input_ids and
						__a        =torch.cat([input_ids, next_tokens]					,  dim=-1					)
						__a        =torch.cat([input_mask, next_mask]					,  dim=-1					)

						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  output_hidden_states=__snake_case					,  )['hidden_states'][0]
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  past_key_values=__snake_case					,  output_hidden_states=__snake_case					,  )['hidden_states'][0]

						# select random slice
						__a        =ids_tensor((1,)					,  output_from_past.shape[-1]					).item()
						__a        =output_from_no_past[:, -3:, random_slice_idx].detach()
						__a        =output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(__snake_case					,  __snake_case					,  atol=1e-3					)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  *__snake_case					,  )   -> Any:




						'''simple docstring'''
						__a        =BertGenerationDecoder(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					,  labels=__snake_case					)
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a      ,    __a      ,    __a      ,    __a        =self.prepare_config_and_inputs()
						__a        ={'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict






@require_torch
class 	__magic_name__    (			snake_case__  ,    snake_case__  ,    snake_case__  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE							   =						(BertGenerationDecoder,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE							   =						(
			    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
			    if is_torch_available()
			    else {}
			)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =BertGenerationEncoderTester(self					)
						__a        =ConfigTester(self					,  config_class=__snake_case					,  hidden_size=37					)





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




						'''simple docstring'''
						self.config_tester.run_common_tests()





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a      ,    __a      ,    __a      ,    __a        =self.model_tester.prepare_config_and_inputs()
						__a        ='bert'
						self.model_tester.create_and_check_model(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*__snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						(
						    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,    (
						    __a 
						)      ,     
						)        =self.model_tester.prepare_config_and_inputs_for_decoder()

						__a        =None

						self.model_tester.create_and_check_model_as_decoder(
						    __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_for_causal_lm(*__snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'					)
						self.assertIsNotNone(__snake_case					)






@require_torch
class 	__magic_name__    (			unittest.TestCase      ):





			@slow
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'					)
						__a        =torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]					)
						with torch.no_grad():
									__a        =model(__snake_case					)[0]
						__a        =torch.Size([1, 8, 1024]					)
						self.assertEqual(output.shape					,  __snake_case					)
						__a        =torch.tensor(
						    [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]					)
						self.assertTrue(torch.allclose(output[:, :3, :3]					,  __snake_case					,  atol=1e-4					)					)






@require_torch
class 	__magic_name__    (			unittest.TestCase      ):





			@slow
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'					)
						__a        =torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]					)
						with torch.no_grad():
									__a        =model(__snake_case					)[0]
						__a        =torch.Size([1, 8, 5_0358]					)
						self.assertEqual(output.shape					,  __snake_case					)
						__a        =torch.tensor(
						    [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]					)
						self.assertTrue(torch.allclose(output[:, :3, :3]					,  __snake_case					,  atol=1e-4					)					)














import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "AutoTokenizer"
	UpperCamelCase__								=      ["tokenizer"]

	UpperCamelCase__								=      {
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase    )

							_UpperCAmelCase									=			speaker_embeddings

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							if speaker_embeddings_dict_path is not None:
													_UpperCAmelCase									=			get_file_from_repo(
													    UpperCAmelCase       ,    UpperCAmelCase       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if speaker_embeddings_path is None:
																			logger.warning(
																			    F"""`{os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""    )
																			_UpperCAmelCase									=			None
													else:
																			with open(UpperCAmelCase    ) as speaker_embeddings_json:
																									_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			None

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(UpperCAmelCase       ,    **UpperCAmelCase    )

							return cls(tokenizer=UpperCAmelCase       ,    speaker_embeddings=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    UpperCAmelCase="speaker_embeddings"       ,    UpperCAmelCase = False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(UpperCAmelCase       ,    UpperCAmelCase       ,    'v2'    )       ,    exist_ok=UpperCAmelCase    )

													_UpperCAmelCase									=			{}

													_UpperCAmelCase									=			save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

																									_UpperCAmelCase									=			{}
																									for key in self.speaker_embeddings[prompt_key]:
																															np.save(
																															    os.path.join(
																															        embeddings_dict['repo_or_path']       ,    UpperCAmelCase       ,    F"""{prompt_key}_{key}"""    )       ,    voice_preset[key]       ,    allow_pickle=UpperCAmelCase       ,    )
																															_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    F"""{prompt_key}_{key}.npy"""    )

																									_UpperCAmelCase									=			tmp_dict

													with open(os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )       ,    'w'    ) as fp:
																			json.dump(UpperCAmelCase       ,    UpperCAmelCase    )

							super().save_pretrained(UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.speaker_embeddings[voice_preset]

							_UpperCAmelCase									=			{}
							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																			raise ValueError(
																			    F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""    )

													_UpperCAmelCase									=			get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path'       ,    '/'    )       ,    voice_preset_paths[key]       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if path is None:
																			raise ValueError(
																			    F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"       ,    "/"    )       ,    voice_preset_paths[key]    )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""    )

													_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							return voice_preset_dict

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																			raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""    )

													if not isinstance(voice_preset[key]       ,    np.ndarray    ):
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

													if len(voice_preset[key].shape    ) != self.preset_shape[key]:
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase=None       ,    UpperCAmelCase="pt"       ,    UpperCAmelCase=256       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if voice_preset is not None and not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													if (
													    isinstance(UpperCAmelCase       ,    UpperCAmelCase    )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																			_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

													else:
																			if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) and not voice_preset.endswith('.npz'    ):
																									_UpperCAmelCase									=			voice_preset + '.npz'

																			_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							if voice_preset is not None:
													self._validate_voice_preset_dict(UpperCAmelCase       ,    **UpperCAmelCase    )
													_UpperCAmelCase									=			BatchFeature(data=UpperCAmelCase       ,    tensor_type=UpperCAmelCase    )

							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding='max_length'       ,    max_length=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

							if voice_preset is not None:
													_UpperCAmelCase									=			voice_preset

							return encoded_text
