




import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


_SCREAMING_SNAKE_CASE    :      List[Any]          =			logging.get_logger(__name__)







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__=None				,       UpperCamelCase__=None		):
					if not conversation_id:
									A__  :	Any         =							uuid.uuida()
					if past_user_inputs is None:
									A__  :	List[str]         =							[]
					if generated_responses is None:
									A__  :	Union[str, Any]         =							[]

					A__  :	uuid.UUID         =							conversation_id
					A__  :	List[str]         =							past_user_inputs
					A__  :	List[str]         =							generated_responses
					A__  :	Optional[str]         =							text





	def __eq__(			self				,       UpperCamelCase__		):
					if not isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									return False
					if self.uuid == other.uuid:
									return True
					return (
					    self.new_user_input == other.new_user_input
					    and self.past_user_inputs == other.past_user_inputs
					    and self.generated_responses == other.generated_responses
					)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = False		):
					if self.new_user_input:
									if overwrite:
													logger.warning(
													    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
													    F"with: \"{text}\"."		)
													A__  :	Dict         =							text
									else:
													logger.warning(
													    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
													    F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"		)
					else:
									A__  :	Optional[int]         =							text





	def   __snake_case				(			self		):
					if self.new_user_input:
									self.past_user_inputs.append(self.new_user_input		)
					A__  :	List[str]         =							None





	def   __snake_case				(			self				,       UpperCamelCase__		):
					self.generated_responses.append(UpperCamelCase__		)





	def   __snake_case				(			self		):
					for user_input, generated_response in zip(self.past_user_inputs				,       self.generated_responses		):
									yield True, user_input
									yield False, generated_response
					if self.new_user_input:
									yield True, self.new_user_input





	def __repr__(			self		):
					A__  :	Tuple         =							F"Conversation id: {self.uuid} \n"
					for is_user, text in self.iter_texts():
									A__  :	str         =							'''user''' if is_user else '''bot'''
									output += F"{name} >> {text} \n"
					return output







@add_end_docstrings(
    SCREAMING_SNAKE_CASE_,			R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    ",			)
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					super().__init__(*UpperCamelCase__				,       **UpperCamelCase__		)
					if self.tokenizer.pad_token_id is None:
									A__  :	Tuple         =							self.tokenizer.eos_token





	def   __snake_case				(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       **UpperCamelCase__		):
					A__  :	str         =							{}
					A__  :	Optional[int]         =							{}
					A__  :	Optional[int]         =							{}

					if min_length_for_response is not None:
									A__  :	Dict         =							min_length_for_response
					if minimum_tokens is not None:
									A__  :	Union[str, Any]         =							minimum_tokens

					if "max_length" in generate_kwargs:
									A__  :	Union[str, Any]         =							generate_kwargs['''max_length''']
									# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
					if clean_up_tokenization_spaces is not None:
									A__  :	Optional[Any]         =							clean_up_tokenization_spaces

					if generate_kwargs:
									forward_params.update(UpperCamelCase__		)
					return preprocess_params, forward_params, postprocess_params





	def __call__(			self				,       UpperCamelCase__				,       UpperCamelCase__=0				,       **UpperCamelCase__		):
					A__  :	List[str]         =							super().__call__(UpperCamelCase__				,       num_workers=UpperCamelCase__				,       **UpperCamelCase__		)
					if isinstance(UpperCamelCase__				,       UpperCamelCase__		) and len(UpperCamelCase__		) == 1:
									return outputs[0]
					return outputs





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=32		):
					if not isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''		)
					if conversation.new_user_input is None:
									raise ValueError(
									    F"Conversation with UUID {type(conversation.uuid		)} does not contain new user input to process. "
									    '''Add user inputs with the conversation\'s `add_user_input` method'''		)
					if hasattr(self.tokenizer				,       '''_build_conversation_input_ids'''		):
									A__  :	List[Any]         =							self.tokenizer._build_conversation_input_ids(UpperCamelCase__		)
					else:
									# If the tokenizer cannot handle conversations, we default to only the old version
									A__  :	Any         =							self._legacy_parse_and_tokenize(UpperCamelCase__		)

					if self.framework == "pt":
									A__  :	Optional[Any]         =							torch.LongTensor([input_ids]		)
					elif self.framework == "tf":
									A__  :	Optional[Any]         =							tf.constant([input_ids]		)
					return {"input_ids": input_ids, "conversation": conversation}





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=10				,       **UpperCamelCase__		):
					A__  :	str         =							generate_kwargs.get('''max_length'''				,       self.model.config.max_length		)

					A__  :	List[Any]         =							model_inputs['''input_ids'''].shape[1]
					if max_length - minimum_tokens < n:
									logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"		)
									A__  :	Any         =							max_length - minimum_tokens
									A__  :	List[Any]         =							model_inputs['''input_ids'''][:, -trim:]
									if "attention_mask" in model_inputs:
													A__  :	List[str]         =							model_inputs['''attention_mask'''][:, -trim:]
					A__  :	List[str]         =							model_inputs.pop('''conversation'''		)
					A__  :	List[str]         =							max_length
					A__  :	Any         =							self.model.generate(**UpperCamelCase__				,       **UpperCamelCase__		)
					if self.model.config.is_encoder_decoder:
									A__  :	Any         =							1
					else:
									A__  :	List[str]         =							n
					return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=True		):
					A__  :	Dict         =							model_outputs['''output_ids''']
					A__  :	List[Any]         =							self.tokenizer.decode(
					    output_ids[0]				,       skip_special_tokens=UpperCamelCase__				,       clean_up_tokenization_spaces=UpperCamelCase__				,       )
					A__  :	Union[str, Any]         =							model_outputs['''conversation''']
					conversation.mark_processed()
					conversation.append_response(UpperCamelCase__		)
					return conversation





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							self.tokenizer.eos_token_id
					A__  :	str         =							[]
					for is_user, text in conversation.iter_texts():
									if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [eos_token_id]		)
									else:
													input_ids.extend(self.tokenizer.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		)		)

					if len(UpperCamelCase__		) > self.tokenizer.model_max_length:
									A__  :	Dict         =							input_ids[-self.tokenizer.model_max_length :]
					return input_ids

# 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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "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."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





	def   __snake_case				(			self		):
					if self.post_processor is None:
									A__  :	int         =							'''microsoft/speecht5_hifigan'''
					super().setup()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	List[Any]         =							self.pre_processor(text=UpperCamelCase__				,       return_tensors='''pt'''				,       truncation=UpperCamelCase__		)

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

									A__  :	List[Any]         =							load_dataset('''Matthijs/cmu-arctic-xvectors'''				,       split='''validation'''		)
									A__  :	Dict         =							torch.tensor(embeddings_dataset[7305]['''xvector''']		).unsqueeze(0		)

					return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()