






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   ['''image_processor''', '''tokenizer''']
						_SCREAMING_SNAKE_CASE		:    str						=   '''LayoutLMv2ImageProcessor'''
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






						def __init__(		self       ,  _UpperCamelCase=None       ,  _UpperCamelCase=None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'       ,  _UpperCamelCase       ,  )
																		lowerCAmelCase__        =  kwargs.pop('feature_extractor'				)

												lowerCAmelCase__        =  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'				)
												if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'				)

												super().__init__(_UpperCamelCase       ,  _UpperCamelCase				)






						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = False       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																		raise ValueError(
																		    'You cannot provide bounding boxes '
																		    'if you initialized the image processor with apply_ocr set to True.'				)

												if self.image_processor.apply_ocr and (word_labels is not None):
																		raise ValueError(
																		    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'				)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																		raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'				)

												# first, apply the image processor
												lowerCAmelCase__        =  self.image_processor(images=_UpperCamelCase       ,  return_tensors=_UpperCamelCase				)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(_UpperCamelCase       ,  _UpperCamelCase				):
																								lowerCAmelCase__        =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		lowerCAmelCase__        =  features['words']

												lowerCAmelCase__        =  self.tokenizer(
												    text=text if text is not None else features['words']       ,  text_pair=text_pair if text_pair is not None else None       ,  boxes=boxes if boxes is not None else features['boxes']       ,  word_labels=_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase       ,  padding=_UpperCamelCase       ,  truncation=_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  stride=_UpperCamelCase       ,  pad_to_multiple_of=_UpperCamelCase       ,  return_token_type_ids=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  return_overflowing_tokens=_UpperCamelCase       ,  return_special_tokens_mask=_UpperCamelCase       ,  return_offsets_mapping=_UpperCamelCase       ,  return_length=_UpperCamelCase       ,  verbose=_UpperCamelCase       ,  return_tensors=_UpperCamelCase       ,  **_UpperCamelCase       ,  )

												# add pixel values
												lowerCAmelCase__        =  features.pop('pixel_values'				)
												if return_overflowing_tokens is True:
																		lowerCAmelCase__        =  self.get_overflowing_images(_UpperCamelCase       ,  encoded_inputs['overflow_to_sample_mapping']				)
												lowerCAmelCase__        =  images

												return encoded_inputs






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												lowerCAmelCase__        =  []
												for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]				)

												if len(_UpperCamelCase				) != len(_UpperCamelCase				):
																		raise ValueError(
																		    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
																		    F" {len(_UpperCamelCase				)} and {len(_UpperCamelCase				)}"				)

												return images_with_overflow






						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.tokenizer.batch_decode(*_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.tokenizer.decode(*_UpperCamelCase       ,  **_UpperCamelCase				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return ["input_ids", "bbox", "attention_mask", "image"]






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												warnings.warn(
												    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,  _UpperCamelCase       ,  )
												return self.image_processor_class







						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												warnings.warn(
												    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,  _UpperCamelCase       ,  )
												return self.image_processor













# 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       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''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.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  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.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





