





"""simple docstring"""



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
       import torch


a_ =			logging.get_logger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     ["""pixel_values"""]

 def __init__(    self					,						__lowerCamelCase = True					,						__lowerCamelCase = None					,						__lowerCamelCase = PILImageResampling.BILINEAR					,						__lowerCamelCase = True					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						__lowerCamelCase = 1 / 255					,						__lowerCamelCase = True					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )
     __A    :						int						 =     size if size is not None else {'''shortest_edge''': 256}
     __A    :						List[Any]						 =     get_size_dict(__lowerCamelCase					,						default_to_square=__lowerCamelCase  )
     __A    :						List[Any]						 =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A    :						Tuple						 =     get_size_dict(__lowerCamelCase					,						param_name='''crop_size'''  )
     __A    :						Optional[Any]						 =     do_resize
     __A    :						Union[str, Any]						 =     size
     __A    :						str						 =     resample
     __A    :						Optional[Any]						 =     do_center_crop
     __A    :						int						 =     crop_size
     __A    :						Optional[Any]						 =     do_rescale
     __A    :						Union[str, Any]						 =     rescale_factor
     __A    :						Tuple						 =     do_normalize
     __A    :						Union[str, Any]						 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __A    :						int						 =     image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = PILImageResampling.BICUBIC					,						__lowerCamelCase = None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						str						 =     get_size_dict(__lowerCamelCase					,						default_to_square=__lowerCamelCase  )
     if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
     __A    :						Any						 =     get_resize_output_image_size(__lowerCamelCase					,						size=size['''shortest_edge''']					,						default_to_square=__lowerCamelCase  )
     return resize(__lowerCamelCase					,						size=__lowerCamelCase					,						resample=__lowerCamelCase					,						data_format=__lowerCamelCase					,						**__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     get_size_dict(__lowerCamelCase  )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""  )
     return center_crop(__lowerCamelCase					,						size=(size['''height'''], size['''width'''])					,						data_format=__lowerCamelCase					,						**__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						**__lowerCamelCase  ):



     '''simple docstring'''




     return rescale(__lowerCamelCase					,						scale=__lowerCamelCase					,						data_format=__lowerCamelCase					,						**__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     return normalize(__lowerCamelCase					,						mean=__lowerCamelCase					,						std=__lowerCamelCase					,						data_format=__lowerCamelCase					,						**__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = ChannelDimension.FIRST					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Any						 =     do_resize if do_resize is not None else self.do_resize
     __A    :						str						 =     size if size is not None else self.size
     __A    :						int						 =     get_size_dict(__lowerCamelCase					,						default_to_square=__lowerCamelCase  )
     __A    :						int						 =     resample if resample is not None else self.resample
     __A    :						str						 =     do_center_crop if do_center_crop is not None else self.do_center_crop
     __A    :						Tuple						 =     crop_size if crop_size is not None else self.crop_size
     __A    :						Optional[Any]						 =     get_size_dict(__lowerCamelCase					,						param_name='''crop_size'''  )
     __A    :						Dict						 =     do_rescale if do_rescale is not None else self.do_rescale
     __A    :						List[Any]						 =     rescale_factor if rescale_factor is not None else self.rescale_factor
     __A    :						Tuple						 =     do_normalize if do_normalize is not None else self.do_normalize
     __A    :						List[str]						 =     image_mean if image_mean is not None else self.image_mean
     __A    :						str						 =     image_std if image_std is not None else self.image_std

     __A    :						Optional[Any]						 =     make_list_of_images(__lowerCamelCase  )

     if not valid_images(__lowerCamelCase  ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

     if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.'''  )

     if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

     if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

     # All transformations expect numpy arrays.
     __A    :						Optional[int]						 =     [to_numpy_array(__lowerCamelCase  ) for image in images]

     if do_resize:
         __A    :						Dict						 =     [self.resize(image=__lowerCamelCase					,						size=__lowerCamelCase					,						resample=__lowerCamelCase  ) for image in images]

     if do_center_crop:
         __A    :						List[str]						 =     [self.center_crop(image=__lowerCamelCase					,						size=__lowerCamelCase  ) for image in images]

     if do_rescale:
         __A    :						Union[str, Any]						 =     [self.rescale(image=__lowerCamelCase					,						scale=__lowerCamelCase  ) for image in images]

     if do_normalize:
         __A    :						Optional[Any]						 =     [self.normalize(image=__lowerCamelCase					,						mean=__lowerCamelCase					,						std=__lowerCamelCase  ) for image in images]

     __A    :						List[Any]						 =     [to_channel_dimension_format(__lowerCamelCase					,						__lowerCamelCase  ) for image in images]

     __A    :						List[str]						 =     {'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase					,						tensor_type=__lowerCamelCase  )







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



     '''simple docstring'''




     __A    :						Optional[Any]						 =     outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
             raise ValueError(
                 '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

         if is_torch_tensor(__lowerCamelCase  ):
             __A    :						Optional[int]						 =     target_sizes.numpy()

         __A    :						Tuple						 =     []

         for idx in range(len(__lowerCamelCase  )  ):
             __A    :						Tuple						 =     torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0  )					,						size=target_sizes[idx]					,						mode='''bilinear'''					,						align_corners=__lowerCamelCase  )
             __A    :						str						 =     resized_logits[0].argmax(dim=0  )
             semantic_segmentation.append(__lowerCamelCase  )
     else:
         __A    :						List[Any]						 =     logits.argmax(dim=1  )
         __A    :						List[str]						 =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

     return semantic_segmentation











"""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 		__snake_case					(		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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

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



     '''simple docstring'''




     __A    :						int						 =     self.pre_processor(text=__lowerCamelCase					,						return_tensors='''pt'''					,						truncation=__lowerCamelCase  )

     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    :						int						 =     torch.tensor(embeddings_dataset[7305]['''xvector''']  ).unsqueeze(0  )

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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




