






'''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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


lowerCamelCase_				: int    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							str					     =					["pixel_values"]






			def __init__(   self							:						Union[str, Any] ,      lowercase							:						bool = True ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						PILImageResampling = PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      lowercase							:						Union[int, float] = 1 / 2_5_5 ,      lowercase							:						bool = True ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									super().__init__(**lowercase			)
									UpperCamelCase__         = size if size is not None else {"""shortest_edge""": 2_2_4}
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									UpperCamelCase__         = crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
									UpperCamelCase__         = get_size_dict(lowercase ,      param_name="""crop_size"""			)

									UpperCamelCase__         = do_resize
									UpperCamelCase__         = size
									UpperCamelCase__         = resample
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = rescale_factor
									UpperCamelCase__         = do_center_crop
									UpperCamelCase__         = crop_size
									UpperCamelCase__         = do_flip_channel_order






			def 				A      (   self							:						str ,      lowercase							:						np.ndarray ,      lowercase							:						Dict[str, int] ,      lowercase							:						PILImageResampling = PIL.Image.BILINEAR ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None ,      **lowercase							:						List[Any] ,      )							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									if "shortest_edge" not in size:
															raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"			)
									UpperCamelCase__         = get_resize_output_image_size(lowercase ,      size=size["""shortest_edge"""] ,      default_to_square=lowercase			)
									return resize(lowercase ,      size=lowercase ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)






			def 				A      (   self							:						Dict ,      lowercase							:						np.ndarray ,      lowercase							:						Dict[str, int] ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None ,      **lowercase							:						Tuple ,      )							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__         = get_size_dict(lowercase			)
									if "height" not in size or "width" not in size:
															raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"			)
									return center_crop(lowercase ,      size=(size["""height"""], size["""width"""]) ,      data_format=lowercase ,      **lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						np.ndarray ,      lowercase							:						Union[int, float] ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None ,      **lowercase							:						str ,      )							->    str:

									'''simple docstring'''







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






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None			)							->    np.ndarray:

									'''simple docstring'''







									return flip_channel_order(lowercase ,      data_format=lowercase			)






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						ImageInput ,      lowercase							:						bool = None ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						PILImageResampling = None ,      lowercase							:						bool = None ,      lowercase							:						float = None ,      lowercase							:						bool = None ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						bool = None ,      lowercase							:						Optional[Union[str, TensorType]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						Dict ,      )							->    PIL.Image.Image:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = resample if resample is not None else self.resample
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCamelCase__         = do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCamelCase__         = (
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									UpperCamelCase__         = size if size is not None else self.size
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									UpperCamelCase__         = crop_size if crop_size is not None else self.crop_size
									UpperCamelCase__         = get_size_dict(lowercase ,      param_name="""crop_size"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															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_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

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

									# All transformations expect numpy arrays.
									UpperCamelCase__         = [to_numpy_array(lowercase			) for image in images]

									if do_resize:
															UpperCamelCase__         = [self.resize(image=lowercase ,      size=lowercase ,      resample=lowercase			) for image in images]

									if do_center_crop:
															UpperCamelCase__         = [self.center_crop(image=lowercase ,      size=lowercase			) for image in images]

									if do_rescale:
															UpperCamelCase__         = [self.rescale(image=lowercase ,      scale=lowercase			) for image in images]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
															UpperCamelCase__         = [self.flip_channel_order(image=lowercase			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)







			def 				A      (   self							:						Any ,      lowercase							:						int ,      lowercase							:						List[Tuple] = None			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = outputs.logits

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

															if is_torch_tensor(lowercase			):
																					UpperCamelCase__         = target_sizes.numpy()

															UpperCamelCase__         = []

															for idx in range(len(lowercase			)			):
																					UpperCamelCase__         = torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0			) ,      size=target_sizes[idx] ,      mode="""bilinear""" ,      align_corners=lowercase			)
																					UpperCamelCase__         = resized_logits[0].argmax(dim=0			)
																					semantic_segmentation.append(lowercase			)
									else:
															UpperCamelCase__         = logits.argmax(dim=1			)
															UpperCamelCase__         = [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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					"microsoft/speecht5_tts"
			__a							:							int					     =					(
			    "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."
			)
			__a							:							Optional[Any]					     =					"text_reader"
			__a							:							Tuple					     =					SpeechTaProcessor
			__a							:							int					     =					SpeechTaForTextToSpeech
			__a							:							int					     =					SpeechTaHifiGan

			__a							:							Optional[int]					     =					["text"]
			__a							:							Union[str, Any]					     =					["audio"]






			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if self.post_processor is None:
															UpperCamelCase__         = """microsoft/speecht5_hifigan"""
									super().setup()






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any]=None			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.pre_processor(text=lowercase ,      return_tensors="""pt""" ,      truncation=lowercase			)

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

															UpperCamelCase__         = load_dataset("""Matthijs/cmu-arctic-xvectors""" ,      split="""validation"""			)
															UpperCamelCase__         = torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]			).unsqueeze(0			)

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






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







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







			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







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