






'''simple docstring'''


import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
					import PIL.Image

					from .features import FeatureType


lowerCamelCase_				: Optional[Any]			=							None
lowerCamelCase_				: List[Any]			=							'''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCamelCase_				: int			=							[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]
@dataclass
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							bool					     =					True
			__a							:							Optional[str]					     =					None
			# Automatically constructed
			__a							:							ClassVar[str]					     =					"PIL.Image.Image"
			__a							:							ClassVar[Any]					     =					pa.struct({"bytes": pa.binary(), "path": pa.string()}	)
			__a							:							str					     =					field(default="Image"       ,init=a__       ,repr=a__	)






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

									'''simple docstring'''







									return self.pa_type






			def 				A      (   self							:						int ,      lowercase							:						Any			)							->    dict:

									'''simple docstring'''







									if config.PIL_AVAILABLE:
															import PIL.Image
									else:
															raise ImportError("""To support encoding images, please install \'Pillow\'."""			)

									if isinstance(lowerCamelCase_ ,      lowerCamelCase_			):
															UpperCamelCase__         = np.array(lowerCamelCase_			)

									if isinstance(lowerCamelCase_ ,      lowerCamelCase_			):
															return {"path": value, "bytes": None}
									elif isinstance(lowerCamelCase_ ,      lowerCamelCase_			):
															return {"path": None, "bytes": value}
									elif isinstance(lowerCamelCase_ ,      np.ndarray			):
															# convert the image array to PNG/TIFF bytes
															return encode_np_array(lowerCamelCase_			)
									elif isinstance(lowerCamelCase_ ,      PIL.Image.Image			):
															# convert the PIL image to bytes (default format is PNG/TIFF)
															return encode_pil_image(lowerCamelCase_			)
									elif value.get("""path"""			) is not None and os.path.isfile(value["""path"""]			):
															# we set "bytes": None to not duplicate the data if they're already available locally
															return {"bytes": None, "path": value.get("""path"""			)}
									elif value.get("""bytes"""			) is not None or value.get("""path"""			) is not None:
															# store the image bytes, and path is used to infer the image format using the file extension
															return {"bytes": value.get("""bytes"""			), "path": value.get("""path"""			)}
									else:
															raise ValueError(
															    f"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."			)






			def 				A      (   self							:						List[str] ,      lowercase							:						Tuple ,      lowercase							:						Optional[int]=None			)							->    "PIL.Image.Image":

									'''simple docstring'''







									if not self.decode:
															raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""			)

									if config.PIL_AVAILABLE:
															import PIL.Image
									else:
															raise ImportError("""To support decoding images, please install \'Pillow\'."""			)

									if token_per_repo_id is None:
															UpperCamelCase__         = {}

									UpperCamelCase__    ,   UpperCamelCase__         = value["""path"""], value["""bytes"""]
									if bytes_ is None:
															if path is None:
																					raise ValueError(f"An image should have one of 'path' or 'bytes' but both are None in {value}."			)
															else:
																					if is_local_path(lowerCamelCase_			):
																											UpperCamelCase__         = PIL.Image.open(lowerCamelCase_			)
																					else:
																											UpperCamelCase__         = path.split("""::"""			)[-1]
																											try:
																																	UpperCamelCase__         = string_to_dict(lowerCamelCase_ ,      config.HUB_DATASETS_URL			)["""repo_id"""]
																																	UpperCamelCase__         = token_per_repo_id.get(lowerCamelCase_			)
																											except ValueError:
																																	UpperCamelCase__         = None
																											with xopen(lowerCamelCase_ ,      """rb""" ,      use_auth_token=lowerCamelCase_			) as f:
																																	UpperCamelCase__         = BytesIO(f.read()			)
																											UpperCamelCase__         = PIL.Image.open(bytes_			)
									else:
															UpperCamelCase__         = PIL.Image.open(BytesIO(bytes_			)			)
									image.load()  # to avoid "Too many open files" errors
									return image






			def 				A      (   self							:						int			)							->    Union["FeatureType", Dict[str, "FeatureType"]]:

									'''simple docstring'''







									from .features import Value

									return (
									    self
									    if self.decode
									    else {
									        "bytes": Value("""binary"""			),
									        "path": Value("""string"""			),
									    }
									)






			def 				A      (   self							:						int ,      lowercase							:						Any			)							->    pa.StructArray:

									'''simple docstring'''







									if pa.types.is_string(storage.type			):
															UpperCamelCase__         = pa.array([None] * len(lowerCamelCase_			) ,      type=pa.binary()			)
															UpperCamelCase__         = pa.StructArray.from_arrays([bytes_array, storage] ,      ["""bytes""", """path"""] ,      mask=storage.is_null()			)
									elif pa.types.is_binary(storage.type			):
															UpperCamelCase__         = pa.array([None] * len(lowerCamelCase_			) ,      type=pa.string()			)
															UpperCamelCase__         = pa.StructArray.from_arrays([storage, path_array] ,      ["""bytes""", """path"""] ,      mask=storage.is_null()			)
									elif pa.types.is_struct(storage.type			):
															if storage.type.get_field_index("""bytes"""			) >= 0:
																					UpperCamelCase__         = storage.field("""bytes"""			)
															else:
																					UpperCamelCase__         = pa.array([None] * len(lowerCamelCase_			) ,      type=pa.binary()			)
															if storage.type.get_field_index("""path"""			) >= 0:
																					UpperCamelCase__         = storage.field("""path"""			)
															else:
																					UpperCamelCase__         = pa.array([None] * len(lowerCamelCase_			) ,      type=pa.string()			)
															UpperCamelCase__         = pa.StructArray.from_arrays([bytes_array, path_array] ,      ["""bytes""", """path"""] ,      mask=storage.is_null()			)
									elif pa.types.is_list(storage.type			):
															UpperCamelCase__         = pa.array(
															    [encode_np_array(np.array(lowerCamelCase_			)			)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] ,      type=pa.binary() ,      )
															UpperCamelCase__         = pa.array([None] * len(lowerCamelCase_			) ,      type=pa.string()			)
															UpperCamelCase__         = pa.StructArray.from_arrays(
															    [bytes_array, path_array] ,      ["""bytes""", """path"""] ,      mask=bytes_array.is_null()			)
									return array_cast(lowerCamelCase_ ,      self.pa_type			)







			def 				A      (   self							:						Optional[Any] ,      lowercase							:						Any			)							->    pa.StructArray:

									'''simple docstring'''







									@no_op_if_value_is_null
									def path_to_bytes(lowercase							:						List[str]			):
															with xopen(lowerCamelCase_ ,      """rb"""			) as f:
																					UpperCamelCase__         = f.read()
															return bytes_

									UpperCamelCase__         = pa.array(
									    [
									        (path_to_bytes(x["""path"""]			) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
									        for x in storage.to_pylist()
									    ] ,      type=pa.binary() ,      )
									UpperCamelCase__         = pa.array(
									    [os.path.basename(lowerCamelCase_			) if path is not None else None for path in storage.field("""path"""			).to_pylist()] ,      type=pa.string() ,      )
									UpperCamelCase__         = pa.StructArray.from_arrays([bytes_array, path_array] ,      ["""bytes""", """path"""] ,      mask=bytes_array.is_null()			)
									return array_cast(lowerCamelCase_ ,      self.pa_type			)







def 	__magic_name__( ):
						'''simple docstring'''





						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install \'Pillow\'."""					)

						global _IMAGE_COMPRESSION_FORMATS
						if _IMAGE_COMPRESSION_FORMATS is None:
												PIL.Image.init()
												UpperCamelCase__         = list(set(PIL.Image.OPEN.keys()					) & set(PIL.Image.SAVE.keys()					)					)
						return _IMAGE_COMPRESSION_FORMATS







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = BytesIO()
						if image.format in list_image_compression_formats():
												UpperCamelCase__         = image.format
						else:
												UpperCamelCase__         = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
						image.save(_A						,      format=_A					)
						return buffer.getvalue()







def 	__magic_name__( _A					):
						'''simple docstring'''





						if hasattr(_A						,      """filename"""					) and image.filename != "":
												return {"path": image.filename, "bytes": None}
						else:
												return {"path": None, "bytes": image_to_bytes(_A					)}







def 	__magic_name__( _A					):
						'''simple docstring'''





						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install \'Pillow\'."""					)

						UpperCamelCase__         = array.dtype
						UpperCamelCase__         = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
						UpperCamelCase__         = dtype.kind
						UpperCamelCase__         = dtype.itemsize

						UpperCamelCase__         = None

						# Multi-channel array case (only np.dtype("|u1") is allowed)
						if array.shape[2:]:
												UpperCamelCase__         = np.dtype("""|u1"""					)
												if dtype_kind not in ["u", "i"]:
																		raise TypeError(
																		    f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."					)
												if dtype is not dest_dtype:
																		warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"					)
    # Exact match
						elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
												UpperCamelCase__         = dtype
						else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
												while dtype_itemsize >= 1:
																		UpperCamelCase__         = dtype_byteorder + dtype_kind + str(_A					)
																		UpperCamelCase__         = np.dtype(_A					)
																		if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																								warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"					)
																								break
																		else:
																								dtype_itemsize //= 2

						if dest_dtype is None:
												raise TypeError(
												    f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"					)
						UpperCamelCase__         = PIL.Image.fromarray(array.astype(_A					)					)
						return {"path": None, "bytes": image_to_bytes(_A					)}







def 	__magic_name__( _A					):
						'''simple docstring'''





						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install \'Pillow\'."""					)

						if objs:
												UpperCamelCase__    ,   UpperCamelCase__         = first_non_null_value(_A					)
												if isinstance(_A						,      _A					):
																		return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
												if isinstance(_A						,      np.ndarray					):
																		UpperCamelCase__         = no_op_if_value_is_null(_A					)
																		return [obj_to_image_dict_func(_A					) for obj in objs]
												elif isinstance(_A						,      PIL.Image.Image					):
																		UpperCamelCase__         = no_op_if_value_is_null(_A					)
																		return [obj_to_image_dict_func(_A					) for obj in objs]
												else:
																		return objs
						else:
												return objs








'''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()
