



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A		=						logging.get_logger(__name__)

A		=						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """yolos"""



			def __init__(			self							:				Optional[int]   ,	_lowercase							:				int=768   ,	_lowercase							:				Dict=12   ,	_lowercase							:				Optional[int]=12   ,	_lowercase							:				Tuple=3072   ,	_lowercase							:				Union[str, Any]="gelu"   ,	_lowercase							:				List[str]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=0.02   ,	_lowercase							:				Union[str, Any]=1E-12   ,	_lowercase							:				Optional[int]=[512, 864]   ,	_lowercase							:				List[Any]=16   ,	_lowercase							:				Optional[int]=3   ,	_lowercase							:				str=True   ,	_lowercase							:				str=100   ,	_lowercase							:				Union[str, Any]=True   ,	_lowercase							:				Optional[Any]=False   ,	_lowercase							:				Optional[Any]=1   ,	_lowercase							:				List[Any]=5   ,	_lowercase							:				List[str]=2   ,	_lowercase							:				str=5   ,	_lowercase							:				str=2   ,	_lowercase							:				List[str]=0.1   ,	**_lowercase							:				Dict   ,	)							->					List[Any]:
									super().__init__(**A_		)

									snake_case   :			List[str]						    = hidden_size
									snake_case   :			Union[str, Any]						    = num_hidden_layers
									snake_case   :			int						    = num_attention_heads
									snake_case   :			List[Any]						    = intermediate_size
									snake_case   :			Any						    = hidden_act
									snake_case   :			Optional[int]						    = hidden_dropout_prob
									snake_case   :			Optional[Any]						    = attention_probs_dropout_prob
									snake_case   :			Tuple						    = initializer_range
									snake_case   :			List[Any]						    = layer_norm_eps
									snake_case   :			int						    = image_size
									snake_case   :			Optional[Any]						    = patch_size
									snake_case   :			Tuple						    = num_channels
									snake_case   :			str						    = qkv_bias
									snake_case   :			int						    = num_detection_tokens
									snake_case   :			Dict						    = use_mid_position_embeddings
									snake_case   :			List[Any]						    = auxiliary_loss
									# Hungarian matcher
									snake_case   :			List[str]						    = class_cost
									snake_case   :			str						    = bbox_cost
									snake_case   :			Any						    = giou_cost
									# Loss coefficients
									snake_case   :			List[Any]						    = bbox_loss_coefficient
									snake_case   :			str						    = giou_loss_coefficient
									snake_case   :			Optional[int]						    = eos_coefficient



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.11""")



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
									    ]		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					float:
									return 1E-4



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					int:
									return 12





"""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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.pre_processor(text=A_			,					return_tensors='''pt'''			,					truncation=A_							)

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

           _lowercase:      Union[str, Any]   			=  load_dataset('''Matthijs/cmu-arctic-xvectors'''			,					split='''validation'''							)
           _lowercase:      Optional[int]   			=  torch.tensor(embeddings_dataset[7305]['''xvector''']							).unsqueeze(0							)

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




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



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




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