






'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase_				: Union[str, Any]			=							logging.get_logger(__name__)

lowerCamelCase_				: Optional[Any]			=							{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}
class    _SCREAMING_SNAKE_CASE							(		UpperCAmelCase__       ,UpperCAmelCase__	):


			'''simple docstring'''





			__a							:							str					     =					"swin"

			__a							:							List[str]					     =					{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}






			def __init__(   self							:						List[Any] ,      lowercase							:						Union[str, Any]=2_2_4 ,      lowercase							:						str=4 ,      lowercase							:						Union[str, Any]=3 ,      lowercase							:						Union[str, Any]=9_6 ,      lowercase							:						Optional[int]=[2, 2, 6, 2] ,      lowercase							:						Any=[3, 6, 1_2, 2_4] ,      lowercase							:						Dict=7 ,      lowercase							:						Optional[Any]=4.0 ,      lowercase							:						Optional[int]=True ,      lowercase							:						Optional[int]=0.0 ,      lowercase							:						List[Any]=0.0 ,      lowercase							:						int=0.1 ,      lowercase							:						Any="gelu" ,      lowercase							:						Optional[Any]=False ,      lowercase							:						Optional[int]=0.0_2 ,      lowercase							:						Dict=1e-5 ,      lowercase							:						Optional[int]=3_2 ,      lowercase							:						int=None ,      lowercase							:						Any=None ,      **lowercase							:						Optional[int] ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(**__lowerCAmelCase			)

									UpperCamelCase__         = image_size
									UpperCamelCase__         = patch_size
									UpperCamelCase__         = num_channels
									UpperCamelCase__         = embed_dim
									UpperCamelCase__         = depths
									UpperCamelCase__         = len(__lowerCAmelCase			)
									UpperCamelCase__         = num_heads
									UpperCamelCase__         = window_size
									UpperCamelCase__         = mlp_ratio
									UpperCamelCase__         = qkv_bias
									UpperCamelCase__         = hidden_dropout_prob
									UpperCamelCase__         = attention_probs_dropout_prob
									UpperCamelCase__         = drop_path_rate
									UpperCamelCase__         = hidden_act
									UpperCamelCase__         = use_absolute_embeddings
									UpperCamelCase__         = layer_norm_eps
									UpperCamelCase__         = initializer_range
									UpperCamelCase__         = encoder_stride
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									UpperCamelCase__         = int(embed_dim * 2 ** (len(__lowerCAmelCase			) - 1)			)
									UpperCamelCase__         = ["""stem"""] + [f"stage{idx}" for idx in range(1 ,      len(__lowerCAmelCase			) + 1			)]
									UpperCamelCase__    ,   UpperCamelCase__         = get_aligned_output_features_output_indices(
									    out_features=__lowerCAmelCase ,      out_indices=__lowerCAmelCase ,      stage_names=self.stage_names			)
class    _SCREAMING_SNAKE_CASE							(		UpperCAmelCase__	):


			'''simple docstring'''





			__a							:							List[Any]					     =					version.parse("1.11"	)






			@property
			def 				A      (   self							:						Any			)							->    Mapping[str, Mapping[int, str]]:

									'''simple docstring'''







									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]			)







			@property
			def 				A      (   self							:						Optional[int]			)							->    float:

									'''simple docstring'''







									return 1e-4








'''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()
