
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_a														=		logging.get_logger("""transformers.models.speecht5""")



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					hf_model.apply_weight_norm()

					_UpperCamelCase									=  checkpoint["""input_conv.weight_g"""]
					_UpperCamelCase									=  checkpoint["""input_conv.weight_v"""]
					_UpperCamelCase									=  checkpoint["""input_conv.bias"""]

					for i in range(len(config.upsample_rates     )     ):
										_UpperCamelCase									=  checkpoint[F'''upsamples.{i}.1.weight_g''']
										_UpperCamelCase									=  checkpoint[F'''upsamples.{i}.1.weight_v''']
										_UpperCamelCase									=  checkpoint[F'''upsamples.{i}.1.bias''']

					for i in range(len(config.upsample_rates     ) * len(config.resblock_kernel_sizes     )     ):
										for j in range(len(config.resblock_dilation_sizes     )     ):
															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
															_UpperCamelCase									=  checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

					_UpperCamelCase									=  checkpoint["""output_conv.1.weight_g"""]
					_UpperCamelCase									=  checkpoint["""output_conv.1.weight_v"""]
					_UpperCamelCase									=  checkpoint["""output_conv.1.bias"""]

					hf_model.remove_weight_norm()



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case=None,					__snake_case=None,					)							->		Optional[int]:


					"""simple docstring"""





					if config_path is not None:
										_UpperCamelCase									=  SpeechTaHifiGanConfig.from_pretrained(_a     )
					else:
										_UpperCamelCase									=  SpeechTaHifiGanConfig()

					_UpperCamelCase									=  SpeechTaHifiGan(_a     )

					_UpperCamelCase									=  torch.load(_a     )
					load_weights(orig_checkpoint['''model''']['''generator'''],					_a,					_a     )

					_UpperCamelCase									=  np.load(_a     )
					_UpperCamelCase									=  stats[0].reshape(-1     )
					_UpperCamelCase									=  stats[1].reshape(-1     )
					_UpperCamelCase									=  torch.from_numpy(_a     ).float()
					_UpperCamelCase									=  torch.from_numpy(_a     ).float()

					model.save_pretrained(_a     )

					if repo_id:
										print('''Pushing to the hub...'''     )
										model.push_to_hub(_a     )


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser()
				parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
				parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
				parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
				parser.add_argument(
				    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
				)

				_a														=		parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)








"""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.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






