
"""simple docstring"""

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'Pix2StructImageProcessor'
	lowercase_				=     ('T5Tokenizer', 'T5TokenizerFast')




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      List[str]          =   False
			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 2048		,			UpperCAmelCase_ = 0		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Any:
			if images is None and text is None:
					raise ValueError('You have to specify either images or text.' )

			# Get only text
			if images is None and not self.image_processor.is_vqa:
					lowerCamelCase					:      str          =   self.tokenizer
					lowerCamelCase					:      Tuple          =   self.tokenizer(
					    text=UpperCAmelCase_		,			add_special_tokens=UpperCAmelCase_		,			padding=UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			stride=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			return_attention_mask=UpperCAmelCase_		,			return_overflowing_tokens=UpperCAmelCase_		,			return_special_tokens_mask=UpperCAmelCase_		,			return_offsets_mapping=UpperCAmelCase_		,			return_token_type_ids=UpperCAmelCase_		,			return_length=UpperCAmelCase_		,			verbose=UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_		,			)
					return text_encoding

			if not self.image_processor.is_vqa:
					# add pixel_values
					lowerCamelCase					:      List[str]          =   self.image_processor(
					    UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			max_patches=UpperCAmelCase_		,			**UpperCAmelCase_ )
			else:
					# add pixel_values and bbox
					lowerCamelCase					:      Optional[Any]          =   self.image_processor(
					    UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			max_patches=UpperCAmelCase_		,			header_text=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and not self.image_processor.is_vqa:
					lowerCamelCase					:      Union[str, Any]          =   self.tokenizer(
					    text=UpperCAmelCase_		,			add_special_tokens=UpperCAmelCase_		,			padding=UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			stride=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			return_attention_mask=UpperCAmelCase_		,			return_overflowing_tokens=UpperCAmelCase_		,			return_special_tokens_mask=UpperCAmelCase_		,			return_offsets_mapping=UpperCAmelCase_		,			return_token_type_ids=UpperCAmelCase_		,			return_length=UpperCAmelCase_		,			verbose=UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

					if "attention_mask" in text_encoding:
							lowerCamelCase					:      Optional[int]          =   text_encoding.pop('attention_mask' )
					if "input_ids" in text_encoding:
							lowerCamelCase					:      Optional[Any]          =   text_encoding.pop('input_ids' )
			else:
					lowerCamelCase					:      List[str]          =   None

			if text_encoding is not None:
					encoding_image_processor.update(UpperCAmelCase_ )

			return encoding_image_processor




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							str:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Union[str, Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Optional[int]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )








"""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 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

		for i in range(len(config.upsample_rates      )      ):
				lowerCamelCase					:      Tuple          =   checkpoint[F"""upsamples.{i}.1.weight_g"""]
				lowerCamelCase					:      Any          =   checkpoint[F"""upsamples.{i}.1.weight_v"""]
				lowerCamelCase					:      List[str]          =   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      )      ):
						lowerCamelCase					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
						lowerCamelCase					:      Tuple          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
						lowerCamelCase					:      Union[str, Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

						lowerCamelCase					:      Dict          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
						lowerCamelCase					:      str          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
						lowerCamelCase					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

		lowerCamelCase					:      Any          =   checkpoint['output_conv.1.weight_g']
		lowerCamelCase					:      Tuple          =   checkpoint['output_conv.1.weight_v']
		lowerCamelCase					:      int          =   checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

		lowerCamelCase					:      Optional[Any]          =   torch.load(a_      )
		load_weights(orig_checkpoint['model']['generator'],						a_,						a_      )

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[int]          =   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,
					)






