






'''simple docstring'''

import warnings
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     a_						(      lowerCamelCase__    ):
		lowercase							=				["""image_processor""", """tokenizer"""]
		lowercase							=				"""LayoutLMv2ImageProcessor"""
		lowercase							=				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

		def __init__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



								"""simple docstring"""






								if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""      ,		__snake_case      ,		)
														UpperCamelCase      = kwargs.pop("""feature_extractor"""				)

								UpperCamelCase      = image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
														raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(__snake_case      ,		__snake_case				)

		def __call__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[int]:



								"""simple docstring"""






								if self.image_processor.apply_ocr and (boxes is not None):
														raise ValueError(
														    """You cannot provide bounding boxes """
														    """if you initialized the image processor with apply_ocr set to True."""				)

								if self.image_processor.apply_ocr and (word_labels is not None):
														raise ValueError(
														    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

								if return_overflowing_tokens is True and return_offsets_mapping is False:
														raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""				)

								# first, apply the image processor
								UpperCamelCase      = self.image_processor(images=__snake_case      ,		return_tensors=__snake_case				)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(__snake_case      ,		__snake_case				):
																				UpperCamelCase      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
														UpperCamelCase      = features['words']

								UpperCamelCase      = self.tokenizer(
								    text=text if text is not None else features["""words"""]      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features["""boxes"""]      ,		word_labels=__snake_case      ,		add_special_tokens=__snake_case      ,		padding=__snake_case      ,		truncation=__snake_case      ,		max_length=__snake_case      ,		stride=__snake_case      ,		pad_to_multiple_of=__snake_case      ,		return_token_type_ids=__snake_case      ,		return_attention_mask=__snake_case      ,		return_overflowing_tokens=__snake_case      ,		return_special_tokens_mask=__snake_case      ,		return_offsets_mapping=__snake_case      ,		return_length=__snake_case      ,		verbose=__snake_case      ,		return_tensors=__snake_case      ,		**__snake_case      ,		)

								# add pixel values
								UpperCamelCase      = features.pop("""pixel_values"""				)
								if return_overflowing_tokens is True:
														UpperCamelCase      = self.get_overflowing_images(__snake_case      ,		encoded_inputs["""overflow_to_sample_mapping"""]				)
								UpperCamelCase      = images

								return encoded_inputs

		def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



								"""simple docstring"""






								UpperCamelCase      = []
								for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx]				)

								if len(__snake_case				) != len(__snake_case				):
														raise ValueError(
														    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
														    F" {len(__snake_case				)} and {len(__snake_case				)}"				)

								return images_with_overflow

		def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



								"""simple docstring"""






								return self.tokenizer.batch_decode(*__snake_case      ,		**__snake_case				)

		def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



								"""simple docstring"""






								return self.tokenizer.decode(*__snake_case      ,		**__snake_case				)

		@property
		def   A__						(				self				)  ->				Any:



								"""simple docstring"""






								return ["input_ids", "bbox", "attention_mask", "image"]

		@property
		def   A__						(				self				)  ->				Dict:



								"""simple docstring"""






								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,		__snake_case      ,		)
								return self.image_processor_class


		@property
		def   A__						(				self				)  ->				Tuple:



								"""simple docstring"""






								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,		__snake_case      ,		)
								return self.image_processor








'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger('transformers.models.speecht5')







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

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

      model.save_pretrained(__UpperCamelCase   )

      if repo_id:
            print("""Pushing to the hub..."""   )
            model.push_to_hub(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       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.'
     )

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