
"""simple docstring"""

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		def constraint_to_multiple_of(a_,						a_,						a_=0,						a_=None      ):
				lowerCamelCase					:      Union[str, Any]          =   round(val / multiple      ) * multiple

				if max_val is not None and x > max_val:
						lowerCamelCase					:      int          =   math.floor(val / multiple      ) * multiple

				if x < min_val:
						lowerCamelCase					:      Optional[int]          =   math.ceil(val / multiple      ) * multiple

				return x

		lowerCamelCase					:      str          =   (output_size, output_size) if isinstance(a_,						a_      ) else output_size

		lowerCamelCase     ,   lowerCamelCase					:      Dict          =   get_image_size(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   output_size

		# determine new height and width
		lowerCamelCase					:      Dict          =   output_height / input_height
		lowerCamelCase					:      Dict          =   output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width      ) < abs(1 - scale_height      ):
						# fit width
						lowerCamelCase					:      Dict          =   scale_width
				else:
						# fit height
						lowerCamelCase					:      List[str]          =   scale_height

		lowerCamelCase					:      Tuple          =   constraint_to_multiple_of(scale_height * input_height,						multiple=a_      )
		lowerCamelCase					:      Any          =   constraint_to_multiple_of(scale_width * input_width,						multiple=a_      )

		return (new_height, new_width)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['pixel_values']




	def __init__(			self		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = PILImageResampling.BILINEAR		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = 1 / 255		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							None:
			super().__init__(**UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   size if size is not None else {'height': 384, 'width': 384}
			lowerCamelCase					:      Any          =   get_size_dict(UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   do_resize
			lowerCamelCase					:      Dict          =   size
			lowerCamelCase					:      Tuple          =   keep_aspect_ratio
			lowerCamelCase					:      Dict          =   ensure_multiple_of
			lowerCamelCase					:      List[str]          =   resample
			lowerCamelCase					:      Any          =   do_rescale
			lowerCamelCase					:      List[Any]          =   rescale_factor
			lowerCamelCase					:      str          =   do_normalize
			lowerCamelCase					:      Tuple          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCamelCase					:      str          =   image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = PILImageResampling.BICUBIC		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							np.ndarray:
			lowerCamelCase					:      List[Any]          =   get_size_dict(UpperCAmelCase_ )
			if "height" not in size or "width" not in size:
					raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
			lowerCamelCase					:      int          =   get_resize_output_image_size(
			    UpperCAmelCase_		,			output_size=(size['height'], size['width'])		,			keep_aspect_ratio=UpperCAmelCase_		,			multiple=UpperCAmelCase_		,			)
			return resize(UpperCAmelCase_		,			size=UpperCAmelCase_		,			resample=UpperCAmelCase_		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Optional[int]:
			return rescale(UpperCAmelCase_		,			scale=UpperCAmelCase_		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							np.ndarray:
			return normalize(UpperCAmelCase_		,			mean=UpperCAmelCase_		,			std=UpperCAmelCase_		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = ChannelDimension.FIRST		,			**UpperCAmelCase_		,			)		->							PIL.Image.Image:
			lowerCamelCase					:      List[Any]          =   do_resize if do_resize is not None else self.do_resize
			lowerCamelCase					:      Any          =   size if size is not None else self.size
			lowerCamelCase					:      List[str]          =   get_size_dict(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
			lowerCamelCase					:      int          =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
			lowerCamelCase					:      List[Any]          =   resample if resample is not None else self.resample
			lowerCamelCase					:      str          =   do_rescale if do_rescale is not None else self.do_rescale
			lowerCamelCase					:      Optional[int]          =   rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCamelCase					:      Dict          =   do_normalize if do_normalize is not None else self.do_normalize
			lowerCamelCase					:      Optional[Any]          =   image_mean if image_mean is not None else self.image_mean
			lowerCamelCase					:      Any          =   image_std if image_std is not None else self.image_std

			lowerCamelCase					:      int          =   make_list_of_images(UpperCAmelCase_ )

			if not valid_images(UpperCAmelCase_ ):
					raise ValueError(
					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
					    'torch.Tensor, tf.Tensor or jax.ndarray.' )

			if do_resize and size is None or resample is None:
					raise ValueError('Size and resample must be specified if do_resize is True.' )

			if do_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.' )

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError('Image mean and std must be specified if do_normalize is True.' )

			# All transformations expect numpy arrays.
			lowerCamelCase					:      Tuple          =   [to_numpy_array(UpperCAmelCase_ ) for image in images]

			if do_resize:
					lowerCamelCase					:      int          =   [self.resize(image=UpperCAmelCase_		,			size=UpperCAmelCase_		,			resample=UpperCAmelCase_ ) for image in images]

			if do_rescale:
					lowerCamelCase					:      Tuple          =   [self.rescale(image=UpperCAmelCase_		,			scale=UpperCAmelCase_ ) for image in images]

			if do_normalize:
					lowerCamelCase					:      Optional[Any]          =   [self.normalize(image=UpperCAmelCase_		,			mean=UpperCAmelCase_		,			std=UpperCAmelCase_ ) for image in images]

			lowerCamelCase					:      Tuple          =   [to_channel_dimension_format(UpperCAmelCase_		,			UpperCAmelCase_ ) for image in images]

			lowerCamelCase					:      Any          =   {'pixel_values': images}
			return BatchFeature(data=UpperCAmelCase_		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							int:
			lowerCamelCase					:      List[Any]          =   outputs.logits

			# Resize logits and compute semantic segmentation maps
			if target_sizes is not None:
					if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
							raise ValueError(
							    'Make sure that you pass in as many target sizes as the batch dimension of the logits' )

					if is_torch_tensor(UpperCAmelCase_ ):
							lowerCamelCase					:      Optional[int]          =   target_sizes.numpy()

					lowerCamelCase					:      Tuple          =   []

					for idx in range(len(UpperCAmelCase_ ) ):
							lowerCamelCase					:      List[Any]          =   torch.nn.functional.interpolate(
							    logits[idx].unsqueeze(dim=0 )		,			size=target_sizes[idx]		,			mode='bilinear'		,			align_corners=UpperCAmelCase_ )
							lowerCamelCase					:      Optional[Any]          =   resized_logits[0].argmax(dim=0 )
							semantic_segmentation.append(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Dict          =   logits.argmax(dim=1 )
					lowerCamelCase					:      Union[str, Any]          =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

			return semantic_segmentation








"""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,
					)






