


"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :    Optional[Any]									=				logging.get_logger("""transformers.models.speecht5""")






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Any     ,   _snake_case						:      int     )     ->Optional[int]:
	"""simple docstring"""





	hf_model.apply_weight_norm()

	__snake_case	:						Optional[Any]						=				checkpoint['''input_conv.weight_g''']
	__snake_case	:						str						=				checkpoint['''input_conv.weight_v''']
	__snake_case	:						Tuple						=				checkpoint['''input_conv.bias''']

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

			__snake_case	:						List[Any]						=				checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
			__snake_case	:						Tuple						=				checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
			__snake_case	:						Optional[int]						=				checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

	__snake_case	:						Optional[int]						=				checkpoint['''output_conv.1.weight_g''']
	__snake_case	:						int						=				checkpoint['''output_conv.1.weight_v''']
	__snake_case	:						int						=				checkpoint['''output_conv.1.bias''']

	hf_model.remove_weight_norm()






@torch.no_grad()
def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Dict     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[Any]=None     ,   _snake_case						:      int=None     ,   )     ->int:
	"""simple docstring"""





	if config_path is not None:
		__snake_case	:						Union[str, Any]						=				SpeechTaHifiGanConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Dict						=				SpeechTaHifiGanConfig()

	__snake_case	:						Union[str, Any]						=				SpeechTaHifiGan(_snake_case     )

	__snake_case	:						Union[str, Any]						=				torch.load(_snake_case     )
	load_weights(orig_checkpoint['''model''']['''generator''']     ,   _snake_case     ,   _snake_case     )

	__snake_case	:						Optional[Any]						=				np.load(_snake_case     )
	__snake_case	:						str						=				stats[0].reshape(-1     )
	__snake_case	:						Optional[int]						=				stats[1].reshape(-1     )
	__snake_case	:						List[str]						=				torch.from_numpy(_snake_case     ).float()
	__snake_case	:						int						=				torch.from_numpy(_snake_case     ).float()

	model.save_pretrained(_snake_case     )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Tuple									=				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 :    Dict									=				parser.parse_args()
			convert_hifigan_checkpoint(
			    args.checkpoint_path,
			    args.stats_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.push_to_hub,
			)







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     KandinskyVaaPriorPipeline
	_A								=     ['prompt']
	_A								=     ['prompt', 'negative_prompt']
	_A								=     [
	    'num_images_per_prompt',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	_A								=     False







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Optional[Any]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Tuple:
								return self.time_input_dim







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								return self.time_input_dim * 4







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> str:
								return 1_0_0







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								__UpperCamelCase	:   int			   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
								return tokenizer







	@property
	def    _lowerCamelCase				(					self		:str						)		-> int:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					CLIPTextConfig(
								    bos_token_id=0     ,   eos_token_id=2     ,   hidden_size=self.text_embedder_hidden_size     ,   projection_dim=self.text_embedder_hidden_size     ,   intermediate_size=3_7     ,   layer_norm_eps=1E-0_5     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   pad_token_id=1     ,   vocab_size=1_0_0_0     ,   )
								return CLIPTextModelWithProjection(a						)







	@property
	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								torch.manual_seed(0						)

								__UpperCamelCase	:   Optional[int]			   =					{
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_2,
								    "embedding_dim": self.text_embedder_hidden_size,
								    "num_layers": 1,
								}

								__UpperCamelCase	:   List[Any]			   =					PriorTransformer(**a						)
								# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
								__UpperCamelCase	:   Optional[int]			   =					nn.Parameter(torch.ones(model.clip_std.shape						)						)
								return model







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> Union[str, Any]:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Union[str, Any]			   =					CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,   image_size=2_2_4     ,   projection_dim=self.text_embedder_hidden_size     ,   intermediate_size=3_7     ,   num_attention_heads=4     ,   num_channels=3     ,   num_hidden_layers=5     ,   patch_size=1_4     ,   )

								__UpperCamelCase	:   int			   =					CLIPVisionModelWithProjection(a						)
								return model







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					CLIPImageProcessor(
								    crop_size=2_2_4     ,   do_center_crop=a     ,   do_normalize=a     ,   do_resize=a     ,   image_mean=[0.48145466, 0.4578275, 0.40821073]     ,   image_std=[0.26862954, 0.26130258, 0.27577711]     ,   resample=3     ,   size=2_2_4     ,   )

								return image_processor







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.dummy_prior
								__UpperCamelCase	:   int			   =					self.dummy_image_encoder
								__UpperCamelCase	:   Tuple			   =					self.dummy_text_encoder
								__UpperCamelCase	:   int			   =					self.dummy_tokenizer
								__UpperCamelCase	:   Optional[Any]			   =					self.dummy_image_processor

								__UpperCamelCase	:   int			   =					UnCLIPScheduler(
								    variance_type="fixed_small_log"     ,   prediction_type="sample"     ,   num_train_timesteps=1_0_0_0     ,   clip_sample=a     ,   clip_sample_range=10.0     ,   )

								__UpperCamelCase	:   List[Any]			   =					{
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "scheduler": scheduler,
								    "image_processor": image_processor,
								}

								return components







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:int     ,   a		:Union[str, Any]=0						)		-> Any:
								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   int			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   List[Any]			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   int			   =					{
								    "prompt": "horse",
								    "generator": generator,
								    "guidance_scale": 4.0,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}
								return inputs







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   int			   =					"cpu"

								__UpperCamelCase	:   List[str]			   =					self.get_dummy_components()

								__UpperCamelCase	:   List[str]			   =					self.pipeline_class(**a						)
								__UpperCamelCase	:   Tuple			   =					pipe.to(a						)

								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   int			   =					pipe(**self.get_dummy_inputs(a						)						)
								__UpperCamelCase	:   int			   =					output.image_embeds

								__UpperCamelCase	:   Optional[int]			   =					pipe(
								    **self.get_dummy_inputs(a						)     ,   return_dict=a     ,   )[0]

								__UpperCamelCase	:   Union[str, Any]			   =					image[0, -1_0:]
								__UpperCamelCase	:   List[str]			   =					image_from_tuple[0, -1_0:]

								assert image.shape == (1, 3_2)

								__UpperCamelCase	:   List[Any]			   =					np.array(
								    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2







	@skip_mps
	def    _lowerCamelCase				(					self		:int						)		-> Union[str, Any]:
								__UpperCamelCase	:   str			   =					torch_device == "cpu"
								__UpperCamelCase	:   List[str]			   =					True
								__UpperCamelCase	:   List[Any]			   =					False

								self._test_inference_batch_single_identical(
								    test_max_difference=a     ,   relax_max_difference=a     ,   test_mean_pixel_difference=a     ,   )







	@skip_mps
	def    _lowerCamelCase				(					self		:Any						)		-> int:
								__UpperCamelCase	:   Optional[Any]			   =					torch_device == "cpu"
								__UpperCamelCase	:   Dict			   =					False

								self._test_attention_slicing_forward_pass(
								    test_max_difference=a     ,   test_mean_pixel_difference=a     ,   )