




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
		hf_model.apply_weight_norm()

		UpperCamelCase__				:					List[Any]					=					checkpoint['input_conv.weight_g']
		UpperCamelCase__				:					Union[str, Any]					=					checkpoint['input_conv.weight_v']
		UpperCamelCase__				:					List[Any]					=					checkpoint['input_conv.bias']

		for i in range(len(config.upsample_rates)):
				UpperCamelCase__				:					Optional[Any]					=					checkpoint[f'upsamples.{i}.1.weight_g']
				UpperCamelCase__				:					Tuple					=					checkpoint[f'upsamples.{i}.1.weight_v']
				UpperCamelCase__				:					Optional[Any]					=					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__				:					Optional[Any]					=					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
						UpperCamelCase__				:					Optional[Any]					=					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
						UpperCamelCase__				:					Any					=					checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

						UpperCamelCase__				:					Dict					=					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
						UpperCamelCase__				:					str					=					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
						UpperCamelCase__				:					List[Any]					=					checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

		UpperCamelCase__				:					str					=					checkpoint['output_conv.1.weight_g']
		UpperCamelCase__				:					Dict					=					checkpoint['output_conv.1.weight_v']
		UpperCamelCase__				:					str					=					checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()







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






		if config_path is not None:
				UpperCamelCase__				:					Dict					=					SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase_)
		else:
				UpperCamelCase__				:					Tuple					=					SpeechTaHifiGanConfig()

		UpperCamelCase__				:					List[str]					=					SpeechTaHifiGan(lowerCamelCase_)

		UpperCamelCase__				:					Dict					=					torch.load(lowerCamelCase_)
		load_weights(orig_checkpoint['model']['generator']     ,      lowerCamelCase_     ,      lowerCamelCase_)

		UpperCamelCase__				:					Optional[Any]					=					np.load(lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					stats[0].reshape(-1)
		UpperCamelCase__				:					List[str]					=					stats[1].reshape(-1)
		UpperCamelCase__				:					List[Any]					=					torch.from_numpy(lowerCamelCase_).float()
		UpperCamelCase__				:					List[Any]					=					torch.from_numpy(lowerCamelCase_).float()

		model.save_pretrained(lowerCamelCase_)

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


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

		lowerCAmelCase__          =						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'''




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Optional[int]=13  ,					UpperCAmelCase_   :						Tuple=30  ,					UpperCAmelCase_   :						Dict=2  ,					UpperCAmelCase_   :						Dict=3  ,					UpperCAmelCase_   :						Tuple=True  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						Tuple=32  ,					UpperCAmelCase_   :						List[str]=5  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						Optional[int]=37  ,					UpperCAmelCase_   :						str="gelu"  ,					UpperCAmelCase_   :						List[str]=0.1  ,					UpperCAmelCase_   :						Dict=0.1  ,					UpperCAmelCase_   :						Dict=10  ,					UpperCAmelCase_   :						Optional[int]=0.02  ,					UpperCAmelCase_   :						Union[str, Any]=3  ,					UpperCAmelCase_   :						Any=0.6  ,					UpperCAmelCase_   :						Dict=None  ,					):
			UpperCamelCase__				:					Tuple					=					parent
			UpperCamelCase__				:					List[str]					=					batch_size
			UpperCamelCase__				:					Optional[Any]					=					image_size
			UpperCamelCase__				:					Optional[Any]					=					patch_size
			UpperCamelCase__				:					List[str]					=					num_channels
			UpperCamelCase__				:					Union[str, Any]					=					is_training
			UpperCamelCase__				:					int					=					use_labels
			UpperCamelCase__				:					Optional[int]					=					hidden_size
			UpperCamelCase__				:					Any					=					num_hidden_layers
			UpperCamelCase__				:					str					=					num_attention_heads
			UpperCamelCase__				:					str					=					intermediate_size
			UpperCamelCase__				:					Union[str, Any]					=					hidden_act
			UpperCamelCase__				:					Optional[int]					=					hidden_dropout_prob
			UpperCamelCase__				:					Tuple					=					attention_probs_dropout_prob
			UpperCamelCase__				:					Any					=					type_sequence_label_size
			UpperCamelCase__				:					int					=					initializer_range
			UpperCamelCase__				:					Optional[int]					=					mask_ratio
			UpperCamelCase__				:					int					=					scope

			# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
			# (we add 1 for the [CLS] token)
			UpperCamelCase__				:					str					=					(image_size // patch_size) ** 2
			UpperCamelCase__				:					Dict					=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)))

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			UpperCamelCase__				:					List[str]					=					None
			if self.use_labels:
					UpperCamelCase__				:					Dict					=					ids_tensor([self.batch_size]  ,					self.type_sequence_label_size)

			UpperCamelCase__				:					Any					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[Any]):
			return ViTMAEConfig(
			    image_size=self.image_size  ,					patch_size=self.patch_size  ,					num_channels=self.num_channels  ,					hidden_size=self.hidden_size  ,					num_hidden_layers=self.num_hidden_layers  ,					num_attention_heads=self.num_attention_heads  ,					intermediate_size=self.intermediate_size  ,					hidden_act=self.hidden_act  ,					hidden_dropout_prob=self.hidden_dropout_prob  ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,					is_decoder=UpperCAmelCase_  ,					initializer_range=self.initializer_range  ,					mask_ratio=self.mask_ratio  ,					)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					ViTMAEModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Optional[int]					=					model(UpperCAmelCase_)
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size))

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Dict					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					(self.image_size // self.patch_size) ** 2
			UpperCamelCase__				:					Optional[int]					=					self.patch_size**2 * self.num_channels
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))

			# test greyscale images
			UpperCamelCase__				:					List[Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					List[str]					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			UpperCamelCase__				:					Union[str, Any]					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.patch_size**2
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[str]					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					config_and_inputs
			UpperCamelCase__				:					Any					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCamelCase              =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[str]					=					ViTMAEModelTester(self)
			UpperCamelCase__				:					Any					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37)

	def      __UpperCamelCase							(      self   :						Any):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='ViTMAE does not use inputs_embeds')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					List[str]					=					model_class(UpperCAmelCase_)
					self.assertIsInstance(model.get_input_embeddings()  ,					(nn.Module))
					UpperCamelCase__				:					Optional[Any]					=					model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_  ,					nn.Linear))

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Tuple					=					model_class(UpperCAmelCase_)
					UpperCamelCase__				:					int					=					inspect.signature(model.forward)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					UpperCamelCase__				:					Any					=					[*signature.parameters.keys()]

					UpperCamelCase__				:					Optional[int]					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			# make masks reproducible
			np.random.seed(2)

			UpperCamelCase__				:					str					=					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
			UpperCamelCase__				:					Tuple					=					np.random.uniform(size=(self.model_tester.batch_size, num_patches))
			UpperCamelCase__				:					Optional[Any]					=					torch.from_numpy(UpperCAmelCase_)

			# Add `noise` argument.
			# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
			UpperCamelCase__				:					List[str]					=					pt_noise

			super().check_pt_tf_models(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Optional[Any]					=					model_class(UpperCAmelCase_)
					model.to(UpperCAmelCase_)
					model.eval()
					# make random mask reproducible
					torch.manual_seed(2)
					with torch.no_grad():
							UpperCamelCase__				:					Tuple					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

					UpperCamelCase__				:					Dict					=					outputs[0].cpu().numpy()
					UpperCamelCase__				:					Optional[int]					=					0

					with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(UpperCAmelCase_)
							UpperCamelCase__				:					str					=					model_class.from_pretrained(UpperCAmelCase_)
							model.to(UpperCAmelCase_)
							# make random mask reproducible
							torch.manual_seed(2)
							with torch.no_grad():
									UpperCamelCase__				:					List[str]					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

							# Make sure we don't have nans
							UpperCamelCase__				:					Tuple					=					after_outputs[0].cpu().numpy()
							UpperCamelCase__				:					Any					=					0
							UpperCamelCase__				:					Union[str, Any]					=					np.amax(np.abs(out_a - out_a))
							self.assertLessEqual(UpperCAmelCase_  ,					1e-5)

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
	def      __UpperCamelCase							(      self   :						Optional[int]):
			pass





	@slow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Tuple					=					ViTMAEModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					int					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
		return image







@require_torch
@require_vision
class       __lowercase					(unittest.TestCase    ):

	@cached_property
	def      __UpperCamelCase							(      self   :						int):
			return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None





	@slow
	def      __UpperCamelCase							(      self   :						str):
			# make random mask reproducible across the PT and TF model
			np.random.seed(2)

			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.default_image_processor
			UpperCamelCase__				:					Dict					=					prepare_img()
			UpperCamelCase__				:					Optional[int]					=					image_processor(images=UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# prepare a noise vector that will be also used for testing the TF model
			# (this way we can ensure that the PT and TF models operate on the same inputs)
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEConfig()
			UpperCamelCase__				:					int					=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
			UpperCamelCase__				:					Any					=					np.random.uniform(size=(1, num_patches))

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Dict					=					model(**UpperCAmelCase_  ,					noise=torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_))

			# verify the logits
			UpperCamelCase__				:					Tuple					=					torch.Size((1, 196, 768))
			self.assertEqual(outputs.logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					torch.tensor(
			    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

			self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  ,					expected_slice.to(UpperCAmelCase_)  ,					atol=1e-4))




