





import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
	from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class    __lowerCAmelCase			:






			def __init__(self   ,				__magic_name__   ,				__magic_name__=13   ,				__magic_name__=30   ,				__magic_name__=2   ,				__magic_name__=3   ,				__magic_name__=True   ,				__magic_name__=True   ,				__magic_name__=32   ,				__magic_name__=5   ,				__magic_name__=4   ,				__magic_name__=37   ,				__magic_name__="gelu"   ,				__magic_name__=0.1   ,				__magic_name__=0.1   ,				__magic_name__=10   ,				__magic_name__=0.02   ,				__magic_name__=None   ,				)	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    parent
							snake_case_		:       Optional[Any]           =    batch_size
							snake_case_		:       List[Any]           =    image_size
							snake_case_		:       Optional[int]           =    patch_size
							snake_case_		:       Optional[Any]           =    num_channels
							snake_case_		:       Optional[Any]           =    is_training
							snake_case_		:       List[Any]           =    use_labels
							snake_case_		:       Optional[int]           =    hidden_size
							snake_case_		:       Optional[Any]           =    num_hidden_layers
							snake_case_		:       Union[str, Any]           =    num_attention_heads
							snake_case_		:       Optional[Any]           =    intermediate_size
							snake_case_		:       Any           =    hidden_act
							snake_case_		:       List[str]           =    hidden_dropout_prob
							snake_case_		:       Dict           =    attention_probs_dropout_prob
							snake_case_		:       List[str]           =    type_sequence_label_size
							snake_case_		:       Union[str, Any]           =    initializer_range
							snake_case_		:       List[Any]           =    scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							snake_case_		:       Any           =    (image_size // patch_size) ** 2
							snake_case_		:       int           =    num_patches + 1






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							snake_case_		:       List[Any]           =    None
							if self.use_labels:
											snake_case_		:       Optional[Any]           =    ids_tensor([self.batch_size]   ,				self.type_sequence_label_size       )

							snake_case_		:       int           =    self.get_config()

							return config, pixel_values, labels






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							return ViTMSNConfig(
							    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   ,				initializer_range=self.initializer_range   ,				)






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       int           =    ViTMSNModel(config=__magic_name__       )
							model.to(__magic_name__       )
							model.eval()
							snake_case_		:       List[str]           =    model(__magic_name__       )
							self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       int           =    self.type_sequence_label_size
							snake_case_		:       Tuple           =    ViTMSNForImageClassification(__magic_name__       )
							model.to(__magic_name__       )
							model.eval()
							snake_case_		:       Any           =    model(__magic_name__   ,				labels=__magic_name__       )
							print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''       )
							print('''Labels: {labels}'''       )
							self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)       )

							# test greyscale images
							snake_case_		:       Optional[int]           =    1
							snake_case_		:       List[str]           =    ViTMSNForImageClassification(__magic_name__       )
							model.to(__magic_name__       )
							model.eval()

							snake_case_		:       int           =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
							snake_case_		:       Any           =    model(__magic_name__       )
							self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)       )






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Any           =    self.prepare_config_and_inputs()
							snake_case_     ,				snake_case_     ,				snake_case_		:       Optional[int]           =    config_and_inputs
							snake_case_		:       Union[str, Any]           =    {'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class    __lowerCAmelCase			(     _a,	_a,	unittest.TestCase	):
			lowerCamelCase_	:	List[Any]			     = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			lowerCamelCase_	:	Optional[int]			     = (
			    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase_	:	int			     = False
			lowerCamelCase_	:	Optional[int]			     = False
			lowerCamelCase_	:	int			     = False
			lowerCamelCase_	:	Optional[int]			     = False






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    ViTMSNModelTester(self       )
							snake_case_		:       Optional[Any]           =    ConfigTester(self   ,				config_class=__magic_name__   ,				has_text_modality=__magic_name__   ,				hidden_size=37       )






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''       )
			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							pass






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       Tuple           =    self.model_tester.prepare_config_and_inputs_for_common()

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






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       Dict           =    self.model_tester.prepare_config_and_inputs_for_common()

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

											snake_case_		:       List[str]           =    ['''pixel_values''']
											self.assertListEqual(arg_names[:1]   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__       )






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__magic_name__       )






			@slow
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:       str           =    ViTMSNModel.from_pretrained(__magic_name__       )
											self.assertIsNotNone(__magic_name__       )






def 	lowerCamelCase_		(							)						->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:       int           =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
				return image





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






			@cached_property
			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''       ) if is_vision_available() else None






			@slow
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							torch.manual_seed(2       )
							snake_case_		:       List[str]           =    ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(__magic_name__       )

							snake_case_		:       str           =    self.default_image_processor
							snake_case_		:       str           =    prepare_img()
							snake_case_		:       int           =    image_processor(images=__magic_name__   ,				return_tensors='''pt'''       ).to(__magic_name__       )

							# forward pass
							with torch.no_grad():
											snake_case_		:       Optional[int]           =    model(**__magic_name__       )

							# verify the logits
							snake_case_		:       Optional[int]           =    torch.Size((1, 1000)       )
							self.assertEqual(outputs.logits.shape   ,				__magic_name__       )

							snake_case_		:       List[Any]           =    torch.tensor([-0.0_803, -0.4_454, -0.2_375]       ).to(__magic_name__       )

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






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :						int							=  logging.get_logger("""transformers.models.speecht5""")







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

       hf_model.apply_weight_norm()

       A__ 					=       checkpoint['''input_conv.weight_g''']
       A__ 					=       checkpoint['''input_conv.weight_v''']
       A__ 					=       checkpoint['''input_conv.bias''']

       for i in range(len(config.upsample_rates		)		):
              A__ 					=       checkpoint[f"""upsamples.{i}.1.weight_g"""]
              A__ 					=       checkpoint[f"""upsamples.{i}.1.weight_v"""]
              A__ 					=       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		)		):
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     A__ 					=       checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       A__ 					=       checkpoint['''output_conv.1.weight_g''']
       A__ 					=       checkpoint['''output_conv.1.weight_v''']
       A__ 					=       checkpoint['''output_conv.1.bias''']

       hf_model.remove_weight_norm()







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			str:



       """simple docstring"""

       if config_path is not None:
              A__ 					=       SpeechTaHifiGanConfig.from_pretrained(lowercase_		)
       else:
              A__ 					=       SpeechTaHifiGanConfig()

       A__ 					=       SpeechTaHifiGan(lowercase_		)

       A__ 					=       torch.load(lowercase_		)
       load_weights(orig_checkpoint['''model''']['''generator''']					,							lowercase_					,							lowercase_		)

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

       model.save_pretrained(lowercase_		)

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


if __name__ == "__main__":
  _lowerCamelCase      :						Any							=  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      :						List[str]							=  parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )


