


'''simple docstring'''




import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    MPNetForMaskedLM,
				    MPNetForMultipleChoice,
				    MPNetForQuestionAnswering,
				    MPNetForSequenceClassification,
				    MPNetForTokenClassification,
				    MPNetModel,
				)



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=False ,					snake_case__=True ,					snake_case__=99 ,					snake_case__=64 ,					snake_case__=5 ,					snake_case__=4 ,					snake_case__=64 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=16 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=3 ,					snake_case__=4 ,					snake_case__=None ,					):
		lowerCAmelCase					:    int      							= parent
		lowerCAmelCase					:    Any      							= batch_size
		lowerCAmelCase					:    Dict      							= seq_length
		lowerCAmelCase					:    List[Any]      							= is_training
		lowerCAmelCase					:    List[str]      							= use_input_mask
		lowerCAmelCase					:    Dict      							= use_token_type_ids
		lowerCAmelCase					:    Tuple      							= use_labels
		lowerCAmelCase					:    List[Any]      							= vocab_size
		lowerCAmelCase					:    List[str]      							= hidden_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[str]      							= num_attention_heads
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Optional[Any]      							= hidden_dropout_prob
		lowerCAmelCase					:    int      							= attention_probs_dropout_prob
		lowerCAmelCase					:    int      							= max_position_embeddings
		lowerCAmelCase					:    Dict      							= type_vocab_size
		lowerCAmelCase					:    List[str]      							= type_sequence_label_size
		lowerCAmelCase					:    Optional[Any]      							= initializer_range
		lowerCAmelCase					:    Tuple      							= num_labels
		lowerCAmelCase					:    List[Any]      							= num_choices
		lowerCAmelCase					:    List[str]      							= scope



	def 				lowercase     (					self    ):
		return MPNetConfig.from_pretrained('microsoft/mpnet-base'    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

		lowerCAmelCase					:    Dict      							= None
		if self.use_input_mask:
			lowerCAmelCase					:    Optional[Any]      							= random_attention_mask([self.batch_size, self.seq_length]    )

		lowerCAmelCase					:    str      							= None
		lowerCAmelCase					:    str      							= None
		lowerCAmelCase					:    Optional[int]      							= None
		if self.use_labels:
			lowerCAmelCase					:    Optional[int]      							= ids_tensor([self.batch_size] ,					self.type_sequence_label_size    )
			lowerCAmelCase					:    int      							= ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels    )
			lowerCAmelCase					:    List[str]      							= ids_tensor([self.batch_size] ,					self.num_choices    )

		lowerCAmelCase					:    Optional[int]      							= self.get_config()
		return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



	def 				lowercase     (					self    ):
		return MPNetConfig(
		    vocab_size=self.vocab_size ,					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 ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= MPNetModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Dict      							= model(snake_case__    )
		self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)    )
		self.parent.assertEqual(result.pooler_output.shape ,					(self.batch_size, self.hidden_size)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[str]      							= MPNetForQuestionAnswering(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[Any]      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					start_positions=snake_case__ ,					end_positions=snake_case__ ,					)
		self.parent.assertEqual(result.start_logits.shape ,					(self.batch_size, self.seq_length)    )
		self.parent.assertEqual(result.end_logits.shape ,					(self.batch_size, self.seq_length)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    str      							= self.num_labels
		lowerCAmelCase					:    Any      							= MPNetForSequenceClassification(snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Optional[int]      							= model(snake_case__ ,					attention_mask=snake_case__ ,					labels=snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= self.num_choices
		lowerCAmelCase					:    Optional[int]      							= MPNetForMultipleChoice(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    int      							= input_ids.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    Tuple      							= input_mask.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    Tuple      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					labels=snake_case__ ,					)
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Tuple      							= self.num_labels
		lowerCAmelCase					:    Optional[int]      							= MPNetForTokenClassification(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[Any]      							= model(snake_case__ ,					attention_mask=snake_case__ ,					labels=snake_case__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.prepare_config_and_inputs()
		((lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase))					:    Optional[int]      							= config_and_inputs
		lowerCAmelCase					:    List[Any]      							= {'input_ids': input_ids, 'attention_mask': input_mask}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = (
	    (
	        MPNetForMaskedLM,
	        MPNetForMultipleChoice,
	        MPNetForQuestionAnswering,
	        MPNetForSequenceClassification,
	        MPNetForTokenClassification,
	        MPNetModel,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCamelCase   :					Dict			      = (
	    {
	        """feature-extraction""": MPNetModel,
	        """fill-mask""": MPNetForMaskedLM,
	        """question-answering""": MPNetForQuestionAnswering,
	        """text-classification""": MPNetForSequenceClassification,
	        """token-classification""": MPNetForTokenClassification,
	        """zero-shot""": MPNetForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCamelCase   :					List[Any]			      = False
	_lowerCamelCase   :					Union[str, Any]			      = True



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= MPNetModelTester(self    )
		lowerCAmelCase					:    int      							= ConfigTester(self ,					config_class=snake_case__ ,					hidden_size=37    )



	def 				lowercase     (					self    ):
		self.config_tester.run_common_tests()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mpnet_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case__    )



@require_torch
class  lowerCAmelCase				(				unittest.TestCase							):



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= MPNetModel.from_pretrained('microsoft/mpnet-base'    )
		lowerCAmelCase					:    str      							= torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
		lowerCAmelCase					:    Tuple      							= model(snake_case__    )[0]
		lowerCAmelCase					:    Optional[int]      							= torch.Size((1, 11, 768)    )
		self.assertEqual(output.shape ,					snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor(
		    [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]    )
		# compare the actual values for a slice.
		self.assertTrue(torch.allclose(output[:, :3, :3] ,					snake_case__ ,					atol=1e-4    )    )




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"upsamples.{i}.1.weight_g"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.weight_v"]
		lowerCAmelCase					:    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					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
			lowerCAmelCase					:    str      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





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

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    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__":
				_lowerCAmelCase  :		List[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  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
