
"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 	_lowercase       (    __snake_case		):
	lowercase_				=     'marian'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




	def __init__(			self		,			UpperCAmelCase_=58101		,			UpperCAmelCase_=None		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=12		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=16		,			UpperCAmelCase_=12		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=16		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=58100		,			UpperCAmelCase_=False		,			UpperCAmelCase_=58100		,			UpperCAmelCase_=0		,			UpperCAmelCase_=0		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Dict:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      Optional[Any]          =   decoder_vocab_size or vocab_size
			lowerCamelCase					:      Union[str, Any]          =   max_position_embeddings
			lowerCamelCase					:      Any          =   d_model
			lowerCamelCase					:      Union[str, Any]          =   encoder_ffn_dim
			lowerCamelCase					:      List[Any]          =   encoder_layers
			lowerCamelCase					:      Optional[int]          =   encoder_attention_heads
			lowerCamelCase					:      Dict          =   decoder_ffn_dim
			lowerCamelCase					:      Union[str, Any]          =   decoder_layers
			lowerCamelCase					:      List[Any]          =   decoder_attention_heads
			lowerCamelCase					:      Optional[int]          =   dropout
			lowerCamelCase					:      Union[str, Any]          =   attention_dropout
			lowerCamelCase					:      Optional[int]          =   activation_dropout
			lowerCamelCase					:      Tuple          =   activation_function
			lowerCamelCase					:      Tuple          =   init_std
			lowerCamelCase					:      Optional[int]          =   encoder_layerdrop
			lowerCamelCase					:      str          =   decoder_layerdrop
			lowerCamelCase					:      Optional[int]          =   use_cache
			lowerCamelCase					:      str          =   encoder_layers
			lowerCamelCase					:      str          =   scale_embedding  # scale factor will be sqrt(d_model) if True
			lowerCamelCase					:      Union[str, Any]          =   share_encoder_decoder_embeddings
			super().__init__(
			    pad_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			is_encoder_decoder=UpperCAmelCase_		,			decoder_start_token_id=UpperCAmelCase_		,			forced_eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_		,			)






class 	_lowercase       (    __snake_case		):




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      int          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ] )

					if self.use_past:
							lowerCamelCase					:      List[Any]          =   {0: 'batch'}
							lowerCamelCase					:      Union[str, Any]          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
					else:
							lowerCamelCase					:      Union[str, Any]          =   {0: 'batch', 1: 'decoder_sequence'}
							lowerCamelCase					:      int          =   {0: 'batch', 1: 'decoder_sequence'}

					if self.use_past:
							self.fill_with_past_key_values_(UpperCAmelCase_		,			direction='inputs' )
			elif self.task == "causal-lm":
					# TODO: figure this case out.
					lowerCamelCase					:      List[Any]          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					    ] )
					if self.use_past:
							lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   self.num_layers
							for i in range(UpperCAmelCase_ ):
									lowerCamelCase					:      Union[str, Any]          =   {0: 'batch', 2: 'past_sequence + sequence'}
									lowerCamelCase					:      List[Any]          =   {0: 'batch', 2: 'past_sequence + sequence'}
			else:
					lowerCamelCase					:      Tuple          =   OrderedDict(
					    [
					        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
					        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
					        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
					        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
					    ] )

			return common_inputs




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      int          =   super().outputs
			else:
					lowerCamelCase					:      str          =   super(UpperCAmelCase_		,			self ).outputs
					if self.use_past:
							lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   self.num_layers
							for i in range(UpperCAmelCase_ ):
									lowerCamelCase					:      List[Any]          =   {0: 'batch', 2: 'past_sequence + sequence'}
									lowerCamelCase					:      Optional[Any]          =   {0: 'batch', 2: 'past_sequence + sequence'}
			return common_outputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			lowerCamelCase					:      Tuple          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

			# Generate decoder inputs
			lowerCamelCase					:      str          =   seq_length if not self.use_past else 1
			lowerCamelCase					:      Tuple          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
			lowerCamelCase					:      Union[str, Any]          =   dict(**UpperCAmelCase_		,			**UpperCAmelCase_ )

			if self.use_past:
					if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
					else:
							import torch
					lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   common_inputs['input_ids'].shape
					lowerCamelCase					:      List[Any]          =   common_inputs['decoder_input_ids'].shape[1]
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self.num_attention_heads
					lowerCamelCase					:      Union[str, Any]          =   (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase					:      Dict          =   decoder_seq_length + 3
					lowerCamelCase					:      Any          =   (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					lowerCamelCase					:      Optional[int]          =   torch.cat(
					    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase_		,			UpperCAmelCase_ )]		,			dim=1 )

					lowerCamelCase					:      Dict          =   []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   self.num_layers
					lowerCamelCase					:      Dict          =   min(UpperCAmelCase_		,			UpperCAmelCase_ )
					lowerCamelCase					:      Union[str, Any]          =   max(UpperCAmelCase_		,			UpperCAmelCase_ ) - min_num_layers
					lowerCamelCase					:      Optional[int]          =   'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

					for _ in range(UpperCAmelCase_ ):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(UpperCAmelCase_ ),
							        torch.zeros(UpperCAmelCase_ ),
							        torch.zeros(UpperCAmelCase_ ),
							        torch.zeros(UpperCAmelCase_ ),
							    ) )
					# TODO: test this.
					lowerCamelCase					:      Tuple          =   encoder_shape if remaining_side_name == 'encoder' else decoder_shape
					for _ in range(UpperCAmelCase_		,			UpperCAmelCase_ ):
							common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_ ), torch.zeros(UpperCAmelCase_ )) )
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			lowerCamelCase					:      List[str]          =   self._generate_dummy_inputs_for_encoder_and_decoder(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

			if self.use_past:
					if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
					else:
							import torch
					lowerCamelCase     ,   lowerCamelCase					:      Any          =   common_inputs['input_ids'].shape
					# Not using the same length for past_key_values
					lowerCamelCase					:      List[str]          =   seqlen + 2
					lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   self.num_layers
					lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   self.num_attention_heads
					lowerCamelCase					:      List[str]          =   (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					lowerCamelCase					:      str          =   common_inputs['attention_mask'].dtype
					lowerCamelCase					:      Union[str, Any]          =   torch.cat(
					    [common_inputs['attention_mask'], torch.ones(UpperCAmelCase_		,			UpperCAmelCase_		,			dtype=UpperCAmelCase_ )]		,			dim=1 )
					lowerCamelCase					:      Union[str, Any]          =   [
					    (torch.zeros(UpperCAmelCase_ ), torch.zeros(UpperCAmelCase_ )) for _ in range(UpperCAmelCase_ )
					]
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			# Copied from OnnxConfig.generate_dummy_inputs
			# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
			# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
			lowerCamelCase					:      List[str]          =   compute_effective_axis_dimension(
			    UpperCAmelCase_		,			fixed_dimension=OnnxConfig.default_fixed_batch		,			num_token_to_add=0 )

			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			lowerCamelCase					:      int          =   tokenizer.num_special_tokens_to_add(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   compute_effective_axis_dimension(
			    UpperCAmelCase_		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=UpperCAmelCase_ )

			# Generate dummy inputs according to compute batch and sequence
			lowerCamelCase					:      Tuple          =   [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
			lowerCamelCase					:      str          =   dict(tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_ ) )
			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = -1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			)		->							Mapping[str, Any]:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      Dict          =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    UpperCAmelCase_		,			batch_size=UpperCAmelCase_		,			seq_length=UpperCAmelCase_		,			is_pair=UpperCAmelCase_		,			framework=UpperCAmelCase_ )

			else:
					lowerCamelCase					:      Optional[int]          =   self._generate_dummy_inputs_for_causal_lm(
					    UpperCAmelCase_		,			batch_size=UpperCAmelCase_		,			seq_length=UpperCAmelCase_		,			is_pair=UpperCAmelCase_		,			framework=UpperCAmelCase_ )

			return common_inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase					:      str          =   super()._flatten_past_key_values_(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   super(UpperCAmelCase_		,			self )._flatten_past_key_values_(
					    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							float:
			return 1E-4








"""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,
					)






