
"""simple docstring"""






import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_lowercase							 =							[
    '''lm_head''',
]



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->List[Any]:
			for attribute in key.split("."			):
						__lowercase						      =					getattr(__magic_name__ ,      __magic_name__			)

			if weight_type is not None:
						__lowercase						      =					getattr(__magic_name__ ,      __magic_name__			).shape
			else:
						__lowercase						      =					hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
						    F''' {value.shape} for {full_name}'''			)

			if weight_type == "weight":
						__lowercase						      =					value
			elif weight_type == "weight_g":
						__lowercase						      =					value
			elif weight_type == "weight_v":
						__lowercase						      =					value
			elif weight_type == "bias":
						__lowercase						      =					value
			else:
						__lowercase						      =					value

			logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__			)	->List[Any]:
			__lowercase						      =					[]
			__lowercase						      =					fairseq_model.state_dict()

			if not is_headless:
						__lowercase						      =					hf_model.dataavec_audio.feature_extractor
						__lowercase						      =					hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						__lowercase						      =					hf_model.feature_extractor
						__lowercase						      =					hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						__lowercase						      =					False
						if "conv_layers" in name:
									load_conv_layer(
									    __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      )
									__lowercase						      =					True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      )
									__lowercase						      =					True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															__lowercase						      =					"data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
															__lowercase						      =					True
															if "*" in mapped_key:
																		__lowercase						      =					name.split(__magic_name__			)[0].split("."			)[-2]
																		__lowercase						      =					mapped_key.replace("*" ,      __magic_name__			)
															if "weight_g" in name:
																		__lowercase						      =					"weight_g"
															elif "weight_v" in name:
																		__lowercase						      =					"weight_v"
															elif "bias" in name:
																		__lowercase						      =					"bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		__lowercase						      =					"weight"
															else:
																		__lowercase						      =					None
															set_recursively(__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)
												continue
						if not is_used:
									unused_weights.append(__magic_name__			)

			logger.warning(F'''Unused weights: {unused_weights}'''			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->List[Any]:
			__lowercase						      =					path.split("."			)
			return reduce(__magic_name__ ,      __magic_name__ ,      __magic_name__			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->int:
			__lowercase						      =					access_by_string(__magic_name__ ,      __magic_name__			)
			__lowercase						      =					hf_weight.data

			if fsq_value.shape != hf_value.shape:
						raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''			)
			__lowercase						      =					fsq_value
			logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Dict:
			__lowercase						      =					full_name.split("conv_layers."			)[-1]
			__lowercase						      =					name.split("."			)
			__lowercase						      =					int(items[0]			)
			__lowercase						      =					int(items[1]			)

			__lowercase						      =					name.split("."			)[-1]
			if type_id == 0:
						__lowercase						      =					"conv"
			elif type_id == 2:
						__lowercase						      =					"layer_norm"
			else:
						unused_weights.append(__magic_name__			)
						return

			set_weights(__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Tuple:
			__lowercase						      =					full_name.split("pos_conv."			)[-1]
			__lowercase						      =					name.split("."			)
			__lowercase						      =					int(items[0]			)
			__lowercase						      =					int(items[1]			)

			__lowercase						      =					name.split("."			)[-1]
			if type_id != 0:
						unused_weights.append(__magic_name__			)
						return
			else:
						__lowercase						      =					"conv"

			set_weights(__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      F'''layers.{layer_id}.{layer_type}.{weight_type}'''			)



@torch.no_grad()
def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=None ,      __magic_name__=True			)	->Optional[Any]:
			if config_path is not None:
						__lowercase						      =					DataaVecAudioConfig.from_pretrained(__magic_name__			)
			else:
						__lowercase						      =					DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						__lowercase						      =					DataaVecAudioModel(__magic_name__			)
						__lowercase						      =					os.path.dirname(__magic_name__			)

						__lowercase						      =					torch.load(__magic_name__			)
						__lowercase						      =					state_dict["model"].pop("final_proj.0.weight"			)
						__lowercase						      =					state_dict["model"].pop("final_proj.0.bias"			)
						__lowercase						      =					os.path.join(__magic_name__ ,      "converted.pt"			)
						torch.save(__magic_name__ ,      __magic_name__			)
			else:
						__lowercase						      =					DataaVecAudioForCTC(__magic_name__			)
						__lowercase						      =					checkpoint_path

			def load_dataavec(__magic_name__			):
						__lowercase      ,				__lowercase      ,				__lowercase						      =					fairseq.checkpoint_utils.load_model_ensemble_and_task([path]			)
						return model[0].eval()

			__lowercase						      =					load_dataavec(__magic_name__			)

			recursively_load_weights(__magic_name__ ,      __magic_name__ ,      not is_finetuned			)

			__lowercase						      =					WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"			)

			__lowercase						      =					load_dataset("patrickvonplaten/librispeech_asr_dummy" ,      "clean" ,      split="validation"			)
			__lowercase						      =					[x["array"] for x in ds[:4]["audio"]]

			__lowercase						      =					processor(__magic_name__ ,      return_tensors="pt" ,      padding=__magic_name__			)

			__lowercase						      =					inputs.input_values
			__lowercase						      =					inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

			hf_wavavec.eval()
			model.eval()
			if is_finetuned:
						__lowercase						      =					model(source=__magic_name__ ,      padding_mask=(1 - attention_mask) ,      mask=__magic_name__ ,      features_only=__magic_name__			)[
						    "encoder_out"
						].transpose(0 ,      1			)
						__lowercase						      =					hf_wavavec(__magic_name__ ,      attention_mask=__magic_name__			)["logits"]

						__lowercase						      =					torch.argmax(__magic_name__ ,      dim=-1			)
						__lowercase						      =					processor.batch_decode(__magic_name__			)

						print(F'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''			)
			else:
						__lowercase						      =					model(source=__magic_name__ ,      padding_mask=(1 - attention_mask) ,      mask=__magic_name__ ,      features_only=__magic_name__			)[
						    "layer_results"
						][-1][0].transpose(0 ,      1			)
						__lowercase						      =					hf_wavavec(__magic_name__ ,      attention_mask=__magic_name__			)["last_hidden_state"]

			print(our_output.shape ,      their_output.shape			)
			__lowercase						      =					torch.max(torch.abs(our_output - their_output			)			).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
			__lowercase						      =					torch.allclose(__magic_name__ ,      __magic_name__ ,      atol=1E-3			)
			print("Do both models output the same tensors?" ,      "🔥" if success else "💩"			)
			if not success:
						raise Exception("Something went wRoNg"			)

			hf_wavavec.save_pretrained(__magic_name__			)

			if is_finetuned:
						processor.save_pretrained(__magic_name__			)
			else:
						processor.feature_extractor.save_pretrained(__magic_name__			)


if __name__ == "__main__":
							_lowercase							 =							argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							_lowercase							 =							parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)







"""simple docstring"""






class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					data
										__lowercase						      =					None
										__lowercase						      =					None





							def __str__(    self					) ->       str:




										'''simple docstring'''
										return f'''{self.data}'''





class 				__a    :



							'''simple docstring'''







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




										'''simple docstring'''
										__lowercase						      =					None
										__lowercase						      =					None
							def __iter__(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.head
										while node:
													yield node.data
													__lowercase						      =					node.next
							def __str__(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return "->".join([str(_lowerCamelCase					) for item in self]					)
							def __len__(    self					) ->       Dict:




										'''simple docstring'''
										return sum(1 for _ in self					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										self.insert_at_nth(0     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       List[str]:




										'''simple docstring'''
										self.insert_at_nth(len(self					)     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length:
													raise IndexError("list index out of range"					)
										__lowercase						      =					Node(_lowerCamelCase					)
										if self.head is None:
													__lowercase						      =					__lowercase						      =					new_node
										elif index == 0:
													__lowercase						      =					new_node
													__lowercase						      =					self.head
													__lowercase						      =					new_node
										elif index == length:
													__lowercase						      =					new_node
													__lowercase						      =					self.tail
													__lowercase						      =					new_node
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					new_node
													__lowercase						      =					temp.previous
													__lowercase						      =					temp
													__lowercase						      =					new_node
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return self.delete_at_nth(0					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										return self.delete_at_nth(len(self					) - 1					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					len(self					)

										if not 0 <= index <= length - 1:
													raise IndexError("list index out of range"					)
										__lowercase						      =					self.head  # default first node
										if length == 1:
													__lowercase						      =					__lowercase						      =					None
										elif index == 0:
													__lowercase						      =					self.head.next
													__lowercase						      =					None
										elif index == length - 1:
													__lowercase						      =					self.tail
													__lowercase						      =					self.tail.previous
													__lowercase						      =					None
										else:
													__lowercase						      =					self.head
													for _ in range(0     ,						_lowerCamelCase					):
																__lowercase						      =					temp.next
													__lowercase						      =					temp
													__lowercase						      =					temp.previous
													__lowercase						      =					temp.next
										return delete_node.data
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.head

										while current.data != data:  # Find the position to delete
													if current.next:
																__lowercase						      =					current.next
													else:  # We have reached the end an no value matches
																raise ValueError("No data matching given value"					)

										if current == self.head:
													self.delete_head()

										elif current == self.tail:
													self.delete_tail()

										else:  # Before: 1 <--> 2(current) <--> 3
													__lowercase						      =					current.next  # 1 --> 3
													__lowercase						      =					current.previous  # 1 <--> 3
										return data





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




										'''simple docstring'''
										return len(self					) == 0





def     lowerCAmelCase__ (		)	->None:
			__lowercase						      =					DoublyLinkedList()
			assert linked_list.is_empty() is True
			assert str(__magic_name__			) == ""

			try:
						linked_list.delete_head()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			try:
						linked_list.delete_tail()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			for i in range(1_0			):
						assert len(__magic_name__			) == i
						linked_list.insert_at_nth(__magic_name__ ,      i + 1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_1			)			)

			linked_list.insert_at_head(0			)
			linked_list.insert_at_tail(1_1			)
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(0 ,      1_2			)			)

			assert linked_list.delete_head() == 0
			assert linked_list.delete_at_nth(9			) == 1_0
			assert linked_list.delete_tail() == 1_1
			assert len(__magic_name__			) == 9
			assert str(__magic_name__			) == "->".join(str(__magic_name__			) for i in range(1 ,      1_0			)			)


if __name__ == "__main__":
					from doctest import testmod

					testmod()





