
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()
snake_case_      :					List[Any]				      =     logging.get_logger(__name__)

snake_case_      :					Any				      =     {
    "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",
}
snake_case_      :					int				      =     [
    "lm_head",
]



def      __UpperCAmelCase    (  snake_case_							:		Optional[int]				,					snake_case_							:		int				,					snake_case_							:		Optional[int]				,					snake_case_							:		Optional[Any]				,					snake_case_							:		List[Any]				):



	'''simple docstring'''



	for attribute in key.split("."				):
		UpperCAmelCase:					Any 						=     getattr(snake_case_				,					snake_case_				)

	if weight_type is not None:
		UpperCAmelCase:					Optional[int] 						=     getattr(snake_case_				,					snake_case_				).shape
	else:
		UpperCAmelCase:					str 						=     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":
		UpperCAmelCase:					Union[str, Any] 						=     value
	elif weight_type == "weight_g":
		UpperCAmelCase:					str 						=     value
	elif weight_type == "weight_v":
		UpperCAmelCase:					str 						=     value
	elif weight_type == "bias":
		UpperCAmelCase:					str 						=     value
	else:
		UpperCAmelCase:					Union[str, Any] 						=     value

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



def      __UpperCAmelCase    (  snake_case_							:		int				,					snake_case_							:		Union[str, Any]				,					snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     []
	UpperCAmelCase:					Dict 						=     fairseq_model.state_dict()

	if not is_headless:
		UpperCAmelCase:					str 						=     hf_model.dataavec_audio.feature_extractor
		UpperCAmelCase:					Optional[Any] 						=     hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		UpperCAmelCase:					Optional[Any] 						=     hf_model.feature_extractor
		UpperCAmelCase:					Tuple 						=     hf_model.encoder.pos_conv_embed

	for name, value in fairseq_dict.items():
		UpperCAmelCase:					str 						=     False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					)
			UpperCAmelCase:					int 						=     True
		elif "pos_conv" in name:
			load_pos_conv_layer(
			    snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					)
			UpperCAmelCase:					Optional[int] 						=     True
		else:
			for key, mapped_key in MAPPING.items():
				if not is_headless:
					UpperCAmelCase:					int 						=     '''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]:
					UpperCAmelCase:					List[str] 						=     True
					if "*" in mapped_key:
						UpperCAmelCase:					int 						=     name.split(snake_case_				)[0].split("."				)[-2]
						UpperCAmelCase:					int 						=     mapped_key.replace("*"				,					snake_case_				)
					if "weight_g" in name:
						UpperCAmelCase:					List[str] 						=     '''weight_g'''
					elif "weight_v" in name:
						UpperCAmelCase:					Any 						=     '''weight_v'''
					elif "bias" in name:
						UpperCAmelCase:					Optional[int] 						=     '''bias'''
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCAmelCase:					str 						=     '''weight'''
					else:
						UpperCAmelCase:					int 						=     None
					set_recursively(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
				continue
		if not is_used:
			unused_weights.append(snake_case_				)

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



def      __UpperCAmelCase    (  snake_case_							:		int				,					snake_case_							:		Optional[Any]				):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     path.split("."				)
	return reduce(snake_case_				,					snake_case_				,					snake_case_				)



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		int				,					snake_case_							:		Any				,					snake_case_							:		List[str]				):



	'''simple docstring'''



	UpperCAmelCase:					Any 						=     access_by_string(snake_case_				,					snake_case_				)
	UpperCAmelCase:					Optional[int] 						=     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.'				)
	UpperCAmelCase:					Optional[int] 						=     fsq_value
	logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'				)



def      __UpperCAmelCase    (  snake_case_							:		Tuple				,					snake_case_							:		Optional[int]				,					snake_case_							:		Optional[int]				,					snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[Any] 						=     full_name.split("conv_layers."				)[-1]
	UpperCAmelCase:					Dict 						=     name.split("."				)
	UpperCAmelCase:					Optional[int] 						=     int(items[0]				)
	UpperCAmelCase:					Any 						=     int(items[1]				)

	UpperCAmelCase:					str 						=     name.split("."				)[-1]
	if type_id == 0:
		UpperCAmelCase:					str 						=     '''conv'''
	elif type_id == 2:
		UpperCAmelCase:					int 						=     '''layer_norm'''
	else:
		unused_weights.append(snake_case_				)
		return

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



def      __UpperCAmelCase    (  snake_case_							:		Optional[int]				,					snake_case_							:		Optional[int]				,					snake_case_							:		Tuple				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     full_name.split("pos_conv."				)[-1]
	UpperCAmelCase:					Union[str, Any] 						=     name.split("."				)
	UpperCAmelCase:					Optional[int] 						=     int(items[0]				)
	UpperCAmelCase:					int 						=     int(items[1]				)

	UpperCAmelCase:					Tuple 						=     name.split("."				)[-1]
	if type_id != 0:
		unused_weights.append(snake_case_				)
		return
	else:
		UpperCAmelCase:					List[Any] 						=     '''conv'''

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



@torch.no_grad()
def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Union[str, Any]=None				,					snake_case_							:		List[Any]=None				,					snake_case_							:		int=True				):



	'''simple docstring'''



	if config_path is not None:
		UpperCAmelCase:					Union[str, Any] 						=     DataaVecAudioConfig.from_pretrained(snake_case_				)
	else:
		UpperCAmelCase:					Optional[int] 						=     DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		UpperCAmelCase:					Optional[Any] 						=     DataaVecAudioModel(snake_case_				)
		UpperCAmelCase:					int 						=     os.path.dirname(snake_case_				)

		UpperCAmelCase:					Optional[int] 						=     torch.load(snake_case_				)
		UpperCAmelCase:					Any 						=     state_dict['''model'''].pop("final_proj.0.weight"				)
		UpperCAmelCase:					int 						=     state_dict['''model'''].pop("final_proj.0.bias"				)
		UpperCAmelCase:					Optional[Any] 						=     os.path.join(snake_case_				,					"converted.pt"				)
		torch.save(snake_case_				,					snake_case_				)
	else:
		UpperCAmelCase:					Union[str, Any] 						=     DataaVecAudioForCTC(snake_case_				)
		UpperCAmelCase:					str 						=     checkpoint_path

	def load_dataavec(snake_case_							:		Optional[int]				):
		UpperCAmelCase:					int 						=     fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
		return model[0].eval()

	UpperCAmelCase:					List[Any] 						=     load_dataavec(snake_case_				)

	recursively_load_weights(snake_case_				,					snake_case_				,					not is_finetuned				)

	UpperCAmelCase:					Optional[Any] 						=     WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"				)

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

	UpperCAmelCase:					Optional[Any] 						=     processor(snake_case_				,					return_tensors="pt"				,					padding=snake_case_				)

	UpperCAmelCase:					Optional[int] 						=     inputs.input_values
	UpperCAmelCase:					List[Any] 						=     inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		UpperCAmelCase:					List[str] 						=     model(source=snake_case_				,					padding_mask=(1 - attention_mask)				,					mask=snake_case_				,					features_only=snake_case_				)[
		    '''encoder_out'''
		].transpose(0				,					1				)
		UpperCAmelCase:					Tuple 						=     hf_wavavec(snake_case_				,					attention_mask=snake_case_				)['''logits''']

		UpperCAmelCase:					int 						=     torch.argmax(snake_case_				,					dim=-1				)
		UpperCAmelCase:					List[str] 						=     processor.batch_decode(snake_case_				)

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

	print(our_output.shape				,					their_output.shape				)
	UpperCAmelCase:					int 						=     torch.max(torch.abs(our_output - their_output				)				).item()
	print(F'max_absolute_diff = {max_absolute_diff}'				)  # ~ 1e-7
	UpperCAmelCase:					int 						=     torch.allclose(snake_case_				,					snake_case_				,					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(snake_case_				)

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


if __name__ == "__main__":
	snake_case_      :					Union[str, Any]				      =     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'
	)
	snake_case_      :					Optional[Any]				      =     parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)




import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     str 							= np.exp(SCREAMING_SNAKE_CASE  )

 # Add up the all the exponentials
 UpperCamelCase__       :     Optional[int] 							= np.sum(SCREAMING_SNAKE_CASE  )

 # Divide every exponent by the sum of all exponents
 UpperCamelCase__       :     Optional[Any] 							= exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
       print(softmax((0,)))


