



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()
UpperCamelCase_	       =   logging.get_logger(__name__)

UpperCamelCase_	       =   {
    "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",
}
UpperCamelCase_	       =   [
    "lm_head",
]







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Optional[Any]:



						'''simple docstring'''






						for attribute in key.split('''.'''	):
												UpperCAmelCase_     							=     getattr(__UpperCAmelCase							,					__UpperCAmelCase	)

						if weight_type is not None:
												UpperCAmelCase_     							=     getattr(__UpperCAmelCase							,					__UpperCAmelCase	).shape
						else:
												UpperCAmelCase_     							=     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_     							=     value
						elif weight_type == "weight_g":
												UpperCAmelCase_     							=     value
						elif weight_type == "weight_v":
												UpperCAmelCase_     							=     value
						elif weight_type == "bias":
												UpperCAmelCase_     							=     value
						else:
												UpperCAmelCase_     							=     value

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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     []
						UpperCAmelCase_     							=     fairseq_model.state_dict()

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

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

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

						logger.warning(f"Unused weights: {unused_weights}"	)







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Dict:



						'''simple docstring'''






						UpperCAmelCase_     							=     path.split('''.'''	)
						return reduce(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     access_by_string(__UpperCAmelCase							,					__UpperCAmelCase	)
						UpperCAmelCase_     							=     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_     							=     fsq_value
						logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."	)







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Any:



						'''simple docstring'''






						UpperCAmelCase_     							=     full_name.split('''conv_layers.'''	)[-1]
						UpperCAmelCase_     							=     name.split('''.'''	)
						UpperCAmelCase_     							=     int(items[0]	)
						UpperCAmelCase_     							=     int(items[1]	)

						UpperCAmelCase_     							=     name.split('''.'''	)[-1]
						if type_id == 0:
												UpperCAmelCase_     							=     '''conv'''
						elif type_id == 2:
												UpperCAmelCase_     							=     '''layer_norm'''
						else:
												unused_weights.append(__UpperCAmelCase	)
												return

						set_weights(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					f"conv_layers.{layer_id}.{layer_type}.{weight_type}"	)







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     full_name.split('''pos_conv.'''	)[-1]
						UpperCAmelCase_     							=     name.split('''.'''	)
						UpperCAmelCase_     							=     int(items[0]	)
						UpperCAmelCase_     							=     int(items[1]	)

						UpperCAmelCase_     							=     name.split('''.'''	)[-1]
						if type_id != 0:
												unused_weights.append(__UpperCAmelCase	)
												return
						else:
												UpperCAmelCase_     							=     '''conv'''

						set_weights(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					f"layers.{layer_id}.{layer_type}.{weight_type}"	)







@torch.no_grad()
def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=True	)       ->       Tuple:



						'''simple docstring'''






						if config_path is not None:
												UpperCAmelCase_     							=     DataaVecAudioConfig.from_pretrained(__UpperCAmelCase	)
						else:
												UpperCAmelCase_     							=     DataaVecAudioConfig()

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

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

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

						UpperCAmelCase_     							=     load_dataavec(__UpperCAmelCase	)

						recursively_load_weights(__UpperCAmelCase							,					__UpperCAmelCase							,					not is_finetuned	)

						UpperCAmelCase_     							=     WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''	)

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

						UpperCAmelCase_     							=     processor(__UpperCAmelCase							,					return_tensors='''pt'''							,					padding=__UpperCAmelCase	)

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

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

												UpperCAmelCase_     							=     torch.argmax(__UpperCAmelCase							,					dim=-1	)
												UpperCAmelCase_     							=     processor.batch_decode(__UpperCAmelCase	)

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

						print(our_output.shape							,					their_output.shape	)
						UpperCAmelCase_     							=     torch.max(torch.abs(our_output - their_output	)	).item()
						print(f"max_absolute_diff = {max_absolute_diff}"	)  # ~ 1e-7
						UpperCAmelCase_     							=     torch.allclose(__UpperCAmelCase							,					__UpperCAmelCase							,					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(__UpperCAmelCase	)

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


if __name__ == "__main__":
							UpperCamelCase_	       =   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"
							)
							UpperCamelCase_	       =   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"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


