"""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()
a							=logging.get_logger(__name__)

a							={
    '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',
}
a							=[
    'lm_head',
]





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						for attribute in key.split("."				):
												lowerCamelCase__					=getattr(__lowerCAmelCase   ,    __lowerCAmelCase				)

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

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=[]
						lowerCamelCase__					=fairseq_model.state_dict()

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

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

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

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=path.split("."				)
						return reduce(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=access_by_string(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=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.'''				)
						lowerCamelCase__					=fsq_value
						logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					=full_name.split("conv_layers."				)[-1]
						lowerCamelCase__					=name.split("."				)
						lowerCamelCase__					=int(items[0]				)
						lowerCamelCase__					=int(items[1]				)

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

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=full_name.split("pos_conv."				)[-1]
						lowerCamelCase__					=name.split("."				)
						lowerCamelCase__					=int(items[0]				)
						lowerCamelCase__					=int(items[1]				)

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

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





@torch.no_grad()
def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase=None   ,    __lowerCAmelCase=None   ,    __lowerCAmelCase=True				)							->					Tuple:




						'''simple docstring'''



						if config_path is not None:
												lowerCamelCase__					=DataaVecAudioConfig.from_pretrained(__lowerCAmelCase				)
						else:
												lowerCamelCase__					=DataaVecAudioConfig()

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

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

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

						lowerCamelCase__					=load_dataavec(__lowerCAmelCase				)

						recursively_load_weights(__lowerCAmelCase   ,    __lowerCAmelCase   ,    not is_finetuned				)

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

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

						lowerCamelCase__					=processor(__lowerCAmelCase   ,    return_tensors="pt"   ,    padding=__lowerCAmelCase				)

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

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

												lowerCamelCase__					=torch.argmax(__lowerCAmelCase   ,    dim=-1				)
												lowerCamelCase__					=processor.batch_decode(__lowerCAmelCase				)

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

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

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


if __name__ == "__main__":
	a							=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'
	)
	a							=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'''







from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(lowercase	)	):
						# for loop iterates over number of elements in inner layer of items
						for j in range(len(items[i]	)	):
									# appending squared differences to 'squared_diff' list
									squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# for loop iterates over number of elements in list
			for i in range(len(lowercase	)	):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]	)	):
									__lowerCAmelCase											=				[]  # to store all discriminant values of each item as a list
									# for loop iterates over number of classes we have in our dataset
									for k in range(len(lowercase	)	):
												# appending values of discriminants for each class to 'temp' list
												temp.append(
												    x_items[i][j] * (means[k] / variance)
												    - (means[k] ** 2 / (2 * variance))
												    + log(probabilities[k]	)	)
									# appending discriminant values of each item to 'results' list
									results.append(lowercase	)

			return [result.index(max(lowercase	)	) for result in results]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				sum(1 for i, j in zip(lowercase					,	lowercase	) if i == j	)
			# percentage of accuracy equals to number of correct predictions divided by number
			# of all data and multiplied by 100
			return (correct / len(lowercase	)) * 100


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				input_type(input(lowercase	).strip() or default	)
									if condition(lowercase	):
												return user_input
									else:
												print(f'{user_input}: {err_msg}'	)
												continue
						except ValueError:
									print(
									    f'{user_input}: Incorrect input type, expected {input_type.__name__!r}'	)





def    _lowerCAmelCase						(						)					->					List[str]:
			while True:
						print(""" Linear Discriminant Analysis """.center(50					,	"""*"""	)	)
						print("""*""" * 50					,	"""\n"""	)
						print("""First of all we should specify the number of classes that"""	)
						print("""we want to generate as training dataset"""	)
						# Trying to get number of classes
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg="""Enter the number of classes (Data Groupings): """					,	err_msg="""Number of classes should be positive!"""					,	)

						print("""-""" * 100	)

						# Trying to get the value of standard deviation
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x >= 0					,	input_msg=(
						        """Enter the value of standard deviation"""
						        """(Default value is 1.0 for all classes): """
						    )					,	err_msg="""Standard deviation should not be negative!"""					,	default="""1.0"""					,	)

						print("""-""" * 100	)

						# Trying to get number of instances in classes and theirs means to generate
						# dataset
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg=(f'Enter The number of instances for class_{i+1}: ')					,	err_msg="""Number of instances should be positive!"""					,	)
									counts.append(lowercase	)
						print("""-""" * 100	)

						# An empty list to store values of user-entered means of classes
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	input_msg=(f'Enter the value of mean for class_{a+1}: ')					,	err_msg="""This is an invalid value."""					,	)
									user_means.append(lowercase	)
						print("""-""" * 100	)

						print("""Standard deviation: """					,	lowercase	)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(lowercase					,	1	):
									print(f'Number of instances in class_{i} is: {count}'	)
						print("""-""" * 100	)

						# print out mean values of classes separated line
						for i, user_mean in enumerate(lowercase					,	1	):
									print(f'Mean of class_{i} is: {user_mean}'	)
						print("""-""" * 100	)

						# Generating training dataset drawn from gaussian distribution
						__lowerCAmelCase											=				[
						    gaussian_distribution(user_means[j]					,	lowercase					,	counts[j]	)
						    for j in range(lowercase	)
						]
						print("""Generated Normal Distribution: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Generating Ys to detecting corresponding classes
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[calculate_mean(counts[k]					,	x[k]	) for k in range(lowercase	)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(lowercase					,	1	):
									print(f'Actual(Real) mean of class_{i} is: {actual_mean}'	)
						print("""-""" * 100	)

						# Calculating the value of probabilities for each class
						__lowerCAmelCase											=				[
						    calculate_probabilities(counts[i]					,	sum(lowercase	)	) for i in range(lowercase	)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(lowercase					,	1	):
									print(f'Probability of class_{i} is: {probability}'	)
						print("""-""" * 100	)

						# Calculating the values of variance for each class
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				predict_y_values(lowercase					,	lowercase					,	lowercase					,	lowercase	)
						print("""-""" * 100	)

						# Calculating Accuracy of the model
						print(f'Accuracy: {accuracy(lowercase					,	lowercase	)}'	)
						print("""-""" * 100	)
						print(""" DONE """.center(100					,	"""+"""	)	)

						if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
									print("""\n""" + """GoodBye!""".center(100					,	"""-"""	) + """\n"""	)
									break
						system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
	main()

