"""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()
lowerCAmelCase__    :    Any			=      logging.get_logger(__name__)

lowerCAmelCase__    :    List[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',
}
lowerCAmelCase__    :    Any			=      [
    'lm_head',
]



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				for attribute in key.split('.'			):
								UpperCAmelCase__			 =				getattr(lowerCamelCase   ,     lowerCamelCase			)

				if weight_type is not None:
								UpperCAmelCase__			 =				getattr(lowerCamelCase   ,     lowerCamelCase			).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_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				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(
												    lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     )
												UpperCAmelCase__			 =				True
								elif "pos_conv" in name:
												load_pos_conv_layer(
												    lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     )
												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(lowerCamelCase			)[0].split('.'			)[-2]
																								UpperCAmelCase__			 =				mapped_key.replace('*'   ,     lowerCamelCase			)
																				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(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase			)

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



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				path.split('.'			)
				return reduce(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				access_by_string(lowerCamelCase   ,     lowerCamelCase			)
				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_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				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(lowerCamelCase			)
								return

				set_weights(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				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(lowerCamelCase			)
								return
				else:
								UpperCAmelCase__			 =				'conv'

				set_weights(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     f'''layers.{layer_id}.{layer_type}.{weight_type}'''			)



@torch.no_grad()
def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=None   ,     lowerCamelCase=None   ,     lowerCamelCase=True			):
				if config_path is not None:
								UpperCAmelCase__			 =				DataaVecAudioConfig.from_pretrained(lowerCamelCase			)
				else:
								UpperCAmelCase__			 =				DataaVecAudioConfig()

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

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

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

				UpperCAmelCase__			 =				load_dataavec(lowerCamelCase			)

				recursively_load_weights(lowerCamelCase   ,     lowerCamelCase   ,     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(lowerCamelCase   ,     return_tensors='pt'   ,     padding=lowerCamelCase			)

				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=lowerCamelCase   ,     padding_mask=(1 - attention_mask)   ,     mask=lowerCamelCase   ,     features_only=lowerCamelCase			)[
								    'encoder_out'
								].transpose(0   ,     1			)
								UpperCAmelCase__			 =				hf_wavavec(lowerCamelCase   ,     attention_mask=lowerCamelCase			)['logits']

								UpperCAmelCase__			 =				torch.argmax(lowerCamelCase   ,     dim=-1			)
								UpperCAmelCase__			 =				processor.batch_decode(lowerCamelCase			)

								print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''			)
				else:
								UpperCAmelCase__			 =				model(source=lowerCamelCase   ,     padding_mask=(1 - attention_mask)   ,     mask=lowerCamelCase   ,     features_only=lowerCamelCase			)[
								    'layer_results'
								][-1][0].transpose(0   ,     1			)
								UpperCAmelCase__			 =				hf_wavavec(lowerCamelCase   ,     attention_mask=lowerCamelCase			)['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(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__":
						lowerCAmelCase__    :    Optional[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'
						)
						lowerCAmelCase__    :    Union[str, 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
						)




"""simple docstring"""






from __future__ import annotations



def     a_		(   lowerCamelCase			):
				for r in range(1   ,     lowerCamelCase			):
								UpperCAmelCase__			 =				[]
								for x in range(n - 1			):
												UpperCAmelCase__			 =				pow(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			)
												if val in li:
																break
												li.append(lowerCamelCase			)
								else:
												return r
				return None


if __name__ == "__main__":
						lowerCAmelCase__    :    Any			=      int(input('Enter a prime number q: '))
						lowerCAmelCase__    :    List[str]			=      find_primitive(q)
						if a is None:
												print(F"""Cannot find the primitive for the value: {a!r}""")
						else:
												lowerCAmelCase__    :    str			=      int(input('Enter private key of A: '))
												lowerCAmelCase__    :    List[Any]			=      pow(a, a_private, q)
												lowerCAmelCase__    :    List[str]			=      int(input('Enter private key of B: '))
												lowerCAmelCase__    :    Any			=      pow(a, b_private, q)

												lowerCAmelCase__    :    Union[str, Any]			=      pow(b_public, a_private, q)
												lowerCAmelCase__    :    Tuple			=      pow(a_public, b_private, q)

												print('The key value generated by A is: ', a_secret)
												print('The key value generated by B is: ', b_secret)



