
"""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			=		logging.get_logger(__name__)

lowerCAmelCase			=		{
    """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			=		[
    """lm_head""",
]


def      lowerCAmelCase_     (	snake_case_	:    Union[str, Any]							,					snake_case_	:    Dict							,					snake_case_	:    Tuple							,					snake_case_	:    List[Any]							,					snake_case_	:    Any    )     ->List[str]:
			for attribute in key.split('.'    ):
						lowerCamelCase__			:  Dict  =getattr(snake_case_							,					snake_case_    )

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

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


def      lowerCAmelCase_     (	snake_case_	:    List[str]							,					snake_case_	:    List[Any]							,					snake_case_	:    List[str]    )     ->List[str]:
			lowerCamelCase__			:  Optional[Any]  =[]
			lowerCamelCase__			:  Tuple  =fairseq_model.state_dict()

			if not is_headless:
						lowerCamelCase__			:  List[Any]  =hf_model.dataavec_audio.feature_extractor
						lowerCamelCase__			:  Optional[int]  =hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						lowerCamelCase__			:  Dict  =hf_model.feature_extractor
						lowerCamelCase__			:  Optional[int]  =hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						lowerCamelCase__			:  List[str]  =False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					)
									lowerCamelCase__			:  Any  =True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					)
									lowerCamelCase__			:  Any  =True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															lowerCamelCase__			:  str  ='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__			:  Optional[int]  =True
															if "*" in mapped_key:
																		lowerCamelCase__			:  Union[str, Any]  =name.split(snake_case_    )[0].split('.'    )[-2]
																		lowerCamelCase__			:  Any  =mapped_key.replace('*'							,					snake_case_    )
															if "weight_g" in name:
																		lowerCamelCase__			:  str  ='weight_g'
															elif "weight_v" in name:
																		lowerCamelCase__			:  List[Any]  ='weight_v'
															elif "bias" in name:
																		lowerCamelCase__			:  Dict  ='bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCamelCase__			:  Any  ='weight'
															else:
																		lowerCamelCase__			:  Optional[Any]  =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      lowerCAmelCase_     (	snake_case_	:    int							,					snake_case_	:    Any    )     ->int:
			lowerCamelCase__			:  Union[str, Any]  =path.split('.'    )
			return reduce(snake_case_							,					snake_case_							,					snake_case_    )


def      lowerCAmelCase_     (	snake_case_	:    Tuple							,					snake_case_	:    Dict							,					snake_case_	:    int							,					snake_case_	:    Tuple    )     ->Optional[int]:
			lowerCamelCase__			:  List[Any]  =access_by_string(snake_case_							,					snake_case_    )
			lowerCamelCase__			:  List[Any]  =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__			:  List[Any]  =fsq_value
			logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""    )


def      lowerCAmelCase_     (	snake_case_	:    List[Any]							,					snake_case_	:    str							,					snake_case_	:    Optional[int]							,					snake_case_	:    List[Any]    )     ->Any:
			lowerCamelCase__			:  List[str]  =full_name.split('conv_layers.'    )[-1]
			lowerCamelCase__			:  Tuple  =name.split('.'    )
			lowerCamelCase__			:  int  =int(items[0]    )
			lowerCamelCase__			:  List[str]  =int(items[1]    )

			lowerCamelCase__			:  str  =name.split('.'    )[-1]
			if type_id == 0:
						lowerCamelCase__			:  Optional[Any]  ='conv'
			elif type_id == 2:
						lowerCamelCase__			:  Dict  ='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      lowerCAmelCase_     (	snake_case_	:    Any							,					snake_case_	:    Tuple							,					snake_case_	:    List[str]							,					snake_case_	:    List[Any]    )     ->List[str]:
			lowerCamelCase__			:  Any  =full_name.split('pos_conv.'    )[-1]
			lowerCamelCase__			:  Tuple  =name.split('.'    )
			lowerCamelCase__			:  Tuple  =int(items[0]    )
			lowerCamelCase__			:  List[str]  =int(items[1]    )

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

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


@torch.no_grad()
def      lowerCAmelCase_     (	snake_case_	:    List[Any]							,					snake_case_	:    int							,					snake_case_	:    Any=None							,					snake_case_	:    Optional[int]=None							,					snake_case_	:    Any=True    )     ->Dict:
			if config_path is not None:
						lowerCamelCase__			:  List[Any]  =DataaVecAudioConfig.from_pretrained(snake_case_    )
			else:
						lowerCamelCase__			:  Tuple  =DataaVecAudioConfig()

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

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

			def load_dataavec(snake_case_	:    Dict    ):
						lowerCamelCase__			:  Tuple  =fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
						return model[0].eval()

			lowerCamelCase__			:  str  =load_dataavec(snake_case_    )

			recursively_load_weights(snake_case_							,					snake_case_							,					not is_finetuned    )

			lowerCamelCase__			:  Any  =WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'    )

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

			lowerCamelCase__			:  int  =processor(snake_case_							,					return_tensors='pt'							,					padding=snake_case_    )

			lowerCamelCase__			:  Optional[Any]  =inputs.input_values
			lowerCamelCase__			:  Union[str, Any]  =inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

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

						lowerCamelCase__			:  Optional[Any]  =torch.argmax(snake_case_							,					dim=-1    )
						lowerCamelCase__			:  List[str]  =processor.batch_decode(snake_case_    )

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

			print(our_output.shape							,					their_output.shape    )
			lowerCamelCase__			:  Optional[Any]  =torch.max(torch.abs(our_output - their_output    )    ).item()
			print(f"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
			lowerCamelCase__			:  Optional[Any]  =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__":
							lowerCAmelCase			=		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			=		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 sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()