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

_lowerCamelCase    :      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    :      int	 =						[
    '''lm_head''',
]





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							Tuple     ):
					for attribute in key.split("."     ):
										SCREAMING_SNAKE_CASE				=				getattr(UpperCAmelCase__					,       UpperCAmelCase__     )

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

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





def 		__lowerCamelCase							(UpperCAmelCase__   :							Union[str, Any]					,       UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				fairseq_model.state_dict()

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

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

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





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				access_by_string(UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				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."     )
					SCREAMING_SNAKE_CASE				=				fsq_value
					logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				full_name.split("conv_layers."     )[-1]
					SCREAMING_SNAKE_CASE				=				name.split("."     )
					SCREAMING_SNAKE_CASE				=				int(items[0]     )
					SCREAMING_SNAKE_CASE				=				int(items[1]     )

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

					set_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       F"conv_layers.{layer_id}.{layer_type}.{weight_type}"     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[int]     ):
					SCREAMING_SNAKE_CASE				=				full_name.split("pos_conv."     )[-1]
					SCREAMING_SNAKE_CASE				=				name.split("."     )
					SCREAMING_SNAKE_CASE				=				int(items[0]     )
					SCREAMING_SNAKE_CASE				=				int(items[1]     )

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

					set_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       F"layers.{layer_id}.{layer_type}.{weight_type}"     )





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__   :							Any					,       UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							Optional[Any]=None					,       UpperCAmelCase__   :							List[Any]=None					,       UpperCAmelCase__   :							Optional[int]=True     ):
					if config_path is not None:
										SCREAMING_SNAKE_CASE				=				DataaVecAudioConfig.from_pretrained(UpperCAmelCase__     )
					else:
										SCREAMING_SNAKE_CASE				=				DataaVecAudioConfig()

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

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

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

					SCREAMING_SNAKE_CASE				=				load_dataavec(UpperCAmelCase__     )

					recursively_load_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       not is_finetuned     )

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

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

					SCREAMING_SNAKE_CASE				=				processor(UpperCAmelCase__					,       return_tensors="pt"					,       padding=UpperCAmelCase__     )

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

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

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

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

					print(our_output.shape					,       their_output.shape     )
					SCREAMING_SNAKE_CASE				=				torch.max(torch.abs(our_output - their_output     )     ).item()
					print(F"max_absolute_diff = {max_absolute_diff}"     )  # ~ 1e-7
					SCREAMING_SNAKE_CASE				=				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__":
							_lowerCamelCase    :      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
							)





from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase    :      Any	 =						'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

_lowerCamelCase    :      str	 =						'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_lowerCamelCase    :      List[str]	 =						'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION   )
class   lowercase			(   datasets.Metric   ):




							def 		__snake_case(  self      :    int					)    -> str:



												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION		,							citation=_CITATION		,							inputs_description=_KWARGS_DESCRIPTION		,							features=datasets.Features(self._get_feature_types()					)		,							reference_urls=[
												        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
												    ]		,							)




							def 		__snake_case(  self      :    int					)    -> Tuple:



												'''simple docstring'''

												if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value("float"					)					),
																	    "references": datasets.Sequence(datasets.Value("float"					)					),
																	}
												else:
																	return {
																	    "predictions": datasets.Value("float"					),
																	    "references": datasets.Value("float"					),
																	}





							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    str=None		,							_UpperCamelCase      :    Any="uniform_average"					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				mean_absolute_error(_UpperCamelCase		,							_UpperCamelCase		,							sample_weight=_UpperCamelCase		,							multioutput=_UpperCamelCase					)

												return {"mae": mae_score}




