



"""simple docstring"""
from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__a           =							{
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a           =							[
						    "Constraint",
						    "ConstraintListState",
						    "DisjunctiveConstraint",
						    "PhrasalConstraint",
						]
						__a           =							[
						    "BeamHypotheses",
						    "BeamScorer",
						    "BeamSearchScorer",
						    "ConstrainedBeamSearchScorer",
						]
						__a           =							[
						    "EpsilonLogitsWarper",
						    "EtaLogitsWarper",
						    "ForcedBOSTokenLogitsProcessor",
						    "ForcedEOSTokenLogitsProcessor",
						    "HammingDiversityLogitsProcessor",
						    "InfNanRemoveLogitsProcessor",
						    "LogitsProcessor",
						    "LogitsProcessorList",
						    "LogitsWarper",
						    "MinLengthLogitsProcessor",
						    "MinNewTokensLengthLogitsProcessor",
						    "NoBadWordsLogitsProcessor",
						    "NoRepeatNGramLogitsProcessor",
						    "PrefixConstrainedLogitsProcessor",
						    "RepetitionPenaltyLogitsProcessor",
						    "SequenceBiasLogitsProcessor",
						    "EncoderRepetitionPenaltyLogitsProcessor",
						    "TemperatureLogitsWarper",
						    "TopKLogitsWarper",
						    "TopPLogitsWarper",
						    "TypicalLogitsWarper",
						    "EncoderNoRepeatNGramLogitsProcessor",
						    "ExponentialDecayLengthPenalty",
						    "LogitNormalization",
						]
						__a           =							[
						    "MaxNewTokensCriteria",
						    "MaxLengthCriteria",
						    "MaxTimeCriteria",
						    "StoppingCriteria",
						    "StoppingCriteriaList",
						    "validate_stopping_criteria",
						]
						__a           =							[
						    "GenerationMixin",
						    "top_k_top_p_filtering",
						    "GreedySearchEncoderDecoderOutput",
						    "GreedySearchDecoderOnlyOutput",
						    "SampleEncoderDecoderOutput",
						    "SampleDecoderOnlyOutput",
						    "BeamSearchEncoderDecoderOutput",
						    "BeamSearchDecoderOnlyOutput",
						    "BeamSampleEncoderDecoderOutput",
						    "BeamSampleDecoderOnlyOutput",
						    "ContrastiveSearchEncoderDecoderOutput",
						    "ContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a           =							[
						    "TFForcedBOSTokenLogitsProcessor",
						    "TFForcedEOSTokenLogitsProcessor",
						    "TFLogitsProcessor",
						    "TFLogitsProcessorList",
						    "TFLogitsWarper",
						    "TFMinLengthLogitsProcessor",
						    "TFNoBadWordsLogitsProcessor",
						    "TFNoRepeatNGramLogitsProcessor",
						    "TFRepetitionPenaltyLogitsProcessor",
						    "TFTemperatureLogitsWarper",
						    "TFTopKLogitsWarper",
						    "TFTopPLogitsWarper",
						    "TFForceTokensLogitsProcessor",
						    "TFSuppressTokensAtBeginLogitsProcessor",
						    "TFSuppressTokensLogitsProcessor",
						]
						__a           =							[
						    "TFGenerationMixin",
						    "tf_top_k_top_p_filtering",
						    "TFGreedySearchDecoderOnlyOutput",
						    "TFGreedySearchEncoderDecoderOutput",
						    "TFSampleEncoderDecoderOutput",
						    "TFSampleDecoderOnlyOutput",
						    "TFBeamSearchEncoderDecoderOutput",
						    "TFBeamSearchDecoderOnlyOutput",
						    "TFBeamSampleEncoderDecoderOutput",
						    "TFBeamSampleDecoderOnlyOutput",
						    "TFContrastiveSearchEncoderDecoderOutput",
						    "TFContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a           =							[
						    "FlaxForcedBOSTokenLogitsProcessor",
						    "FlaxForcedEOSTokenLogitsProcessor",
						    "FlaxLogitsProcessor",
						    "FlaxLogitsProcessorList",
						    "FlaxLogitsWarper",
						    "FlaxMinLengthLogitsProcessor",
						    "FlaxTemperatureLogitsWarper",
						    "FlaxTopKLogitsWarper",
						    "FlaxTopPLogitsWarper",
						]
						__a           =							[
						    "FlaxGenerationMixin",
						    "FlaxGreedySearchOutput",
						    "FlaxSampleOutput",
						    "FlaxBeamSearchOutput",
						]

if TYPE_CHECKING:
						from .configuration_utils import GenerationConfig
						from .streamers import TextIteratorStreamer, TextStreamer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
												from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
												from .logits_process import (
												    EncoderNoRepeatNGramLogitsProcessor,
												    EncoderRepetitionPenaltyLogitsProcessor,
												    EpsilonLogitsWarper,
												    EtaLogitsWarper,
												    ExponentialDecayLengthPenalty,
												    ForcedBOSTokenLogitsProcessor,
												    ForcedEOSTokenLogitsProcessor,
												    HammingDiversityLogitsProcessor,
												    InfNanRemoveLogitsProcessor,
												    LogitNormalization,
												    LogitsProcessor,
												    LogitsProcessorList,
												    LogitsWarper,
												    MinLengthLogitsProcessor,
												    MinNewTokensLengthLogitsProcessor,
												    NoBadWordsLogitsProcessor,
												    NoRepeatNGramLogitsProcessor,
												    PrefixConstrainedLogitsProcessor,
												    RepetitionPenaltyLogitsProcessor,
												    SequenceBiasLogitsProcessor,
												    TemperatureLogitsWarper,
												    TopKLogitsWarper,
												    TopPLogitsWarper,
												    TypicalLogitsWarper,
												)
												from .stopping_criteria import (
												    MaxLengthCriteria,
												    MaxNewTokensCriteria,
												    MaxTimeCriteria,
												    StoppingCriteria,
												    StoppingCriteriaList,
												    validate_stopping_criteria,
												)
												from .utils import (
												    BeamSampleDecoderOnlyOutput,
												    BeamSampleEncoderDecoderOutput,
												    BeamSearchDecoderOnlyOutput,
												    BeamSearchEncoderDecoderOutput,
												    ContrastiveSearchDecoderOnlyOutput,
												    ContrastiveSearchEncoderDecoderOutput,
												    GenerationMixin,
												    GreedySearchDecoderOnlyOutput,
												    GreedySearchEncoderDecoderOutput,
												    SampleDecoderOnlyOutput,
												    SampleEncoderDecoderOutput,
												    top_k_top_p_filtering,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tf_logits_process import (
												    TFForcedBOSTokenLogitsProcessor,
												    TFForcedEOSTokenLogitsProcessor,
												    TFForceTokensLogitsProcessor,
												    TFLogitsProcessor,
												    TFLogitsProcessorList,
												    TFLogitsWarper,
												    TFMinLengthLogitsProcessor,
												    TFNoBadWordsLogitsProcessor,
												    TFNoRepeatNGramLogitsProcessor,
												    TFRepetitionPenaltyLogitsProcessor,
												    TFSuppressTokensAtBeginLogitsProcessor,
												    TFSuppressTokensLogitsProcessor,
												    TFTemperatureLogitsWarper,
												    TFTopKLogitsWarper,
												    TFTopPLogitsWarper,
												)
												from .tf_utils import (
												    TFBeamSampleDecoderOnlyOutput,
												    TFBeamSampleEncoderDecoderOutput,
												    TFBeamSearchDecoderOnlyOutput,
												    TFBeamSearchEncoderDecoderOutput,
												    TFContrastiveSearchDecoderOnlyOutput,
												    TFContrastiveSearchEncoderDecoderOutput,
												    TFGenerationMixin,
												    TFGreedySearchDecoderOnlyOutput,
												    TFGreedySearchEncoderDecoderOutput,
												    TFSampleDecoderOnlyOutput,
												    TFSampleEncoderDecoderOutput,
												    tf_top_k_top_p_filtering,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .flax_logits_process import (
												    FlaxForcedBOSTokenLogitsProcessor,
												    FlaxForcedEOSTokenLogitsProcessor,
												    FlaxLogitsProcessor,
												    FlaxLogitsProcessorList,
												    FlaxLogitsWarper,
												    FlaxMinLengthLogitsProcessor,
												    FlaxTemperatureLogitsWarper,
												    FlaxTopKLogitsWarper,
												    FlaxTopPLogitsWarper,
												)
												from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
						import sys

						__a           =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



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

__A	:   Optional[int]	=					{
    '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	:   Optional[int]	=					[
    'lm_head',
]




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Dict      ,  _A					:							Tuple      ,  _A					:							str      ,  _A					:							int		)				->List[str]:



							"""simple docstring"""
							for attribute in key.split("""."""		):
														lowerCamelCase_								=getattr(_A      ,  _A		)

							if weight_type is not None:
														lowerCamelCase_								=getattr(_A      ,  _A		).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        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Dict      ,  _A					:							Optional[Any]		)				->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(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					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(_A		)[0].split("""."""		)[-2]
																																										lowerCamelCase_								=mapped_key.replace("""*"""      ,  _A		)
																																			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(_A      ,  _A      ,  _A      ,  _A      ,  _A		)
																												continue
														if not is_used:
																					unused_weights.append(_A		)

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




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Union[str, Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=path.split("""."""		)
							return reduce(_A      ,  _A      ,  _A		)




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							List[Any]      ,  _A					:							str      ,  _A					:							Dict		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=access_by_string(_A      ,  _A		)
							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        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->Union[str, Any]:



							"""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(_A		)
														return

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




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[int]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]		)				->Tuple:



							"""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(_A		)
														return
							else:
														lowerCamelCase_								="""conv"""

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




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]=None      ,  _A					:							int=None      ,  _A					:							Union[str, Any]=True		)				->str:



							"""simple docstring"""
							if config_path is not None:
														lowerCamelCase_								=DataaVecAudioConfig.from_pretrained(_A		)
							else:
														lowerCamelCase_								=DataaVecAudioConfig()

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

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

							def load_dataavec(_A					:							str		):
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
														return model[0].eval()

							lowerCamelCase_								=load_dataavec(_A		)

							recursively_load_weights(_A      ,  _A      ,  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(_A      ,  return_tensors="""pt"""      ,  padding=_A		)

							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=_A      ,  padding_mask=(1 - attention_mask)      ,  mask=_A      ,  features_only=_A		)[
														    """encoder_out"""
														].transpose(0      ,  1		)
														lowerCamelCase_								=hf_wavavec(_A      ,  attention_mask=_A		)["""logits"""]

														lowerCamelCase_								=torch.argmax(_A      ,  dim=-1		)
														lowerCamelCase_								=processor.batch_decode(_A		)

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

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


if __name__ == "__main__":
	__A	:   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'
	)
	__A	:   List[str]	=					parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)
