


"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						for attribute in key.split('''.''' ):
												UpperCAmelCase__		=    getattr(_UpperCamelCase	,			_UpperCamelCase )

						if weight_type is not None:
												UpperCAmelCase__		=    getattr(_UpperCamelCase	,			_UpperCamelCase ).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__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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(
																		    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			)
																		UpperCAmelCase__		=    True
												elif "pos_conv" in name:
																		load_pos_conv_layer(
																		    _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			)
																		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(_UpperCamelCase )[0].split('''.''' )[-2]
																																				UpperCAmelCase__		=    mapped_key.replace('''*'''	,			_UpperCamelCase )
																														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(_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase )
																								continue
												if not is_used:
																		unused_weights.append(_UpperCamelCase )

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    path.split('''.''' )
						return reduce(_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    access_by_string(_UpperCamelCase	,			_UpperCamelCase )
						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__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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(_UpperCamelCase )
												return

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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(_UpperCamelCase )
												return
						else:
												UpperCAmelCase__		=    'conv'

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






@torch.no_grad()
def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=None	,			_UpperCamelCase=True ):





						"""simple docstring"""

						if config_path is not None:
												UpperCAmelCase__		=    DataaVecAudioConfig.from_pretrained(_UpperCamelCase )
						else:
												UpperCAmelCase__		=    DataaVecAudioConfig()

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

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

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

						UpperCAmelCase__		=    load_dataavec(_UpperCamelCase )

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

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

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

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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

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