





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()
UpperCAmelCase__    :      str       					=logging.get_logger(__name__)

UpperCAmelCase__    :      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''',
}
UpperCAmelCase__    :      int       					=[
    '''lm_head''',
]





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Any:
				for attribute in key.split("""."""					):
								lowerCamelCase									=getattr(_UpperCAmelCase      ,   _UpperCAmelCase					)

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

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





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Any:
				lowerCamelCase									=path.split("""."""					)
				return reduce(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	List[str]:
				lowerCamelCase									=access_by_string(_UpperCAmelCase      ,   _UpperCAmelCase					)
				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   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Tuple:
				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(_UpperCAmelCase					)
								return

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





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	int:
				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(_UpperCAmelCase					)
								return
				else:
								lowerCamelCase									="""conv"""

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





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase=None      ,   _UpperCAmelCase=None      ,   _UpperCAmelCase=True					)    ->	Union[str, Any]:
				if config_path is not None:
								lowerCamelCase									=DataaVecAudioConfig.from_pretrained(_UpperCAmelCase					)
				else:
								lowerCamelCase									=DataaVecAudioConfig()

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

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

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

				lowerCamelCase									=load_dataavec(_UpperCAmelCase					)

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

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

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

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

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


UpperCAmelCase__    :      int       					={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=[
							    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaubertForMultipleChoice''',
							    '''FlaubertForQuestionAnswering''',
							    '''FlaubertForQuestionAnsweringSimple''',
							    '''FlaubertForSequenceClassification''',
							    '''FlaubertForTokenClassification''',
							    '''FlaubertModel''',
							    '''FlaubertWithLMHeadModel''',
							    '''FlaubertPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      List[str]       					=[
							    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFFlaubertForMultipleChoice''',
							    '''TFFlaubertForQuestionAnsweringSimple''',
							    '''TFFlaubertForSequenceClassification''',
							    '''TFFlaubertForTokenClassification''',
							    '''TFFlaubertModel''',
							    '''TFFlaubertPreTrainedModel''',
							    '''TFFlaubertWithLMHeadModel''',
							]


if TYPE_CHECKING:
							from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
							from .tokenization_flaubert import FlaubertTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flaubert import (
														    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaubertForMultipleChoice,
														    FlaubertForQuestionAnswering,
														    FlaubertForQuestionAnsweringSimple,
														    FlaubertForSequenceClassification,
														    FlaubertForTokenClassification,
														    FlaubertModel,
														    FlaubertPreTrainedModel,
														    FlaubertWithLMHeadModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_flaubert import (
														    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFlaubertForMultipleChoice,
														    TFFlaubertForQuestionAnsweringSimple,
														    TFFlaubertForSequenceClassification,
														    TFFlaubertForTokenClassification,
														    TFFlaubertModel,
														    TFFlaubertPreTrainedModel,
														    TFFlaubertWithLMHeadModel,
														)

else:
							import sys

							UpperCAmelCase__    :      List[Any]       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





