





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

_lowercase		:     Dict 					={
    """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""",
}
_lowercase		:     Optional[int] 					=[
    """lm_head""",
]


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		for attribute in key.split('.'	):
				lowerCamelCase_  :    Tuple   	=			getattr(lowerCAmelCase__       ,lowerCAmelCase__	)

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

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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    Dict   	=			[]
		lowerCamelCase_  :    List[Any]   	=			fairseq_model.state_dict()

		if not is_headless:
				lowerCamelCase_  :    str   	=			hf_model.dataavec_audio.feature_extractor
				lowerCamelCase_  :    List[Any]   	=			hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				lowerCamelCase_  :    Dict   	=			hf_model.feature_extractor
				lowerCamelCase_  :    Union[str, Any]   	=			hf_model.encoder.pos_conv_embed

		for name, value in fairseq_dict.items():
				lowerCamelCase_  :    int   	=			False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,)
						lowerCamelCase_  :    int   	=			True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,)
						lowerCamelCase_  :    Optional[int]   	=			True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										lowerCamelCase_  :    int   	=			'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_  :    int   	=			True
										if "*" in mapped_key:
												lowerCamelCase_  :    List[str]   	=			name.split(lowerCAmelCase__	)[0].split('.'	)[-2]
												lowerCamelCase_  :    Union[str, Any]   	=			mapped_key.replace('*'       ,lowerCAmelCase__	)
										if "weight_g" in name:
												lowerCamelCase_  :    Tuple   	=			'weight_g'
										elif "weight_v" in name:
												lowerCamelCase_  :    int   	=			'weight_v'
										elif "bias" in name:
												lowerCamelCase_  :    List[str]   	=			'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCamelCase_  :    Union[str, Any]   	=			'weight'
										else:
												lowerCamelCase_  :    List[str]   	=			None
										set_recursively(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	)
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase__	)

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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    Optional[Any]   	=			path.split('.'	)
		return reduce(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    Any   	=			access_by_string(lowerCAmelCase__       ,lowerCAmelCase__	)
		lowerCamelCase_  :    List[str]   	=			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_  :    str   	=			fsq_value
		logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."	)


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    int   	=			full_name.split('conv_layers.'	)[-1]
		lowerCamelCase_  :    Optional[int]   	=			name.split('.'	)
		lowerCamelCase_  :    str   	=			int(items[0]	)
		lowerCamelCase_  :    str   	=			int(items[1]	)

		lowerCamelCase_  :    Dict   	=			name.split('.'	)[-1]
		if type_id == 0:
				lowerCamelCase_  :    Any   	=			'conv'
		elif type_id == 2:
				lowerCamelCase_  :    Union[str, Any]   	=			'layer_norm'
		else:
				unused_weights.append(lowerCAmelCase__	)
				return

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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    List[Any]   	=			full_name.split('pos_conv.'	)[-1]
		lowerCamelCase_  :    Tuple   	=			name.split('.'	)
		lowerCamelCase_  :    Tuple   	=			int(items[0]	)
		lowerCamelCase_  :    Dict   	=			int(items[1]	)

		lowerCamelCase_  :    int   	=			name.split('.'	)[-1]
		if type_id != 0:
				unused_weights.append(lowerCAmelCase__	)
				return
		else:
				lowerCamelCase_  :    Union[str, Any]   	=			'conv'

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


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=True	):
		if config_path is not None:
				lowerCamelCase_  :    Union[str, Any]   	=			DataaVecAudioConfig.from_pretrained(lowerCAmelCase__	)
		else:
				lowerCamelCase_  :    int   	=			DataaVecAudioConfig()

		if not is_finetuned:
				# Modify final_proj layer name
				lowerCamelCase_  :    Tuple   	=			DataaVecAudioModel(lowerCAmelCase__	)
				lowerCamelCase_  :    Optional[Any]   	=			os.path.dirname(lowerCAmelCase__	)

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

		def load_dataavec(lowerCAmelCase__	):
				lowerCamelCase_  :    Tuple   	=			fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
				return model[0].eval()

		lowerCamelCase_  :    List[str]   	=			load_dataavec(lowerCAmelCase__	)

		recursively_load_weights(lowerCAmelCase__       ,lowerCAmelCase__       ,not is_finetuned	)

		lowerCamelCase_  :    int   	=			WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'	)

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

		lowerCamelCase_  :    List[str]   	=			processor(lowerCAmelCase__       ,return_tensors='pt'       ,padding=lowerCAmelCase__	)

		lowerCamelCase_  :    Any   	=			inputs.input_values
		lowerCamelCase_  :    int   	=			inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

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

				lowerCamelCase_  :    int   	=			torch.argmax(lowerCAmelCase__       ,dim=-1	)
				lowerCamelCase_  :    Any   	=			processor.batch_decode(lowerCAmelCase__	)

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

		print(our_output.shape       ,their_output.shape	)
		lowerCamelCase_  :    List[str]   	=			torch.max(torch.abs(our_output - their_output	)	).item()
		print(F"max_absolute_diff = {max_absolute_diff}"	)  # ~ 1e-7
		lowerCamelCase_  :    Optional[Any]   	=			torch.allclose(lowerCAmelCase__       ,lowerCAmelCase__       ,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(lowerCAmelCase__	)

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


if __name__ == "__main__":
		_lowercase		:     Union[str, 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"""
		)
		_lowercase		:     Dict 					=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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:     Dict 					=logging.get_logger(__name__)

_lowercase		:     Any 					={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	List[str]							=				'vilt'



					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)

							lowerCamelCase_  :    str   	=			vocab_size
							lowerCamelCase_  :    Dict   	=			type_vocab_size
							lowerCamelCase_  :    str   	=			modality_type_vocab_size
							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings

							lowerCamelCase_  :    List[Any]   	=			hidden_size
							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
							lowerCamelCase_  :    Any   	=			num_attention_heads
							lowerCamelCase_  :    List[Any]   	=			intermediate_size
							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
							lowerCamelCase_  :    Any   	=			initializer_range
							lowerCamelCase_  :    str   	=			layer_norm_eps

							lowerCamelCase_  :    Optional[Any]   	=			image_size
							lowerCamelCase_  :    List[Any]   	=			patch_size
							lowerCamelCase_  :    Tuple   	=			num_channels
							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
							lowerCamelCase_  :    Optional[int]   	=			max_image_length
							lowerCamelCase_  :    Optional[Any]   	=			num_images
