


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

UpperCAmelCase_		:       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_		:       Optional[Any]							       =		[
    'lm_head',
]







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				for attribute in key.split("""."""					):
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				getattr(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

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

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







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[]
				_SCREAMING_SNAKE_CASE			:	str							=				fairseq_model.state_dict()

				if not is_headless:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				hf_model.dataavec_audio.feature_extractor
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				hf_model.dataavec_audio.encoder.pos_conv_embed

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

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

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







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	int							=				path.split("""."""					)
				return reduce(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				access_by_string(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				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			:	List[str]							=				fsq_value
				logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""					)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				full_name.split("""conv_layers."""					)[-1]
				_SCREAMING_SNAKE_CASE			:	Tuple							=				name.split("""."""					)
				_SCREAMING_SNAKE_CASE			:	Dict							=				int(items[0]					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				int(items[1]					)

				_SCREAMING_SNAKE_CASE			:	str							=				name.split("""."""					)[-1]
				if type_id == 0:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				"""conv"""
				elif type_id == 2:
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				"""layer_norm"""
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__					)
								return

				set_weights(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""					)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				full_name.split("""pos_conv."""					)[-1]
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				name.split("""."""					)
				_SCREAMING_SNAKE_CASE			:	str							=				int(items[0]					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				int(items[1]					)

				_SCREAMING_SNAKE_CASE			:	int							=				name.split("""."""					)[-1]
				if type_id != 0:
								unused_weights.append(SCREAMING_SNAKE_CASE__					)
								return
				else:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				"""conv"""

				set_weights(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				f"""layers.{layer_id}.{layer_type}.{weight_type}"""					)







@torch.no_grad()
def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=None							,				SCREAMING_SNAKE_CASE__=None							,				SCREAMING_SNAKE_CASE__=True					):







				"""simple docstring"""





				if config_path is not None:
								_SCREAMING_SNAKE_CASE			:	List[str]							=				DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
				else:
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				DataaVecAudioConfig()

				if not is_finetuned:
								# Modify final_proj layer name
								_SCREAMING_SNAKE_CASE			:	str							=				DataaVecAudioModel(SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	int							=				os.path.dirname(SCREAMING_SNAKE_CASE__					)

								_SCREAMING_SNAKE_CASE			:	Dict							=				torch.load(SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	Any							=				state_dict["""model"""].pop("""final_proj.0.weight"""					)
								_SCREAMING_SNAKE_CASE			:	Any							=				state_dict["""model"""].pop("""final_proj.0.bias"""					)
								_SCREAMING_SNAKE_CASE			:	Dict							=				os.path.join(SCREAMING_SNAKE_CASE__							,				"""converted.pt"""					)
								torch.save(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				else:
								_SCREAMING_SNAKE_CASE			:	Tuple							=				DataaVecAudioForCTC(SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				checkpoint_path

				def load_dataavec(SCREAMING_SNAKE_CASE__					):
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Any							=				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]					)
								return model[0].eval()

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_dataavec(SCREAMING_SNAKE_CASE__					)

				recursively_load_weights(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				not is_finetuned					)

				_SCREAMING_SNAKE_CASE			:	Tuple							=				WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""					)

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

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				processor(SCREAMING_SNAKE_CASE__							,				return_tensors="""pt"""							,				padding=SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	int							=				inputs.input_values
				_SCREAMING_SNAKE_CASE			:	Tuple							=				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			:	Optional[int]							=				model(source=SCREAMING_SNAKE_CASE__							,				padding_mask=(1 - attention_mask)							,				mask=SCREAMING_SNAKE_CASE__							,				features_only=SCREAMING_SNAKE_CASE__					)[
								    """encoder_out"""
								].transpose(0							,				1					)
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				hf_wavavec(SCREAMING_SNAKE_CASE__							,				attention_mask=SCREAMING_SNAKE_CASE__					)["""logits"""]

								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.argmax(SCREAMING_SNAKE_CASE__							,				dim=-1					)
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				processor.batch_decode(SCREAMING_SNAKE_CASE__					)

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

				print(our_output.shape							,				their_output.shape					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.max(torch.abs(our_output - their_output					)					).item()
				print(f"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
				_SCREAMING_SNAKE_CASE			:	int							=				torch.allclose(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				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(SCREAMING_SNAKE_CASE__					)

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


if __name__ == "__main__":
				UpperCAmelCase_		:       Dict							       =		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_		:       Optional[int]							       =		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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , **__snake_case   , )




