
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()
lowerCamelCase__        =						logging.get_logger(__name__)

lowerCamelCase__        =						{
    '''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''',
}
lowerCamelCase__        =						[
    '''lm_head''',
]
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Any:
			for attribute in key.split('.'	):
						lowerCAmelCase__ : Union[str, Any]   =						getattr(_UpperCAmelCase			,				_UpperCAmelCase	)

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

			logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Dict:
			lowerCAmelCase__ : Tuple   =						[]
			lowerCAmelCase__ : Union[str, Any]   =						fairseq_model.state_dict()

			if not is_headless:
						lowerCAmelCase__ : Dict   =						hf_model.dataavec_audio.feature_extractor
						lowerCAmelCase__ : Optional[int]   =						hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						lowerCAmelCase__ : List[Any]   =						hf_model.feature_extractor
						lowerCAmelCase__ : int   =						hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						lowerCAmelCase__ : Dict   =						False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				)
									lowerCAmelCase__ : Dict   =						True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    _UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				)
									lowerCAmelCase__ : Optional[int]   =						True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															lowerCAmelCase__ : Optional[Any]   =						'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__ : Tuple   =						name.split(_UpperCAmelCase	)[0].split('.'	)[-2]
																		lowerCAmelCase__ : Dict   =						mapped_key.replace('*'			,				_UpperCAmelCase	)
															if "weight_g" in name:
																		lowerCAmelCase__ : Dict   =						'weight_g'
															elif "weight_v" in name:
																		lowerCAmelCase__ : Dict   =						'weight_v'
															elif "bias" in name:
																		lowerCAmelCase__ : Tuple   =						'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCAmelCase__ : Union[str, Any]   =						'weight'
															else:
																		lowerCAmelCase__ : int   =						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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Any:
			lowerCAmelCase__ : Dict   =						path.split('.'	)
			return reduce(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			lowerCAmelCase__ : str   =						access_by_string(_UpperCAmelCase			,				_UpperCAmelCase	)
			lowerCAmelCase__ : Optional[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.'''	)
			lowerCAmelCase__ : Union[str, Any]   =						fsq_value
			logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Tuple:
			lowerCAmelCase__ : Optional[int]   =						full_name.split('conv_layers.'	)[-1]
			lowerCAmelCase__ : Optional[Any]   =						name.split('.'	)
			lowerCAmelCase__ : Optional[int]   =						int(items[0]	)
			lowerCAmelCase__ : Optional[int]   =						int(items[1]	)

			lowerCAmelCase__ : Optional[Any]   =						name.split('.'	)[-1]
			if type_id == 0:
						lowerCAmelCase__ : List[Any]   =						'conv'
			elif type_id == 2:
						lowerCAmelCase__ : str   =						'layer_norm'
			else:
						unused_weights.append(_UpperCAmelCase	)
						return

			set_weights(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : Optional[Any]   =						full_name.split('pos_conv.'	)[-1]
			lowerCAmelCase__ : List[Any]   =						name.split('.'	)
			lowerCAmelCase__ : List[Any]   =						int(items[0]	)
			lowerCAmelCase__ : Union[str, Any]   =						int(items[1]	)

			lowerCAmelCase__ : Dict   =						name.split('.'	)[-1]
			if type_id != 0:
						unused_weights.append(_UpperCAmelCase	)
						return
			else:
						lowerCAmelCase__ : Optional[int]   =						'conv'

			set_weights(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase			,				F'''layers.{layer_id}.{layer_type}.{weight_type}'''	)
@torch.no_grad()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=True	)							->						Union[str, Any]:
			if config_path is not None:
						lowerCAmelCase__ : Any   =						DataaVecAudioConfig.from_pretrained(_UpperCAmelCase	)
			else:
						lowerCAmelCase__ : List[str]   =						DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						lowerCAmelCase__ : Tuple   =						DataaVecAudioModel(_UpperCAmelCase	)
						lowerCAmelCase__ : List[str]   =						os.path.dirname(_UpperCAmelCase	)

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

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

			lowerCAmelCase__ : Union[str, Any]   =						load_dataavec(_UpperCAmelCase	)

			recursively_load_weights(_UpperCAmelCase			,				_UpperCAmelCase			,				not is_finetuned	)

			lowerCAmelCase__ : Optional[int]   =						WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'	)

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

			lowerCAmelCase__ : Optional[int]   =						processor(_UpperCAmelCase			,				return_tensors='pt'			,				padding=_UpperCAmelCase	)

			lowerCAmelCase__ : Any   =						inputs.input_values
			lowerCAmelCase__ : Tuple   =						inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

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

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

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

			print(our_output.shape			,				their_output.shape	)
			lowerCAmelCase__ : int   =						torch.max(torch.abs(our_output - their_output	)	).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
			lowerCAmelCase__ : Optional[Any]   =						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__":
					lowerCamelCase__        =						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"""
					)
					lowerCamelCase__        =						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_torch_available, is_vision_available


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
    '''processing_donut''': ['''DonutProcessor'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Any       					=[
							    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DonutSwinModel''',
							    '''DonutSwinPreTrainedModel''',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Tuple       					=['''DonutFeatureExtractor''']
							UpperCAmelCase__    :      List[str]       					=['''DonutImageProcessor''']


if TYPE_CHECKING:
							from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
							from .processing_donut import DonutProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_donut_swin import (
														    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DonutSwinModel,
														    DonutSwinPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_donut import DonutFeatureExtractor
														from .image_processing_donut import DonutImageProcessor

else:
							import sys

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





