






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       str 					= ['image_processor', 'tokenizer']
		UpperCAmelCase_							:       List[str] 					= 'LayoutLMv2ImageProcessor'
		UpperCAmelCase_							:       List[str] 					= ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')






		def __init__(      self							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Optional[int]:
				__A    =							None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
						    " instead."							, lowercase__							, )
						__A    =							kwargs.pop("feature_extractor"						)

				__A    =							image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError("You need to specify an `image_processor`."						)
				if tokenizer is None:
						raise ValueError("You need to specify a `tokenizer`."						)

				super().__init__(lowercase__							, lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = True							, lowercase__ = False							, lowercase__ = False							, lowercase__ = None							, lowercase__ = 0							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = False							, lowercase__ = False							, lowercase__ = False							, lowercase__ = False							, lowercase__ = True							, lowercase__ = None							, **lowercase__							, )				->				BatchEncoding:
				# verify input
				if self.image_processor.apply_ocr and (boxes is not None):
						raise ValueError(
						    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

				if self.image_processor.apply_ocr and (word_labels is not None):
						raise ValueError(
						    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

				if return_overflowing_tokens is True and return_offsets_mapping is False:
						raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

				# first, apply the image processor
				__A    =							self.image_processor(images=lowercase__							, return_tensors=lowercase__						)

				# second, apply the tokenizer
				if text is not None and self.image_processor.apply_ocr and text_pair is None:
						if isinstance(lowercase__							, lowercase__						):
								__A    =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
						__A    =							features["words"]

				__A    =							self.tokenizer(
				    text=text if text is not None else features["words"]							, text_pair=text_pair if text_pair is not None else None							, boxes=boxes if boxes is not None else features["boxes"]							, word_labels=lowercase__							, add_special_tokens=lowercase__							, padding=lowercase__							, truncation=lowercase__							, max_length=lowercase__							, stride=lowercase__							, pad_to_multiple_of=lowercase__							, return_token_type_ids=lowercase__							, return_attention_mask=lowercase__							, return_overflowing_tokens=lowercase__							, return_special_tokens_mask=lowercase__							, return_offsets_mapping=lowercase__							, return_length=lowercase__							, verbose=lowercase__							, return_tensors=lowercase__							, **lowercase__							, )

				# add pixel values
				__A    =							features.pop("pixel_values"						)
				if return_overflowing_tokens is True:
						__A    =							self.get_overflowing_images(lowercase__							, encoded_inputs["overflow_to_sample_mapping"]						)
				__A    =							images

				return encoded_inputs






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				List[Any]:
				# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
				__A    =							[]
				for sample_idx in overflow_to_sample_mapping:
						images_with_overflow.append(images[sample_idx]						)

				if len(lowercase__						) != len(lowercase__						):
						raise ValueError(
						    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
						    F""" {len(lowercase__						)} and {len(lowercase__						)}"""						)

				return images_with_overflow






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				Optional[Any]:
				return self.tokenizer.batch_decode(*lowercase__							, **lowercase__						)






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				List[str]:
				return self.tokenizer.decode(*lowercase__							, **lowercase__						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Any:
				return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase__							, )
				return self.image_processor_class






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase__							, )
				return self.image_processor



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    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_		)     ->Dict:
		for attribute in key.split("."		):
				A				=getattr(a_  ,				a_		)

		if weight_type is not None:
				A				=getattr(a_  ,				a_		).shape
		else:
				A				=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":
				A				=value
		elif weight_type == "weight_g":
				A				=value
		elif weight_type == "weight_v":
				A				=value
		elif weight_type == "bias":
				A				=value
		else:
				A				=value

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

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->Union[str, Any]:
		A				=[]
		A				=fairseq_model.state_dict()

		if not is_headless:
				A				=hf_model.dataavec_audio.feature_extractor
				A				=hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				A				=hf_model.feature_extractor
				A				=hf_model.encoder.pos_conv_embed

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

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

def    UpperCamelCase_ (	a_  ,				a_		)     ->List[str]:
		A				=path.split("."		)
		return reduce(a_  ,				a_  ,				a_		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=access_by_string(a_  ,				a_		)
		A				=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.'''		)
		A				=fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->List[Any]:
		A				=full_name.split("conv_layers."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id == 0:
				A				="conv"
		elif type_id == 2:
				A				="layer_norm"
		else:
				unused_weights.append(a_		)
				return

		set_weights(a_  ,				a_  ,				a_  ,				f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=full_name.split("pos_conv."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

		A				=name.split("."		)[-1]
		if type_id != 0:
				unused_weights.append(a_		)
				return
		else:
				A				="conv"

		set_weights(a_  ,				a_  ,				a_  ,				f'''layers.{layer_id}.{layer_type}.{weight_type}'''		)

@torch.no_grad()
def    UpperCamelCase_ (	a_  ,				a_  ,				a_=None  ,				a_=None  ,				a_=True		)     ->Optional[int]:
		if config_path is not None:
				A				=DataaVecAudioConfig.from_pretrained(a_		)
		else:
				A				=DataaVecAudioConfig()

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

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

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

		A				=load_dataavec(a_		)

		recursively_load_weights(a_  ,				a_  ,				not is_finetuned		)

		A				=WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"		)

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

		A				=processor(a_  ,				return_tensors="pt"  ,				padding=a_		)

		A				=inputs.input_values
		A				=inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

		hf_wavavec.eval()
		model.eval()
		if is_finetuned:
				A				=model(source=a_  ,				padding_mask=(1 - attention_mask)  ,				mask=a_  ,				features_only=a_		)[
				    "encoder_out"
				].transpose(0  ,				1		)
				A				=hf_wavavec(a_  ,				attention_mask=a_		)["logits"]

				A				=torch.argmax(a_  ,				dim=-1		)
				A				=processor.batch_decode(a_		)

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

		print(our_output.shape  ,				their_output.shape		)
		A				=torch.max(torch.abs(our_output - their_output		)		).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
		A				=torch.allclose(a_  ,				a_  ,				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(a_		)

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


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
	)




