





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_snake_case	:						Optional[int]					=					logging.get_logger(__name__)

_snake_case	:						List[Any]					=					{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
		import detectrona






class    _UpperCAmelCase  (      lowerCAmelCase__					):
					UpperCamelCase						=				'''layoutlmv2'''


					def __init__(   self     :Dict	,			__UpperCamelCase     :int=3_05_22	,			__UpperCamelCase     :Any=7_68	,			__UpperCamelCase     :Tuple=12	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :Tuple=30_72	,			__UpperCamelCase     :str="gelu"	,			__UpperCamelCase     :Optional[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :Dict=5_12	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Any=0.02	,			__UpperCamelCase     :Tuple=1e-12	,			__UpperCamelCase     :Dict=0	,			__UpperCamelCase     :Union[str, Any]=10_24	,			__UpperCamelCase     :int=1_28	,			__UpperCamelCase     :List[Any]=32	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=2_56	,			__UpperCamelCase     :Optional[int]=64	,			__UpperCamelCase     :int=True	,			__UpperCamelCase     :Tuple=[7, 7, 2_56]	,			__UpperCamelCase     :Union[str, Any]=1_28	,			__UpperCamelCase     :Optional[int]=1_28	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :str=False	,			__UpperCamelCase     :List[str]=None	,			**__UpperCamelCase     :int	,			):
												super().__init__(
												    vocab_size=snake_case__	,			hidden_size=snake_case__	,			num_hidden_layers=snake_case__	,			num_attention_heads=snake_case__	,			intermediate_size=snake_case__	,			hidden_act=snake_case__	,			hidden_dropout_prob=snake_case__	,			attention_probs_dropout_prob=snake_case__	,			max_position_embeddings=snake_case__	,			type_vocab_size=snake_case__	,			initializer_range=snake_case__	,			layer_norm_eps=snake_case__	,			pad_token_id=snake_case__	,			**snake_case__	,			)
												A						=    max_ad_position_embeddings
												A						=    max_rel_pos
												A						=    rel_pos_bins
												A						=    fast_qkv
												A						=    max_rel_ad_pos
												A						=    rel_ad_pos_bins
												A						=    convert_sync_batchnorm
												A						=    image_feature_pool_shape
												A						=    coordinate_size
												A						=    shape_size
												A						=    has_relative_attention_bias
												A						=    has_spatial_attention_bias
												A						=    has_visual_segment_embedding
												A						=    (
												    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
												)


					@classmethod
					def       lowerCamelCase			(   self     :Dict						):
												return {
												    "MODEL.MASK_ON": True,
												    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
												    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
												    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
												    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
												    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
												    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
												    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
												    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
												    "MODEL.POST_NMS_TOPK_TEST": 10_00,
												    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
												    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
												    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
												    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
												    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
												    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
												    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
												    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
												    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
												    "MODEL.RESNETS.DEPTH": 1_01,
												    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
												    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
												    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
												    "MODEL.RESNETS.NUM_GROUPS": 32,
												    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
												    "MODEL.RESNETS.STRIDE_IN_1X1": False,
												}


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    detectrona.config.get_cfg()
												for k, v in self.detectrona_config_args.items():
																			A						=    k.split("."						)
																			A						=    detectrona_config
																			for attribute in attributes[:-1]:
																										A						=    getattr(snake_case__	,			snake_case__						)
																			setattr(snake_case__	,			attributes[-1]	,			snake_case__						)

												return detectrona_config




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
	)




