




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase		:    int	      =		logging.get_logger(__name__)




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      DetaConfig(
			    num_queries=900,  encoder_ffn_dim=2048,  decoder_ffn_dim=2048,  num_feature_levels=5,  assign_first_stage=A,  with_box_refine=A,  two_stage=A,  )

			# set labels
			a   =      91
			a   =      "huggingface/label-files"
			a   =      "coco-detection-id2label.json"
			a   =      json.load(open(cached_download(hf_hub_url(A,  A,  repo_type="dataset"       )       ),  "r"       )       )
			a   =      {int(A       ): v for k, v in idalabel.items()}
			a   =      idalabel
			a   =      {v: k for k, v in idalabel.items()}

			return config




def       __magic_name__    (			A		: Union[str, Any]       ):






			'''simple docstring'''







			a   =      []

			# stem
			# fmt: off
			rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")       )
			rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")       )
			rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")       )
			rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")       )
			rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")       )
			# stages
			for stage_idx in range(len(config.backbone_config.depths       )       ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
									# shortcut
									if layer_idx == 0:
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
												    )       )
									# 3 convs
									for i in range(3       ):
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
												    )       )
												rename_keys.append(
												    (
												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
												        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
												    )       )
    # transformer encoder
			for i in range(config.encoder_layers       ):
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")       )
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")       )

			# transformer decoder
			for i in range(config.decoder_layers       ):
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")       )
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")       )

			# fmt: on

			return rename_keys




def       __magic_name__    (			A		: Dict,  A		: List[Any],  A		: Optional[Any]       ):






			'''simple docstring'''







			a   =      dct.pop(A       )
			a   =      val




def       __magic_name__    (			A		: List[Any],  A		: List[str]       ):






			'''simple docstring'''







			a   =      config.d_model
			for i in range(config.decoder_layers       ):
						# read in weights + bias of input projection layer of self-attention
						a   =      state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
						a   =      state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
						# next, add query, keys and values (in that order) to the state dict
						a   =      in_proj_weight[:hidden_size, :]
						a   =      in_proj_bias[:hidden_size]
						a   =      in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						a   =      in_proj_bias[hidden_size : hidden_size * 2]
						a   =      in_proj_weight[-hidden_size:, :]
						a   =      in_proj_bias[-hidden_size:]




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      "http://images.cocodataset.org/val2017/000000039769.jpg"
			a   =      Image.open(requests.get(A,  stream=A       ).raw       )

			return im




@torch.no_grad()
def       __magic_name__    (			A		: Union[str, Any],  A		: List[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      get_deta_config()

			# load original state dict
			if model_name == "deta-resnet-50":
						a   =      "adet_checkpoint0011.pth"
			elif model_name == "deta-resnet-50-24-epochs":
						a   =      "adet_2x_checkpoint0023.pth"
			else:
						raise ValueError(F"""Model name {model_name} not supported"""       )
			a   =      hf_hub_download(repo_id="nielsr/deta-checkpoints",  filename=A       )
			a   =      torch.load(A,  map_location="cpu"       )["model"]

			# rename keys
			a   =      create_rename_keys(A       )
			for src, dest in rename_keys:
						rename_key(A,  A,  A       )
			read_in_decoder_q_k_v(A,  A       )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									a   =      state_dict.pop(A       )
									a   =      val
						if "input_proj" in key:
									a   =      state_dict.pop(A       )
									a   =      val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									a   =      state_dict.pop(A       )
									a   =      val

    # finally, create HuggingFace model and load state dict
			a   =      DetaForObjectDetection(A       )
			model.load_state_dict(A       )
			model.eval()

			a   =      "cuda" if torch.cuda.is_available() else "cpu"
			model.to(A       )

			# load image processor
			a   =      DetaImageProcessor(format="coco_detection"       )

			# verify our conversion on image
			a   =      prepare_img()
			a   =      processor(images=A,  return_tensors="pt"       )
			a   =      encoding["pixel_values"]
			a   =      model(pixel_values.to(A       )       )

			# verify logits
			if model_name == "deta-resnet-50":
						a   =      torch.tensor(
						    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]       )
						a   =      torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]       )
			elif model_name == "deta-resnet-50-24-epochs":
						a   =      torch.tensor(
						    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]       )
						a   =      torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]       )

			assert torch.allclose(outputs.logits[0, :3, :3],  expected_logits.to(A       ),  atol=1E-4       )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],  expected_boxes.to(A       ),  atol=1E-4       )
			print("Everything ok!"       )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""       )
						Path(A       ).mkdir(exist_ok=A       )
						model.save_pretrained(A       )
						processor.save_pretrained(A       )

			# Push to hub
			if push_to_hub:
						print("Pushing model and processor to hub..."       )
						model.push_to_hub(F"""jozhang97/{model_name}"""       )
						processor.push_to_hub(F"""jozhang97/{model_name}"""       )


if __name__ == "__main__":
		__lowerCAmelCase		:    Optional[Any]	      =		argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name',
		    type=str,
		    default='deta-resnet-50',
		    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
		    help='Name of the model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    help='Path to the folder to output PyTorch model.',
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)
		__lowerCAmelCase		:    Tuple	      =		parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












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		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    '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		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			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		: Optional[Any]       ):
						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__":
		__lowerCAmelCase		:    List[str]	      =		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		:    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
		)






