



import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[str]=13				, __lowerCAmelCase      :							Dict=64				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Dict=16				, __lowerCAmelCase      :							Any=[1, 2, 1]				, __lowerCAmelCase      :							Optional[Any]=[2, 4, 8]				, __lowerCAmelCase      :							str=3				, __lowerCAmelCase      :							Any=2.0				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Optional[Any]=0.0				, __lowerCAmelCase      :							List[Any]=0.0				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Any="gelu"				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Union[str, Any]=0.0_2				, __lowerCAmelCase      :							Any=1E-5				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							Optional[int]=10				, __lowerCAmelCase      :							List[str]=["stage1", "stage2"]				, __lowerCAmelCase      :							List[str]=[1, 2]				, )							->					Any:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		image_size
    a							=		patch_size
    a							=		num_channels
    a							=		embed_dim
    a							=		depths
    a							=		num_heads
    a							=		kernel_size
    a							=		mlp_ratio
    a							=		qkv_bias
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		drop_path_rate
    a							=		hidden_act
    a							=		patch_norm
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		is_training
    a							=		scope
    a							=		use_labels
    a							=		num_labels
    a							=		out_features
    a							=		out_indices

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

    a							=		None
    if self.use_labels:
     a							=		ids_tensor([self.batch_size]				, self.num_labels				)

    a							=		self.get_config()

    return config, pixel_values, labels

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return NatConfig(
        num_labels=self.num_labels				, image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, embed_dim=self.embed_dim				, depths=self.depths				, num_heads=self.num_heads				, kernel_size=self.kernel_size				, mlp_ratio=self.mlp_ratio				, qkv_bias=self.qkv_bias				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, drop_path_rate=self.drop_path_rate				, hidden_act=self.hidden_act				, patch_norm=self.patch_norm				, layer_norm_eps=self.layer_norm_eps				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, )

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a							=		NatModel(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    a							=		a							=		(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
    a							=		int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

    self.parent.assertEqual(
        result.last_hidden_state.shape				, (self.batch_size, expected_height, expected_width, expected_dim)				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				)							->					Optional[int]:


    """simple docstring"""






    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				, labels=__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

    # test greyscale images
    a							=		1
    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    a							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    a							=		model(__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					Optional[int]:


    """simple docstring"""






    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps				)				, len(config.out_features				)				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[0], 16, 16]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, len(config.out_features				)				)

    # verify backbone works with out_features=None
    a							=		None
    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps				)				, 1				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[-1], 4, 4]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, 1				)




   def 		A				(   self      :							int				)							->					int:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    a		,	a		,	a							=		config_and_inputs
    a							=		{"pixel_values": pixel_values}
    return config, inputs_dict



@require_natten
@require_torch
class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      False

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		NatModelTester(self				)
    a							=		ConfigTester(self				, config_class=__lowerCAmelCase				, embed_dim=37				)

   def 		A				(   self      :							Optional[Any]				)							->					Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    return

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase				)

   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*__lowerCAmelCase				)

   @unittest.skip(reason="Nat does not use inputs_embeds"				)
   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="Nat does not use feedforward chunking"				)
   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    pass

   def 		A				(   self      :							str				)							->					Union[str, Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a							=		model_class(__lowerCAmelCase				)
     self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)				)
     a							=		model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(__lowerCAmelCase				, nn.Linear				)				)

   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a							=		model_class(__lowerCAmelCase				)
     a							=		inspect.signature(model.forward				)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     a							=		[*signature.parameters.keys()]

     a							=		["pixel_values"]
     self.assertListEqual(arg_names[:1]				, __lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[int]:


    """simple docstring"""






    self.skipTest("Nat's attention operation is handled entirely by NATTEN."				)

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				)							->					int:


    """simple docstring"""






    a							=		model_class(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    with torch.no_grad():
     a							=		model(**self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)				)

    a							=		outputs.hidden_states

    a							=		getattr(
        self.model_tester				, "expected_num_hidden_layers"				, len(self.model_tester.depths				) + 1				)
    self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

    # Nat has a different seq_length
    a							=		(
        config.patch_size
        if isinstance(config.patch_size				, collections.abc.Iterable				)
        else (config.patch_size, config.patch_size)
    )

    a							=		image_size[0] // patch_size[0]
    a							=		image_size[1] // patch_size[1]

    self.assertListEqual(
        list(hidden_states[0].shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

    if model_class.__name__ != "NatBackbone":
     a							=		outputs.reshaped_hidden_states
     self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

     a		,	a		,	a		,	a							=		reshaped_hidden_states[0].shape
     a							=		(
         reshaped_hidden_states[0].view(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				).permute(0				, 2				, 3				, 1				)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size				, collections.abc.Iterable				)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
     a							=		True
     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a							=		True

     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

   @slow
   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		NatModel.from_pretrained(__lowerCAmelCase				)
     self.assertIsNotNone(__lowerCAmelCase				)




   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(__lowerCAmelCase				)
    for model_class in self.all_model_classes:
     a							=		model_class(config=__lowerCAmelCase				)
     for name, param in model.named_parameters():
      if "embeddings" not in name and param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1E9).round() / 1E9).item()				, [0.0, 1.0]				, msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				, )



@require_natten
@require_vision
@require_torch
class 		_lowercase		(							unittest.TestCase						):

   @cached_property
   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None




   @slow
   def 		A				(   self      :							Dict				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(__lowerCAmelCase				)
    a							=		self.default_image_processor

    a							=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    a							=		image_processor(images=__lowerCAmelCase				, return_tensors="pt"				).to(__lowerCAmelCase				)

    # forward pass
    with torch.no_grad():
     a							=		model(**__lowerCAmelCase				)

    # verify the logits
    a							=		torch.Size((1, 1000)				)
    self.assertEqual(outputs.logits.shape				, __lowerCAmelCase				)
    a							=		torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]				).to(__lowerCAmelCase				)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]				, __lowerCAmelCase				, atol=1E-4				)				)



@require_torch
@require_natten
class 		_lowercase		(							unittest.TestCase, UpperCAmelCase__						):
   _UpperCAmelCase  						=      (NatBackbone,) if is_torch_available() else ()
   _UpperCAmelCase  						=      NatConfig

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		NatModelTester(self				)



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
	)




