
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import YolosImageProcessor
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=7			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=3_0			,				SCREAMING_SNAKE_CASE_    :     Dict=4_0_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     List[str]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     str=[0.5, 0.5, 0.5]			,				SCREAMING_SNAKE_CASE_    :     int=[0.5, 0.5, 0.5]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1 / 2_5_5			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=True			,				)			->	Union[str, Any]:
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					lowercase_	    =   size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   num_channels
					lowercase_	    =   min_resolution
					lowercase_	    =   max_resolution
					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   do_normalize
					lowercase_	    =   image_mean
					lowercase_	    =   image_std
					lowercase_	    =   do_rescale
					lowercase_	    =   rescale_factor
					lowercase_	    =   do_pad
	def 		_lowercase    (			self    :     Optional[int]			)			->	Any:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict=False			)			->	List[str]:
					if not batched:
									lowercase_	    =   image_inputs[0]
									if isinstance(SCREAMING_SNAKE_CASE_			,				Image.Image			):
													lowercase_    ,      lowercase_	    =   image.size
									else:
													lowercase_    ,      lowercase_	    =   image.shape[1], image.shape[2]
									if w < h:
													lowercase_	    =   int(self.size['''shortest_edge'''] * h / w			)
													lowercase_	    =   self.size['''shortest_edge''']
									elif w > h:
													lowercase_	    =   self.size['''shortest_edge''']
													lowercase_	    =   int(self.size['''shortest_edge'''] * w / h			)
									else:
													lowercase_	    =   self.size['''shortest_edge''']
													lowercase_	    =   self.size['''shortest_edge''']

					else:
									lowercase_	    =   []
									for image in image_inputs:
													lowercase_    ,      lowercase_	    =   self.get_expected_values([image]			)
													expected_values.append((expected_height, expected_width)			)
									lowercase_	    =   max(SCREAMING_SNAKE_CASE_			,				key=lambda SCREAMING_SNAKE_CASE_			: item[0]			)[0]
									lowercase_	    =   max(SCREAMING_SNAKE_CASE_			,				key=lambda SCREAMING_SNAKE_CASE_			: item[1]			)[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 					lowercase__(							UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      YolosImageProcessor if is_vision_available() else None
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   YolosImageProcessingTester(self			)
	@property
	def 		_lowercase    (			self    :     List[str]			)			->	int:
					return self.image_processor_tester.prepare_image_processor_dict()
	def 		_lowercase    (			self    :     Any			)			->	List[Any]:
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''image_mean'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''image_std'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''do_normalize'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''do_resize'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''size'''			)			)
	def 		_lowercase    (			self    :     str			)			->	Any:
					lowercase_	    =   self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size			,				{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}			)
					self.assertEqual(image_processor.do_pad			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.image_processing_class.from_dict(
					    self.image_processor_dict			,				size=4_2			,				max_size=8_4			,				pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(image_processor.size			,				{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}			)
					self.assertEqual(image_processor.do_pad			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	Tuple:
					pass
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Any:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				Image.Image			)

					# Test not batched input
					lowercase_	    =   image_processing(image_inputs[0]			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,				)

					# Test batched
					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).pixel_values
					self.assertEqual(
					    encoded_images.shape			,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,				)
	def 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				numpify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				np.ndarray			)

					# Test not batched input
					lowercase_	    =   image_processing(image_inputs[0]			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,				)

					# Test batched
					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,				)
	def 		_lowercase    (			self    :     List[Any]			)			->	str:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				torchify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			)

					# Test not batched input
					lowercase_	    =   image_processing(image_inputs[0]			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,				)

					# Test batched
					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,				)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					# Initialize image_processings
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					lowercase_	    =   self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE_			,				do_normalize=SCREAMING_SNAKE_CASE_			,				do_rescale=SCREAMING_SNAKE_CASE_			)
					# create random PyTorch tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				torchify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			)

					# Test whether the method "pad" and calling the image processor return the same tensors
					lowercase_	    =   image_processing_a.pad(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			)
					lowercase_	    =   image_processing_a(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			)

					self.assertTrue(
					    torch.allclose(encoded_images_with_method['''pixel_values''']			,				encoded_images['''pixel_values''']			,				atol=1e-4			)			)
	@slow
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					# prepare image and target
					lowercase_	    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,				'''r'''			) as f:
									lowercase_	    =   json.loads(f.read()			)

					lowercase_	    =   {'''image_id''': 3_9_7_6_9, '''annotations''': target}

					# encode them
					lowercase_	    =   YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''			)
					lowercase_	    =   image_processing(images=SCREAMING_SNAKE_CASE_			,				annotations=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			)

					# verify pixel values
					lowercase_	    =   torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
					self.assertEqual(encoding['''pixel_values'''].shape			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)			)

					# verify area
					lowercase_	    =   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify boxes
					lowercase_	    =   torch.Size([6, 4]			)
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-3			)			)
					# verify image_id
					lowercase_	    =   torch.tensor([3_9_7_6_9]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify is_crowd
					lowercase_	    =   torch.tensor([0, 0, 0, 0, 0, 0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify class_labels
					lowercase_	    =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify orig_size
					lowercase_	    =   torch.tensor([4_8_0, 6_4_0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify size
					lowercase_	    =   torch.tensor([8_0_0, 1_0_6_6]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,				SCREAMING_SNAKE_CASE_			)			)




	@slow
	def 		_lowercase    (			self    :     List[Any]			)			->	Union[str, Any]:
					# prepare image, target and masks_path
					lowercase_	    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,				'''r'''			) as f:
									lowercase_	    =   json.loads(f.read()			)

					lowercase_	    =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

					lowercase_	    =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

					# encode them
					lowercase_	    =   YolosImageProcessor(format='''coco_panoptic'''			)
					lowercase_	    =   image_processing(images=SCREAMING_SNAKE_CASE_			,				annotations=SCREAMING_SNAKE_CASE_			,				masks_path=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			)

					# verify pixel values
					lowercase_	    =   torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
					self.assertEqual(encoding['''pixel_values'''].shape			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)			)

					# verify area
					lowercase_	    =   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify boxes
					lowercase_	    =   torch.Size([6, 4]			)
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-3			)			)
					# verify image_id
					lowercase_	    =   torch.tensor([3_9_7_6_9]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify is_crowd
					lowercase_	    =   torch.tensor([0, 0, 0, 0, 0, 0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify class_labels
					lowercase_	    =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify masks
					lowercase_	    =   8_2_2_8_7_3
					self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,				SCREAMING_SNAKE_CASE_			)
					# verify orig_size
					lowercase_	    =   torch.tensor([4_8_0, 6_4_0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify size
					lowercase_	    =   torch.tensor([8_0_0, 1_0_6_6]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,				SCREAMING_SNAKE_CASE_			)			)








"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	hf_pointer.shape

			assert hf_shape == value.shape, (
			    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":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	None
															set_recursively(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
												continue
						if not is_used:
									unused_weights.append(snake_case__		)

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	int(items[1]		)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									_UpperCamelCase					:Optional[int]					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
									)
									_UpperCamelCase					:Optional[Any]					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									_UpperCamelCase					:int					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						_UpperCamelCase					:List[Any]					     =	model.cfg
						_UpperCamelCase					:List[Any]					     =	fs_config.final_dropout
						_UpperCamelCase					:Dict					     =	fs_config.layerdrop
			_UpperCamelCase					:Any					     =	fs_config.activation_dropout
			_UpperCamelCase					:List[str]					     =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			_UpperCamelCase					:Optional[Any]					     =	fs_config.attention_dropout
			_UpperCamelCase					:List[Any]					     =	fs_config.dropout_input
			_UpperCamelCase					:Dict					     =	fs_config.dropout
			_UpperCamelCase					:int					     =	fs_config.mask_channel_length
			_UpperCamelCase					:Tuple					     =	fs_config.mask_channel_prob
			_UpperCamelCase					:int					     =	fs_config.mask_length
			_UpperCamelCase					:Dict					     =	fs_config.mask_prob

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

			if is_finetuned:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
			else:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:str					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:List[Any]					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:Dict					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:str					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Optional[int]					     =	os.path.join(snake_case__       ,					'''vocab.json'''		)
									if not os.path.isdir(snake_case__		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
												return
									os.makedirs(snake_case__       ,					exist_ok=snake_case__		)
									with open(snake_case__       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(target_dict.indices       ,					snake_case__		)
									_UpperCamelCase					:int					     =	WavaVecaCTCTokenizer(
									    snake_case__       ,					unk_token=target_dict.unk_word       ,					pad_token=target_dict.pad_word       ,					bos_token=target_dict.bos_word       ,					eos_token=target_dict.eos_word       ,					word_delimiter_token='''|'''       ,					do_lower_case=snake_case__       ,					)
									_UpperCamelCase					:Dict					     =	WavaVecaProcessor(feature_extractor=snake_case__       ,					tokenizer=snake_case__		)
									processor.save_pretrained(snake_case__		)

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				: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(
					    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					UpperCamelCase__				:Tuple			   =	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

