





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 DetaImageProcessor





class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=3     ,					_A=30     ,					_A=400     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=[0.5, 0.5, 0.5]     ,					_A=[0.5, 0.5, 0.5]     ,					_A=True     ,					_A=1 / 255     ,					_A=True     ,					):
									__A	:						Optional[Any]       =				size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
									__A	:						Optional[Any]       =				parent
									__A	:						int       =				batch_size
									__A	:						Tuple       =				num_channels
									__A	:						List[str]       =				min_resolution
									__A	:						Any       =				max_resolution
									__A	:						Union[str, Any]       =				do_resize
									__A	:						str       =				size
									__A	:						Optional[int]       =				do_normalize
									__A	:						Optional[int]       =				image_mean
									__A	:						Union[str, Any]       =				image_std
									__A	:						List[str]       =				do_rescale
									__A	:						int       =				rescale_factor
									__A	:						Union[str, Any]       =				do_pad



				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ,					_A     ,					_A=False     ):






									if not batched:
														__A	:						Optional[int]       =				image_inputs[0]
														if isinstance(_A     ,					Image.Image     ):
																			__A	:						List[str]       =				image.size
														else:
																			__A	:						List[str]       =				image.shape[1], image.shape[2]
														if w < h:
																			__A	:						Any       =				int(self.size['shortest_edge'] * h / w     )
																			__A	:						str       =				self.size["""shortest_edge"""]
														elif w > h:
																			__A	:						int       =				self.size["""shortest_edge"""]
																			__A	:						Tuple       =				int(self.size['shortest_edge'] * w / h     )
														else:
																			__A	:						Union[str, Any]       =				self.size["""shortest_edge"""]
																			__A	:						str       =				self.size["""shortest_edge"""]

									else:
														__A	:						int       =				[]
														for image in image_inputs:
																			__A	:						str       =				self.get_expected_values([image]     )
																			expected_values.append((expected_height, expected_width)     )
														__A	:						List[Any]       =				max(_A     ,					key=lambda _A     : item[0]     )[0]
														__A	:						Optional[int]       =				max(_A     ,					key=lambda _A     : item[1]     )[1]

									return expected_height, expected_width





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	DetaImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				DetaImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(_A     ,					'image_mean'     )     )
									self.assertTrue(hasattr(_A     ,					'image_std'     )     )
									self.assertTrue(hasattr(_A     ,					'do_normalize'     )     )
									self.assertTrue(hasattr(_A     ,					'do_resize'     )     )
									self.assertTrue(hasattr(_A     ,					'do_rescale'     )     )
									self.assertTrue(hasattr(_A     ,					'do_pad'     )     )
									self.assertTrue(hasattr(_A     ,					'size'     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 18, 'longest_edge': 1333}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__A	:						Optional[Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					Image.Image     )

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

									__A	:						List[Any]       =				self.image_processor_tester.get_expected_values(_A     )

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

									# Test batched
									__A	:						Any       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									__A	:						Tuple       =				image_processing(_A     ,					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 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__A	:						Dict       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					numpify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					np.ndarray     )

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

									__A	:						List[str]       =				self.image_processor_tester.get_expected_values(_A     )

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

									# Test batched
									__A	:						Union[str, Any]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A	:						Tuple       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Tuple       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

									# Test not batched input
									__A	:						List[Any]       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A	:						Any       =				self.image_processor_tester.get_expected_values(_A     )

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

									# Test batched
									__A	:						str       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A	:						Tuple       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

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



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,					'r'     ) as f:
														__A	:						Optional[int]       =				json.loads(f.read()     )

									__A	:						Optional[int]       =				{"""image_id""": 39769, """annotations""": target}

									# encode them
									__A	:						Union[str, Any]       =				DetaImageProcessor()
									__A	:						Dict       =				image_processing(images=_A     ,					annotations=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						int       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						Optional[int]       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						Optional[int]       =				torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						List[str]       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Tuple       =				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						Optional[int]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						List[str]       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						Tuple       =				torch.tensor([75, 75, 63, 65, 17, 17]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify orig_size
									__A	:						Union[str, Any]       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						List[str]       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,					'r'     ) as f:
														__A	:						Any       =				json.loads(f.read()     )

									__A	:						Dict       =				{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

									__A	:						List[Any]       =				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

									# encode them
									__A	:						Optional[int]       =				DetaImageProcessor(format='coco_panoptic'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					masks_path=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						List[str]       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						str       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						int       =				torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						List[str]       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						int       =				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						str       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						Dict       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						Union[str, Any]       =				torch.tensor([17, 17, 63, 75, 75, 93]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify masks
									__A	:						Union[str, Any]       =				822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,					_A     )
									# verify orig_size
									__A	:						Any       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						str       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )








"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase    :     Optional[Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Optional[int]   						=		{
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase    :     Dict   						=		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      int							)		->       List[str]:

							'''simple docstring'''



							for attribute in key.split("""."""							):
														__UpperCAmelCase   :						Tuple      							=					getattr(_UpperCamelCase       ,			_UpperCamelCase							)

							if weight_type is not None:
														__UpperCAmelCase   :						int      							=					getattr(_UpperCamelCase       ,			_UpperCamelCase							).shape
							else:
														__UpperCAmelCase   :						List[Any]      							=					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   :						Optional[Any]      							=					value
							elif weight_type == "weight_g":
														__UpperCAmelCase   :						List[Any]      							=					value
							elif weight_type == "weight_v":
														__UpperCAmelCase   :						Dict      							=					value
							elif weight_type == "bias":
														__UpperCAmelCase   :						List[str]      							=					value
							else:
														__UpperCAmelCase   :						List[str]      							=					value

							logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Optional[Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[]
							__UpperCAmelCase   :						int      							=					fairseq_model.state_dict()

							__UpperCAmelCase   :						Union[str, Any]      							=					hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							__UpperCAmelCase   :						Tuple      							=					None

							for name, value in fairseq_dict.items():
														__UpperCAmelCase   :						Union[str, Any]      							=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			hf_model.config.feat_extract_norm == """group"""       ,			)
																					__UpperCAmelCase   :						int      							=					True
														elif name.split("""."""							)[0] == "proj":
																					__UpperCAmelCase   :						Optional[Any]      							=					fairseq_model.proj
																					__UpperCAmelCase   :						Union[str, Any]      							=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
																																			__UpperCAmelCase   :						Dict      							=					True
																																			if "*" in mapped_key:
																																										__UpperCAmelCase   :						Any      							=					name.split(_UpperCamelCase							)[0].split("""."""							)[-2]
																																										__UpperCAmelCase   :						Tuple      							=					mapped_key.replace("""*"""       ,			_UpperCamelCase							)
																																			if "weight_g" in name:
																																										__UpperCAmelCase   :						Any      							=					"""weight_g"""
																																			elif "weight_v" in name:
																																										__UpperCAmelCase   :						Union[str, Any]      							=					"""weight_v"""
																																			elif "bias" in name:
																																										__UpperCAmelCase   :						List[Any]      							=					"""bias"""
																																			elif "weight" in name:
																																										__UpperCAmelCase   :						List[Any]      							=					"""weight"""
																																			else:
																																										__UpperCAmelCase   :						Dict      							=					None
																																			set_recursively(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)
																												continue
														if not is_used:
																					unused_weights.append(_UpperCamelCase							)

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

							return proj_weight
def      lowerCamelCase       (     _UpperCamelCase		:      str       ,			_UpperCamelCase		:      str       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Dict							)		->       Tuple:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					full_name.split("""conv_layers."""							)[-1]
							__UpperCAmelCase   :						int      							=					name.split("""."""							)
							__UpperCAmelCase   :						List[str]      							=					int(items[0]							)
							__UpperCAmelCase   :						Union[str, Any]      							=					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   :						Tuple      							=					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   :						List[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   :						Optional[Any]      							=					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   :						Optional[int]      							=					value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
							else:
														unused_weights.append(_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase							,__UpperCAmelCase   :						Tuple      							=					emb.weight.shape
							__UpperCAmelCase   :						Optional[int]      							=					nn.Linear(_UpperCamelCase       ,			_UpperCamelCase       ,			bias=_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					emb.weight.data
							return lin_layer
def      lowerCamelCase       (     _UpperCamelCase		:      Any							)		->       Any:

							'''simple docstring'''



							with open(_UpperCamelCase       ,			"""r"""       ,			encoding="""utf-8"""							) as f:
														__UpperCAmelCase   :						Optional[int]      							=					f.readlines()
														__UpperCAmelCase   :						Optional[Any]      							=					[line.split(""" """							)[0] for line in lines]

							__UpperCAmelCase   :						List[Any]      							=					len(_UpperCamelCase							)

							__UpperCAmelCase   :						Any      							=					{
							    """<s>""": 0,
							    """<pad>""": 1,
							    """</s>""": 2,
							    """<unk>""": 3,
							}

							vocab_dict.update(dict(zip(_UpperCamelCase       ,			range(4       ,			num_words + 4							)							)							)							)
							return vocab_dict
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      List[str]       ,			)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[Any]      							=					WavaVecaConfig.from_pretrained(_UpperCamelCase							)
							__UpperCAmelCase   :						Optional[int]      							=					SpeechaTextaConfig.from_pretrained(
							    _UpperCamelCase       ,			vocab_size=_UpperCamelCase       ,			decoder_layers=_UpperCamelCase       ,			do_stable_layer_norm=_UpperCamelCase							)

							__UpperCAmelCase   :						Any      							=					WavaVecaFeatureExtractor(
							    feature_size=1       ,			sampling_rate=1_6_0_0_0       ,			padding_value=0       ,			do_normalize=_UpperCamelCase       ,			return_attention_mask=_UpperCamelCase       ,			)

							__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						int      							=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]       ,			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
							__UpperCAmelCase   :						List[Any]      							=					model[0].eval()

							# set weights for wav2vec2 encoder
							__UpperCAmelCase   :						int      							=					WavaVecaModel(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					recursively_load_weights_wavaveca(model.encoder       ,			_UpperCamelCase							)

							__UpperCAmelCase   :						List[str]      							=					SpeechaTextaForCausalLM(_UpperCamelCase							)
							__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,			strict=_UpperCamelCase							)

							# set output linear layer
							unexpected_keys.remove("""embed_out"""							)
							__UpperCAmelCase   :						List[str]      							=					nn.Parameter(model.decoder.embed_out.detach()							)

							# layer norm is init to identity matrix so leaving it is fine
							logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''							)
							logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''							)

							__UpperCAmelCase   :						str      							=					SpeechEncoderDecoderModel(encoder=_UpperCamelCase       ,			decoder=_UpperCamelCase							)
							__UpperCAmelCase   :						Any      							=					False

							# add projection layer
							__UpperCAmelCase   :						str      							=					nn.Parameter(projection_layer.weight							)
							__UpperCAmelCase   :						str      							=					nn.Parameter(projection_layer.bias							)

							__UpperCAmelCase   :						List[Any]      							=					create_vocab_dict(_UpperCamelCase							)

							with open(os.path.join(_UpperCamelCase       ,			"""vocab.json"""							)       ,			"""w"""							) as fp:
														json.dump(_UpperCamelCase       ,			_UpperCamelCase							)

							__UpperCAmelCase   :						List[str]      							=					SpeechaTextaTokenizer(os.path.join(_UpperCamelCase       ,			"""vocab.json"""							)							)
							tokenizer.save_pretrained(_UpperCamelCase							)

							__UpperCAmelCase   :						Dict      							=					hf_wavavec.config.to_dict()
							__UpperCAmelCase   :						int      							=					tokenizer.pad_token_id
							__UpperCAmelCase   :						List[str]      							=					tokenizer.bos_token_id
							__UpperCAmelCase   :						Tuple      							=					tokenizer.eos_token_id
							__UpperCAmelCase   :						int      							=					"""speech_to_text_2"""
							__UpperCAmelCase   :						Optional[int]      							=					"""wav2vec2"""

							__UpperCAmelCase   :						Union[str, Any]      							=					SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase							)

							hf_wavavec.save_pretrained(_UpperCamelCase							)
							feature_extractor.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Tuple   						=		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(
							    '--encoder_config_path',
							    default='facebook/wav2vec2-large-lv60',
							    type=str,
							    help='Path to hf encoder wav2vec2 checkpoint config',
							)
							parser.add_argument(
							    '--decoder_config_path',
							    default='facebook/s2t-small-mustc-en-fr-st',
							    type=str,
							    help='Path to hf decoder s2t checkpoint config',
							)
							parser.add_argument('--vocab_size', default=1_0224, type=int, help='Vocab size of decoder')
							parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

							UpperCAmelCase    :     int   						=		parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)
