
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 DetrImageProcessor




class 						lowercase__  (						unittest.TestCase  ):
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Tuple=7       ,   UpperCAmelCase_							:   Union[str, Any]=3       ,   UpperCAmelCase_							:   Optional[int]=30       ,   UpperCAmelCase_							:   List[Any]=400       ,   UpperCAmelCase_							:   Tuple=True       ,   UpperCAmelCase_							:   Optional[Any]=None       ,   UpperCAmelCase_							:   str=True       ,   UpperCAmelCase_							:   int=1 / 255       ,   UpperCAmelCase_							:   Any=True       ,   UpperCAmelCase_							:   str=[0.5, 0.5, 0.5]       ,   UpperCAmelCase_							:   Optional[Any]=[0.5, 0.5, 0.5]       ,   UpperCAmelCase_							:   Optional[int]=True       ,   ):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						SCREAMING_SNAKE_CASE__													= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
						SCREAMING_SNAKE_CASE__													= parent
						SCREAMING_SNAKE_CASE__													= batch_size
						SCREAMING_SNAKE_CASE__													= num_channels
						SCREAMING_SNAKE_CASE__													= min_resolution
						SCREAMING_SNAKE_CASE__													= max_resolution
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= size
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= rescale_factor
						SCREAMING_SNAKE_CASE__													= do_normalize
						SCREAMING_SNAKE_CASE__													= image_mean
						SCREAMING_SNAKE_CASE__													= image_std
						SCREAMING_SNAKE_CASE__													= do_pad
	def      A_				(			self							:   int							):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_pad": self.do_pad,
						}





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

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

						return expected_height, expected_width





@require_torch
@require_vision
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[int]				       =DetrImageProcessor if is_vision_available() else None
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= DetrImageProcessingTester(self							)
	@property
	def      A_				(			self							:   Any							):
						return self.image_processor_tester.prepare_image_processor_dict()
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'image_mean'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'image_std'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'do_normalize'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'do_rescale'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'rescale_factor'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'do_resize'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'size'							)							)
						self.assertTrue(hasattr(UpperCAmelCase_       ,   'do_pad'							)							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= 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       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.image_processing_class.from_dict(
						    self.image_processor_dict       ,   size=42       ,   max_size=84       ,   pad_and_return_pixel_mask=UpperCAmelCase_							)
						self.assertEqual(image_processor.size       ,   {'shortest_edge': 42, 'longest_edge': 84}							)
						self.assertEqual(image_processor.do_pad       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						pass
	def      A_				(			self							:   List[Any]							):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__													= self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						SCREAMING_SNAKE_CASE__													= prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=UpperCAmelCase_							)
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase_       ,   Image.Image							)

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

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_							)

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

						# Test batched
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,   batched=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= image_processing(UpperCAmelCase_       ,   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      A_				(			self							:   str							):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__													= self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__													= prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=UpperCAmelCase_       ,   numpify=UpperCAmelCase_							)
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase_       ,   np.ndarray							)

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

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_							)

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

						# Test batched
						SCREAMING_SNAKE_CASE__													= image_processing(UpperCAmelCase_       ,   return_tensors='pt'							).pixel_values

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,   batched=UpperCAmelCase_							)

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

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

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_							)

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

						# Test batched
						SCREAMING_SNAKE_CASE__													= image_processing(UpperCAmelCase_       ,   return_tensors='pt'							).pixel_values

						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,   batched=UpperCAmelCase_							)

						self.assertEqual(
						    encoded_images.shape       ,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )       ,   )
	@slow
	def      A_				(			self							:   Any							):
						# prepare image and target
						SCREAMING_SNAKE_CASE__													= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,   'r'							) as f:
											SCREAMING_SNAKE_CASE__													= json.loads(f.read()							)

						SCREAMING_SNAKE_CASE__													= {'image_id': 39769, 'annotations': target}

						# encode them
						SCREAMING_SNAKE_CASE__													= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'							)
						SCREAMING_SNAKE_CASE__													= image_processing(images=UpperCAmelCase_       ,   annotations=UpperCAmelCase_       ,   return_tensors='pt'							)

						# verify pixel values
						SCREAMING_SNAKE_CASE__													= torch.Size([1, 3, 800, 1066]							)
						self.assertEqual(encoding['pixel_values'].shape       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,   UpperCAmelCase_       ,   atol=1e-4							)							)

						# verify area
						SCREAMING_SNAKE_CASE__													= torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,   UpperCAmelCase_							)							)
						# verify boxes
						SCREAMING_SNAKE_CASE__													= torch.Size([6, 4]							)
						self.assertEqual(encoding['labels'][0]['boxes'].shape       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,   UpperCAmelCase_       ,   atol=1e-3							)							)
						# verify image_id
						SCREAMING_SNAKE_CASE__													= torch.tensor([39769]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,   UpperCAmelCase_							)							)
						# verify is_crowd
						SCREAMING_SNAKE_CASE__													= torch.tensor([0, 0, 0, 0, 0, 0]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,   UpperCAmelCase_							)							)
						# verify class_labels
						SCREAMING_SNAKE_CASE__													= torch.tensor([75, 75, 63, 65, 17, 17]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,   UpperCAmelCase_							)							)
						# verify orig_size
						SCREAMING_SNAKE_CASE__													= torch.tensor([480, 640]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,   UpperCAmelCase_							)							)
						# verify size
						SCREAMING_SNAKE_CASE__													= torch.tensor([800, 1066]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,   UpperCAmelCase_							)							)





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

						SCREAMING_SNAKE_CASE__													= {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

						# encode them
						SCREAMING_SNAKE_CASE__													= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'							)
						SCREAMING_SNAKE_CASE__													= image_processing(images=UpperCAmelCase_       ,   annotations=UpperCAmelCase_       ,   masks_path=UpperCAmelCase_       ,   return_tensors='pt'							)

						# verify pixel values
						SCREAMING_SNAKE_CASE__													= torch.Size([1, 3, 800, 1066]							)
						self.assertEqual(encoding['pixel_values'].shape       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,   UpperCAmelCase_       ,   atol=1e-4							)							)

						# verify area
						SCREAMING_SNAKE_CASE__													= torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,   UpperCAmelCase_							)							)
						# verify boxes
						SCREAMING_SNAKE_CASE__													= torch.Size([6, 4]							)
						self.assertEqual(encoding['labels'][0]['boxes'].shape       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,   UpperCAmelCase_       ,   atol=1e-3							)							)
						# verify image_id
						SCREAMING_SNAKE_CASE__													= torch.tensor([39769]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,   UpperCAmelCase_							)							)
						# verify is_crowd
						SCREAMING_SNAKE_CASE__													= torch.tensor([0, 0, 0, 0, 0, 0]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,   UpperCAmelCase_							)							)
						# verify class_labels
						SCREAMING_SNAKE_CASE__													= torch.tensor([17, 17, 63, 75, 75, 93]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,   UpperCAmelCase_							)							)
						# verify masks
						SCREAMING_SNAKE_CASE__													= 822873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,   UpperCAmelCase_							)
						# verify orig_size
						SCREAMING_SNAKE_CASE__													= torch.tensor([480, 640]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,   UpperCAmelCase_							)							)
						# verify size
						SCREAMING_SNAKE_CASE__													= torch.tensor([800, 1066]							)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,   UpperCAmelCase_							)							)







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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""",
}
__snake_case								=  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					for attribute in key.split('.'		):
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		)

					if weight_type is not None:
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		).shape
					else:
										SCREAMING_SNAKE_CASE__													= 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":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE__													= value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE__													= value
					else:
										SCREAMING_SNAKE_CASE__													= value

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE__													= hf_model.feature_extractor
					SCREAMING_SNAKE_CASE__													= hf_model.adapter

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE__													= False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	hf_model.config.feat_extract_norm == 'group'	,	)
															SCREAMING_SNAKE_CASE__													= True
										elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']		):
															load_adapter(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
																									SCREAMING_SNAKE_CASE__													= True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE__													= name.split(UpperCamelCase_		)[0].split('.'		)[-2]
																														SCREAMING_SNAKE_CASE__													= mapped_key.replace('*'	,	UpperCamelCase_		)
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight_g'
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight_v'
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE__													= 'bias'
																									elif "weight" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight'
																									else:
																														SCREAMING_SNAKE_CASE__													= None
																									set_recursively(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				continue
										if not is_used:
															unused_weights.append(UpperCamelCase_		)

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('conv_layers.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)
					SCREAMING_SNAKE_CASE__													= int(items[0]		)
					SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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."
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('adaptor.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)

					if items[1].isdigit():
										SCREAMING_SNAKE_CASE__													= int(items[1]		)
					else:
										SCREAMING_SNAKE_CASE__													= None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'		)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer bias was initialized from {full_name}.'		)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer weight was initialized from {full_name}.'		)
					elif isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= emb.weight.shape
					SCREAMING_SNAKE_CASE__													= nn.Linear(UpperCamelCase_	,	UpperCamelCase_	,	bias=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= emb.weight.data
					return lin_layer






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= WavaVecaConfig.from_pretrained(
					    UpperCamelCase_	,	add_adapter=UpperCamelCase_	,	adapter_stride=UpperCamelCase_	,	adapter_kernel_size=UpperCamelCase_	,	use_auth_token=UpperCamelCase_	,	output_hidden_size=UpperCamelCase_	,	)
					SCREAMING_SNAKE_CASE__													= MBartConfig.from_pretrained(UpperCamelCase_		)

					# load model
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]	,	arg_overrides={
					        'config_yaml': config_yaml_path,
					        'data': '/'.join(dict_path.split('/'		)[:-1]		),
					        'w2v_path': checkpoint_path,
					        'load_pretrained_decoder_from': None,
					    }	,	)
					SCREAMING_SNAKE_CASE__													= model[0].eval()

					# load feature extractor
					SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_	,	use_auth_token=UpperCamelCase_		)

					# set weights for wav2vec2 encoder
					SCREAMING_SNAKE_CASE__													= WavaVecaModel(UpperCamelCase_		)

					recursively_load_weights_wavaveca(model.encoder	,	UpperCamelCase_		)

					# load decoder weights
					SCREAMING_SNAKE_CASE__													= MBartForCausalLM(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,	strict=UpperCamelCase_		)
					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}'		)

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderModel(encoder=UpperCamelCase_	,	decoder=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= False

					SCREAMING_SNAKE_CASE__													= MBartaaTokenizer(UpperCamelCase_		)
					tokenizer.save_pretrained(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= hf_wavavec.config.to_dict()
					SCREAMING_SNAKE_CASE__													= tokenizer.pad_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.bos_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 'mbart50'
					SCREAMING_SNAKE_CASE__													= 'wav2vec2'

					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 250004
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_		)

					hf_wavavec.save_pretrained(UpperCamelCase_		)
					feature_extractor.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
				__snake_case								=  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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-xls-r-1b""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/mbart-large-50-one-to-many-mmt""",
				    type=str,
				    help="""Path to hf decoder checkpoint config""",
				)
				parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
				parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
				parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
				parser.add_argument("""--encoder_output_dim""", default=10_24, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

				__snake_case								=  parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)





