




'''simple docstring'''

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 						a				(   unittest.TestCase     ):


				def __init__(		self    :					int       ,				lowercase_    :					List[str]       ,				lowercase_    :					List[str]=7       ,				lowercase_    :					Dict=3       ,				lowercase_    :					Tuple=30       ,				lowercase_    :					Any=400       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=None       ,				lowercase_    :					int=True       ,				lowercase_    :					Any=1 / 255       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Any=[0.5, 0.5, 0.5]       ,				lowercase_    :					Optional[int]=[0.5, 0.5, 0.5]       ,				lowercase_    :					Union[str, Any]=True       ,				):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									snake_case_									=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				num_channels
									snake_case_									=				min_resolution
									snake_case_									=				max_resolution
									snake_case_									=				do_resize
									snake_case_									=				size
									snake_case_									=				do_rescale
									snake_case_									=				rescale_factor
									snake_case_									=				do_normalize
									snake_case_									=				image_mean
									snake_case_									=				image_std
									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    :					Dict       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Union[str, Any]=False					):
									if not batched:
														snake_case_									=				image_inputs[0]
														if isinstance(lowercase_       ,				Image.Image					):
																			snake_case_			,snake_case_									=				image.size
														else:
																			snake_case_			,snake_case_									=				image.shape[1], image.shape[2]
														if w < h:
																			snake_case_									=				int(self.size['''shortest_edge'''] * h / w					)
																			snake_case_									=				self.size['''shortest_edge''']
														elif w > h:
																			snake_case_									=				self.size['''shortest_edge''']
																			snake_case_									=				int(self.size['''shortest_edge'''] * w / h					)
														else:
																			snake_case_									=				self.size['''shortest_edge''']
																			snake_case_									=				self.size['''shortest_edge''']

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

									return expected_height, expected_width







@require_torch
@require_vision
class 						a				(   _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		DetrImageProcessor if is_vision_available() else None


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				DetrImageProcessingTester(self					)


				@property
				def   A_ (		self    :					Optional[int]					):
									return self.image_processor_tester.prepare_image_processor_dict()


				def   A_ (		self    :					str					):
									snake_case_									=				self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(lowercase_       ,				'''image_mean'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''image_std'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''do_normalize'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''do_rescale'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''rescale_factor'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''do_resize'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''size'''					)					)
									self.assertTrue(hasattr(lowercase_       ,				'''do_pad'''					)					)


				def   A_ (		self    :					Dict					):
									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       ,				lowercase_					)

									snake_case_									=				self.image_processing_class.from_dict(
									    self.image_processor_dict       ,				size=42       ,				max_size=84       ,				pad_and_return_pixel_mask=lowercase_					)
									self.assertEqual(image_processor.size       ,				{'''shortest_edge''': 42, '''longest_edge''': 84}					)
									self.assertEqual(image_processor.do_pad       ,				lowercase_					)


				def   A_ (		self    :					List[Any]					):
									pass


				def   A_ (		self    :					str					):
									# Initialize image_processing
									snake_case_									=				self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									snake_case_									=				prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowercase_					)
									for image in image_inputs:
														self.assertIsInstance(lowercase_       ,				Image.Image					)

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

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

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

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

									snake_case_									=				image_processing(lowercase_       ,				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    :					List[Any]					):
									# Initialize image_processing
									snake_case_									=				self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									snake_case_									=				prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowercase_       ,				numpify=lowercase_					)
									for image in image_inputs:
														self.assertIsInstance(lowercase_       ,				np.ndarray					)

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

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

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

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

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

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


				def   A_ (		self    :					Dict					):
									# Initialize image_processing
									snake_case_									=				self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									snake_case_									=				prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowercase_       ,				torchify=lowercase_					)
									for image in image_inputs:
														self.assertIsInstance(lowercase_       ,				torch.Tensor					)

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

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

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

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

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

									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    :					str					):
									# prepare image and target
									snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,				'''r'''					) as f:
														snake_case_									=				json.loads(f.read()					)

									snake_case_									=				{'''image_id''': 3_9769, '''annotations''': target}

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

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

									snake_case_									=				torch.tensor([0.2796, 0.3138, 0.3481]					)
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,				lowercase_       ,				atol=1e-4					)					)

									# verify area
									snake_case_									=				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,				lowercase_					)					)
									# verify boxes
									snake_case_									=				torch.Size([6, 4]					)
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,				lowercase_					)
									snake_case_									=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,				lowercase_       ,				atol=1e-3					)					)
									# verify image_id
									snake_case_									=				torch.tensor([3_9769]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,				lowercase_					)					)
									# verify is_crowd
									snake_case_									=				torch.tensor([0, 0, 0, 0, 0, 0]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,				lowercase_					)					)
									# verify class_labels
									snake_case_									=				torch.tensor([75, 75, 63, 65, 17, 17]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,				lowercase_					)					)
									# verify orig_size
									snake_case_									=				torch.tensor([480, 640]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,				lowercase_					)					)
									# verify size
									snake_case_									=				torch.tensor([800, 1066]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,				lowercase_					)					)


				@slow
				def   A_ (		self    :					Optional[int]					):
									# prepare image, target and masks_path
									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:
														snake_case_									=				json.loads(f.read()					)

									snake_case_									=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

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

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

									snake_case_									=				torch.tensor([0.2796, 0.3138, 0.3481]					)
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,				lowercase_       ,				atol=1e-4					)					)

									# verify area
									snake_case_									=				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,				lowercase_					)					)
									# verify boxes
									snake_case_									=				torch.Size([6, 4]					)
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,				lowercase_					)
									snake_case_									=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,				lowercase_       ,				atol=1e-3					)					)
									# verify image_id
									snake_case_									=				torch.tensor([3_9769]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,				lowercase_					)					)
									# verify is_crowd
									snake_case_									=				torch.tensor([0, 0, 0, 0, 0, 0]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,				lowercase_					)					)
									# verify class_labels
									snake_case_									=				torch.tensor([17, 17, 63, 75, 75, 93]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,				lowercase_					)					)
									# verify masks
									snake_case_									=				82_2873
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,				lowercase_					)
									# verify orig_size
									snake_case_									=				torch.tensor([480, 640]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,				lowercase_					)					)
									# verify size
									snake_case_									=				torch.tensor([800, 1066]					)
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,				lowercase_					)					)







'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				list(s_dict.keys()	)
					for key in keys:
										if "transformer_layers" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)
										elif "subsample" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      max_source_positions=args.max_source_positions,      max_target_positions=args.max_target_positions,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''relu''',      num_conv_layers=len(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    F" but all the following weights are missing {missing}"	)

					if tie_embeds:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

