





"""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        _lowerCamelCase      (       unittest.TestCase ):


						def __init__(self   ,							__a   ,							__a=7   ,							__a=3   ,							__a=30   ,							__a=4_00   ,							__a=True   ,							__a=None   ,							__a=True   ,							__a=1 / 2_55   ,							__a=True   ,							__a=[0.5, 0.5, 0.5]   ,							__a=[0.5, 0.5, 0.5]   ,							__a=True   ,							) ->   Tuple:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								UpperCamelCase   =	size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	min_resolution
								UpperCamelCase   =	max_resolution
								UpperCamelCase   =	do_resize
								UpperCamelCase   =	size
								UpperCamelCase   =	do_rescale
								UpperCamelCase   =	rescale_factor
								UpperCamelCase   =	do_normalize
								UpperCamelCase   =	image_mean
								UpperCamelCase   =	image_std
								UpperCamelCase   =	do_pad


						def     snake_case_   (self		) ->   Any:
								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     snake_case_   (self   ,							__a   ,							__a=False		) ->   Union[str, Any]:
								if not batched:
										UpperCamelCase   =	image_inputs[0]
										if isinstance(__a   ,							Image.Image		):
												UpperCamelCase   ,				UpperCamelCase   =	image.size
										else:
												UpperCamelCase   ,				UpperCamelCase   =	image.shape[1], image.shape[2]
										if w < h:
												UpperCamelCase   =	int(self.size["shortest_edge"] * h / w		)
												UpperCamelCase   =	self.size["shortest_edge"]
										elif w > h:
												UpperCamelCase   =	self.size["shortest_edge"]
												UpperCamelCase   =	int(self.size["shortest_edge"] * w / h		)
										else:
												UpperCamelCase   =	self.size["shortest_edge"]
												UpperCamelCase   =	self.size["shortest_edge"]

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

								return expected_height, expected_width





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


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	DetrImageProcessingTester(self		)


						@property
						def     snake_case_   (self		) ->   List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	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_rescale"		)		)
								self.assertTrue(hasattr(__a   ,							"rescale_factor"		)		)
								self.assertTrue(hasattr(__a   ,							"do_resize"		)		)
								self.assertTrue(hasattr(__a   ,							"size"		)		)
								self.assertTrue(hasattr(__a   ,							"do_pad"		)		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size   ,							{"shortest_edge": 18, "longest_edge": 13_33}		)
								self.assertEqual(image_processor.do_pad   ,							__a		)

								UpperCamelCase   =	self.image_processing_class.from_dict(
								    self.image_processor_dict   ,							size=42   ,							max_size=84   ,							pad_and_return_pixel_mask=__a		)
								self.assertEqual(image_processor.size   ,							{"shortest_edge": 42, "longest_edge": 84}		)
								self.assertEqual(image_processor.do_pad   ,							__a		)


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


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

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

								UpperCamelCase   ,				UpperCamelCase   =	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
								UpperCamelCase   ,				UpperCamelCase   =	self.image_processor_tester.get_expected_values(__a   ,							batched=__a		)

								UpperCamelCase   =	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     snake_case_   (self		) ->   Union[str, Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								UpperCamelCase   =	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
								UpperCamelCase   =	image_processing(image_inputs[0]   ,							return_tensors="pt"		).pixel_values

								UpperCamelCase   ,				UpperCamelCase   =	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
								UpperCamelCase   =	image_processing(__a   ,							return_tensors="pt"		).pixel_values

								UpperCamelCase   ,				UpperCamelCase   =	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     snake_case_   (self		) ->   Optional[Any]:
								# Initialize image_processing
								UpperCamelCase   =	self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCamelCase   =	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
								UpperCamelCase   =	image_processing(image_inputs[0]   ,							return_tensors="pt"		).pixel_values

								UpperCamelCase   ,				UpperCamelCase   =	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
								UpperCamelCase   =	image_processing(__a   ,							return_tensors="pt"		).pixel_values

								UpperCamelCase   ,				UpperCamelCase   =	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     snake_case_   (self		) ->   Tuple:
								# prepare image and target
								UpperCamelCase   =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,							"r"		) as f:
										UpperCamelCase   =	json.loads(f.read()		)

								UpperCamelCase   =	{"image_id": 3_97_69, "annotations": target}

								# encode them
								UpperCamelCase   =	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"		)
								UpperCamelCase   =	image_processing(images=__a   ,							annotations=__a   ,							return_tensors="pt"		)

								# verify pixel values
								UpperCamelCase   =	torch.Size([1, 3, 8_00, 10_66]		)
								self.assertEqual(encoding["pixel_values"].shape   ,							__a		)

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

								# verify area
								UpperCamelCase   =	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,							__a		)		)
								# verify boxes
								UpperCamelCase   =	torch.Size([6, 4]		)
								self.assertEqual(encoding["labels"][0]["boxes"].shape   ,							__a		)
								UpperCamelCase   =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,							__a   ,							atol=1e-3		)		)
								# verify image_id
								UpperCamelCase   =	torch.tensor([3_97_69]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,							__a		)		)
								# verify is_crowd
								UpperCamelCase   =	torch.tensor([0, 0, 0, 0, 0, 0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,							__a		)		)
								# verify class_labels
								UpperCamelCase   =	torch.tensor([75, 75, 63, 65, 17, 17]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,							__a		)		)
								# verify orig_size
								UpperCamelCase   =	torch.tensor([4_80, 6_40]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,							__a		)		)
								# verify size
								UpperCamelCase   =	torch.tensor([8_00, 10_66]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,							__a		)		)




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

								UpperCamelCase   =	{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

								UpperCamelCase   =	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

								# encode them
								UpperCamelCase   =	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"		)
								UpperCamelCase   =	image_processing(images=__a   ,							annotations=__a   ,							masks_path=__a   ,							return_tensors="pt"		)

								# verify pixel values
								UpperCamelCase   =	torch.Size([1, 3, 8_00, 10_66]		)
								self.assertEqual(encoding["pixel_values"].shape   ,							__a		)

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

								# verify area
								UpperCamelCase   =	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,							__a		)		)
								# verify boxes
								UpperCamelCase   =	torch.Size([6, 4]		)
								self.assertEqual(encoding["labels"][0]["boxes"].shape   ,							__a		)
								UpperCamelCase   =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,							__a   ,							atol=1e-3		)		)
								# verify image_id
								UpperCamelCase   =	torch.tensor([3_97_69]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,							__a		)		)
								# verify is_crowd
								UpperCamelCase   =	torch.tensor([0, 0, 0, 0, 0, 0]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,							__a		)		)
								# verify class_labels
								UpperCamelCase   =	torch.tensor([17, 17, 63, 75, 75, 93]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,							__a		)		)
								# verify masks
								UpperCamelCase   =	82_28_73
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,							__a		)
								# verify orig_size
								UpperCamelCase   =	torch.tensor([4_80, 6_40]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,							__a		)		)
								# verify size
								UpperCamelCase   =	torch.tensor([8_00, 10_66]		)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,							__a		)		)













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	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  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="relu"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






