



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


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

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

										return expected_height, expected_width






@require_torch
@require_vision
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				DetrImageProcessor if is_vision_available() else None


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Any:
										snake_case:    List[str]      							=DetrImageProcessingTester(self			)


							@property
							def    UpperCamelCase     (				self   :						str			)			->					Dict:
										return self.image_processor_tester.prepare_image_processor_dict()


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										snake_case:    Tuple      							=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    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Any:
										snake_case:    Any      							=self.image_processing_class.from_dict(self.image_processor_dict			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)

										snake_case:    Optional[int]      							=self.image_processing_class.from_dict(
										    self.image_processor_dict      ,						size=4_2      ,						max_size=8_4      ,						pad_and_return_pixel_mask=a_			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 4_2, 'longest_edge': 8_4}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										pass


							def    UpperCamelCase     (				self   :						List[str]			)			->					int:
										# Initialize image_processing
										snake_case:    List[str]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										snake_case:    Tuple      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						Image.Image			)

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

										snake_case							,  snake_case:    Optional[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
										snake_case							,  snake_case:    str      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)

										snake_case:    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   :						str			)			->					List[str]:
										# Initialize image_processing
										snake_case:    Optional[Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case:    List[Any]      							=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
										snake_case:    int      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Tuple      							=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
										snake_case:    Union[str, Any]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

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

										snake_case							,  snake_case:    Optional[int]      							=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
										snake_case:    List[Any]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    int      							=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   :						Optional[Any]			)			->					int:
										# prepare image and target
										snake_case:    List[str]      							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
										with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,						'r'			) as f:
													snake_case:    Union[str, Any]      							=json.loads(f.read()			)

										snake_case:    Dict      							={'image_id': 3_9_7_6_9, 'annotations': target}

										# encode them
										snake_case:    Optional[int]      							=DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'			)
										snake_case:    List[str]      							=image_processing(images=a_      ,						annotations=a_      ,						return_tensors='pt'			)

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

										snake_case:    List[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
										snake_case:    List[str]      							=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
										snake_case:    Optional[int]      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    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
										snake_case:    Tuple      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    Any      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    List[Any]      							=torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify orig_size
										snake_case:    Any      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    int      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)




							@slow
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Union[str, Any]:
										# prepare image, target and masks_path
										snake_case:    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:
													snake_case:    str      							=json.loads(f.read()			)

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

										snake_case:    str      							=pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

										# encode them
										snake_case:    List[Any]      							=DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'			)
										snake_case:    str      							=image_processing(images=a_      ,						annotations=a_      ,						masks_path=a_      ,						return_tensors='pt'			)

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

										snake_case:    Any      							=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
										snake_case:    Any      							=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
										snake_case:    List[Any]      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    Optional[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
										snake_case:    List[Any]      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    str      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    Tuple      							=torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify masks
										snake_case:    List[str]      							=8_2_2_8_7_3
										self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,						a_			)
										# verify orig_size
										snake_case:    List[Any]      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    List[Any]      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)









'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




