
'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 LevitImageProcessor

class 						a   (				unittest.TestCase    ):




				def __init__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=7,    SCREAMING_SNAKE_CASE_	:				Tuple=3,    SCREAMING_SNAKE_CASE_	:				List[str]=18,    SCREAMING_SNAKE_CASE_	:				List[Any]=30,    SCREAMING_SNAKE_CASE_	:				str=4_00,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=True,    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				Dict=True,    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    SCREAMING_SNAKE_CASE_	:				List[Any]=True,    SCREAMING_SNAKE_CASE_	:				Any=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=[0.5, 0.5, 0.5],    ):
										snake_case       :     int =    size if size is not None else {'''shortest_edge''': 18}
										snake_case       :     Tuple =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										snake_case       :     Union[str, Any] =    parent
										snake_case       :     List[str] =    batch_size
										snake_case       :     Optional[Any] =    num_channels
										snake_case       :     Any =    image_size
										snake_case       :     Union[str, Any] =    min_resolution
										snake_case       :     Optional[Any] =    max_resolution
										snake_case       :     Any =    do_resize
										snake_case       :     Union[str, Any] =    size
										snake_case       :     Optional[int] =    do_center_crop
										snake_case       :     Tuple =    crop_size
										snake_case       :     List[Any] =    do_normalize
										snake_case       :     Dict =    image_mean
										snake_case       :     str =    image_std




				def 	__snake_case							(							self	:				str      ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "do_center_crop": self.do_center_crop,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class 						a   (				a__				,unittest.TestCase    ):
				_snake_case		    = LevitImageProcessor if is_vision_available() else None




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     int =    LevitImageProcessingTester(self      )




				@property
				def 	__snake_case							(							self	:				Tuple      ):
										return self.image_processor_tester.prepare_image_processor_dict()




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Tuple =    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_resize'''      )      )
										self.assertTrue(hasattr(lowercase__,    '''do_center_crop'''      )      )
										self.assertTrue(hasattr(lowercase__,    '''size'''      )      )




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     str =    self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size,    {'''shortest_edge''': 18}      )
										self.assertEqual(image_processor.crop_size,    {'''height''': 18, '''width''': 18}      )

										snake_case       :     Tuple =    self.image_processing_class.from_dict(self.image_processor_dict,    size=42,    crop_size=84      )
										self.assertEqual(image_processor.size,    {'''shortest_edge''': 42}      )
										self.assertEqual(image_processor.crop_size,    {'''height''': 84, '''width''': 84}      )




				def 	__snake_case							(							self	:				str      ):
										pass




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Dict =    self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										snake_case       :     Optional[Any] =    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       :     List[str] =    image_processing(image_inputs[0],    return_tensors='''pt'''      ).pixel_values
										self.assertEqual(
										    encoded_images.shape,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )

										# Test batched
										snake_case       :     Any =    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,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										snake_case       :     List[str] =    self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										snake_case       :     Optional[int] =    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       :     Union[str, Any] =    image_processing(image_inputs[0],    return_tensors='''pt'''      ).pixel_values
										self.assertEqual(
										    encoded_images.shape,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )

										# Test batched
										snake_case       :     List[str] =    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,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )




				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Dict =    self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										snake_case       :     str =    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       :     Dict =    image_processing(image_inputs[0],    return_tensors='''pt'''      ).pixel_values
										self.assertEqual(
										    encoded_images.shape,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )

										# Test batched
										snake_case       :     Dict =    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,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ),    )








'''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


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

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''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       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

												snake_case       :     Optional[Any] =    False
												for attribute in attributes:
																		if attribute in mapping:
																								snake_case       :     List[str] =    mapping[attribute]
																								if not hasattr(A_    ,							A_       ) and len(A_       ) > 0:
																														snake_case       :     str =    attribute
																		elif hasattr(A_    ,							A_       ):
																								snake_case       :     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       :     Tuple =    True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								snake_case       :     List[str] =    old_model.bias
																								logger.info(F"""{attribute} is initialized"""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute in special_keys and hasattr(A_    ,							'''in_proj_weight'''       ):
																								snake_case       :     Union[str, Any] =    old_model.in_proj_weight.shape[0] // 3
																								snake_case       :     Any =    getattr(A_    ,							A_       )
																								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       :     Tuple =    nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																														snake_case       :     List[Any] =    nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																								elif attribute == "key_proj":
																														snake_case       :     str =    nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																														snake_case       :     Any =    nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																								elif attribute == "value_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																														snake_case       :     List[str] =    nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																								snake_case       :     Optional[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] == 512, "We want 512 position_embeddings."
																								snake_case       :     List[Any] =    nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																								snake_case       :     Any =    True
																								break

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

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    getattr(A_    ,							A_       )

												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(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   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."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

