





"""simple docstring"""






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 1 / 255	,			_UpperCAmelCase = True	,			_UpperCAmelCase = True	,			_UpperCAmelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,			_UpperCAmelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,			_UpperCAmelCase = True	,			_UpperCAmelCase=7	,			_UpperCAmelCase=30	,			_UpperCAmelCase=400	,			_UpperCAmelCase=3	,			):
									__a :						str			       =  parent
									__a :						Optional[Any]			       =  do_resize
									__a :						Tuple			       =  size if size is not None else {'''shortest_edge''': 288}
									__a :						Dict			       =  size_divisor
									__a :						Optional[int]			       =  do_rescale
									__a :						List[str]			       =  rescale_factor
									__a :						str			       =  do_normalize
									__a :						Dict			       =  do_center_crop
									__a :						Any			       =  image_mean
									__a :						str			       =  image_std
									__a :						Optional[int]			       =  do_pad
									__a :						str			       =  batch_size
									__a :						Dict			       =  num_channels
									__a :						Optional[int]			       =  min_resolution
									__a :						Tuple			       =  max_resolution







					def  _lowerCamelCase		(						self     ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									if not batched:
													__a :						str			       =  self.size['''shortest_edge''']
													__a :						List[str]			       =  image_inputs[0]
													if isinstance(_UpperCAmelCase	,			Image.Image     ):
																	__a       ,						__a :						Union[str, Any]			       =  image.size
													else:
																	__a       ,						__a :						Any			       =  image.shape[1], image.shape[2]
													__a :						Dict			       =  size / min(_UpperCAmelCase	,			_UpperCAmelCase     )
													if h < w:
																	__a       ,						__a :						Union[str, Any]			       =  size, scale * w
													else:
																	__a       ,						__a :						int			       =  scale * h, size

													__a :						Union[str, Any]			       =  int((1333 / 800) * size     )
													if max(_UpperCAmelCase	,			_UpperCAmelCase     ) > max_size:
																	__a :						List[Any]			       =  max_size / max(_UpperCAmelCase	,			_UpperCAmelCase     )
																	__a :						Any			       =  newh * scale
																	__a :						Tuple			       =  neww * scale

													__a       ,						__a :						int			       =  int(newh + 0.5     ), int(neww + 0.5     )
													__a       ,						__a :						Dict			       =  (
													    newh // self.size_divisor * self.size_divisor,
													    neww // self.size_divisor * self.size_divisor,
													)

									else:
													__a :						List[Any]			       =  []
													for image in image_inputs:
																	__a       ,						__a :						str			       =  self.get_expected_values([image]     )
																	expected_values.append((expected_height, expected_width)     )
													__a :						Dict			       =  max(_UpperCAmelCase	,			key=lambda _UpperCAmelCase     : item[0]     )[0]
													__a :						Optional[int]			       =  max(_UpperCAmelCase	,			key=lambda _UpperCAmelCase     : item[1]     )[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  BridgeTowerImageProcessor if is_vision_available() else None







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  BridgeTowerImageProcessingTester(self     )







					@property
					def  _lowerCamelCase		(						self     ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  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_resize'''     )     )
									self.assertTrue(hasattr(_UpperCAmelCase	,			'''size'''     )     )
									self.assertTrue(hasattr(_UpperCAmelCase	,			'''size_divisor'''     )     )







					def  _lowerCamelCase		(						self     ):
									pass







					def  _lowerCamelCase		(						self     ):
									# Initialize image processor
									__a :						Union[str, Any]			       =  self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__a :						int			       =  prepare_image_inputs(self.image_processor_tester	,			equal_resolution=_UpperCAmelCase     )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase	,			Image.Image     )

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

									__a       ,						__a :						List[str]			       =  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
									__a :						Any			       =  image_processing(_UpperCAmelCase	,			return_tensors='''pt'''     ).pixel_values

									__a       ,						__a :						Tuple			       =  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  _lowerCamelCase		(						self     ):
									# Initialize image processor
									__a :						Any			       =  self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__a :						Optional[int]			       =  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
									__a :						Optional[Any]			       =  image_processing(image_inputs[0]	,			return_tensors='''pt'''     ).pixel_values

									__a       ,						__a :						Any			       =  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
									__a :						Union[str, Any]			       =  image_processing(_UpperCAmelCase	,			return_tensors='''pt'''     ).pixel_values

									__a       ,						__a :						List[str]			       =  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  _lowerCamelCase		(						self     ):
									# Initialize image processor
									__a :						Any			       =  self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__a :						Any			       =  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
									__a :						Union[str, Any]			       =  image_processing(image_inputs[0]	,			return_tensors='''pt'''     ).pixel_values

									__a       ,						__a :						Tuple			       =  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
									__a :						Optional[Any]			       =  image_processing(_UpperCAmelCase	,			return_tensors='''pt'''     ).pixel_values

									__a       ,						__a :						Optional[int]			       =  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,
									    )	,			)






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

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						List[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"]:
								__a :						List[str]			       =  key.split('''.''')

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

								__a :						Optional[int]			       =  False
								for attribute in attributes:
												if attribute in mapping:
																__a :						str			       =  mapping[attribute]
																if not hasattr(a_   ,       a_) and len(a_) > 0:
																				__a :						List[str]			       =  attribute
												elif hasattr(a_   ,       a_):
																__a :						int			       =  attribute

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

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  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."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  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__":
		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)