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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BlipImageProcessor





class 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				Optional[Any]							,A      :				int=7							,A      :				Optional[Any]=3							,A      :				Optional[int]=18							,A      :				str=30							,A      :				int=4_00							,A      :				List[str]=True							,A      :				str=None							,A      :				str=True							,A      :				str=False							,A      :				Dict=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,A      :				Dict=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,A      :				List[str]=True							,):
					__A         =				size if size is not None else {"height": 20, "width": 20}
					__A         =				parent
					__A         =				batch_size
					__A         =				num_channels
					__A         =				image_size
					__A         =				min_resolution
					__A         =				max_resolution
					__A         =				do_resize
					__A         =				size
					__A         =				do_normalize
					__A         =				image_mean
					__A         =				image_std
					__A         =				do_pad
					__A         =				do_convert_rgb






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					    "do_pad": self.do_pad,
					}







	def        UpperCamelCase_						( self      :				Any							,A      :				Dict=False							,A      :				List[Any]=False							,A      :				Union[str, Any]=False				):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
									__A         =				[]
									for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55							,size=(self.num_channels, self.max_resolution, self.max_resolution)							,dtype=np.uinta				)				)
					else:
									__A         =				[]
									for i in range(self.batch_size				):
													__A         =				np.random.choice(np.arange(self.min_resolution							,self.max_resolution				)							,2				)
													image_inputs.append(np.random.randint(2_55							,size=(self.num_channels, width, height)							,dtype=np.uinta				)				)

					if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									__A         =				[Image.fromarray(np.moveaxis(A							,0							,-1				)				) for x in image_inputs]

					if torchify:
									__A         =				[torch.from_numpy(A				) for x in image_inputs]

					return image_inputs





@require_torch
@require_vision
class 							UpperCAmelCase   (						lowerCamelCase_    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  BlipImageProcessor if is_vision_available() else None






	def        UpperCamelCase_						( self      :				int				):
					__A         =				BlipImageProcessingTester(self				)






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






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(A							,"do_resize"				)				)
					self.assertTrue(hasattr(A							,"size"				)				)
					self.assertTrue(hasattr(A							,"do_normalize"				)				)
					self.assertTrue(hasattr(A							,"image_mean"				)				)
					self.assertTrue(hasattr(A							,"image_std"				)				)
					self.assertTrue(hasattr(A							,"do_convert_rgb"				)				)






	def        UpperCamelCase_						( self      :				Tuple				):
					pass






	def        UpperCamelCase_						( self      :				List[str]				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					__A         =				self.image_processor_tester.prepare_inputs(equal_resolution=A				)
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image				)

					# Test not batched input
					__A         =				image_processor(image_inputs[0]							,return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)

					# Test batched
					__A         =				image_processor(A							,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.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)






	def        UpperCamelCase_						( self      :				List[str]				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					# create random numpy tensors
					__A         =				self.image_processor_tester.prepare_inputs(equal_resolution=A							,numpify=A				)
					for image in image_inputs:
									self.assertIsInstance(A							,np.ndarray				)

					# Test not batched input
					__A         =				image_processor(image_inputs[0]							,return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)

					# Test batched
					__A         =				image_processor(A							,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.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)







	def        UpperCamelCase_						( self      :				Any				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					__A         =				self.image_processor_tester.prepare_inputs(equal_resolution=A							,torchify=A				)
					for image in image_inputs:
									self.assertIsInstance(A							,torch.Tensor				)

					# Test not batched input
					__A         =				image_processor(image_inputs[0]							,return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)

					# Test batched
					__A         =				image_processor(A							,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.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)





@require_torch
@require_vision
class 							UpperCAmelCase   (						lowerCamelCase_    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  BlipImageProcessor if is_vision_available() else None






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					__A         =				BlipImageProcessingTester(self							,num_channels=4				)
					__A         =				3






	@property
	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.image_processor_tester.prepare_image_processor_dict()






	def        UpperCamelCase_						( self      :				List[str]				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(A							,"do_resize"				)				)
					self.assertTrue(hasattr(A							,"size"				)				)
					self.assertTrue(hasattr(A							,"do_normalize"				)				)
					self.assertTrue(hasattr(A							,"image_mean"				)				)
					self.assertTrue(hasattr(A							,"image_std"				)				)
					self.assertTrue(hasattr(A							,"do_convert_rgb"				)				)






	def        UpperCamelCase_						( self      :				Dict				):
					pass







	def        UpperCamelCase_						( self      :				str				):
					__A         =				self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					__A         =				self.image_processor_tester.prepare_inputs(equal_resolution=A				)
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image				)

					# Test not batched input
					__A         =				image_processor(image_inputs[0]							,return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)

					# Test batched
					__A         =				image_processor(A							,return_tensors="pt"				).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )							,)


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
