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 ViltImageProcessor






class 		a__						( unittest.TestCase ):

					def __init__(   self				:	int,_A				:	List[str],_A				:	int=7,_A				:	Any=3,_A				:	Dict=18,_A				:	List[Any]=30,_A				:	List[Any]=400,_A				:	List[str]=True,_A				:	Dict=None,_A				:	int=2,_A				:	Dict=True,_A				:	int=[0.5, 0.5, 0.5],_A				:	Tuple=[0.5, 0.5, 0.5],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   size if size is not None else {"shortest_edge": 30}
							SCREAMING_SNAKE_CASE_  :		int    				=   parent
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   batch_size
							SCREAMING_SNAKE_CASE_  :		str    				=   num_channels
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   image_size
							SCREAMING_SNAKE_CASE_  :		int    				=   min_resolution
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   max_resolution
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   do_resize
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   size
							SCREAMING_SNAKE_CASE_  :		str    				=   size_divisor
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   do_normalize
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   image_mean
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image_std

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							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     __UpperCamelCase       (   self				:	List[Any],_A				:	Dict,_A				:	Dict=False   ):







							"""simple docstring"""






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

									SCREAMING_SNAKE_CASE_  :		List[str]    				=   int((1333 / 800) * size   )
									if max(_A,_A   ) > max_size:
											SCREAMING_SNAKE_CASE_  :		Any    				=   max_size / max(_A,_A   )
											SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   newh * scale
											SCREAMING_SNAKE_CASE_  :		str    				=   neww * scale

									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   int(newh + 0.5   ), int(neww + 0.5   )
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		str    				=   (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

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

							return expected_height, expected_width







@require_torch
@require_vision
class 		a__						( A__   ,		unittest.TestCase ):
					A     	=							ViltImageProcessor if is_vision_available() else None

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   ViltImageProcessingTester(self   )

					@property
					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   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_resize"   )   )
							self.assertTrue(hasattr(_A,"size"   )   )
							self.assertTrue(hasattr(_A,"size_divisor"   )   )

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size,{"shortest_edge": 30}   )

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.image_processing_class.from_dict(self.image_processor_dict,size=42   )
							self.assertEqual(image_processor.size,{"shortest_edge": 42}   )

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							pass

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   prepare_image_inputs(self.image_processor_tester,equal_resolution=_A   )
							for image in image_inputs:
									self.assertIsInstance(_A,Image.Image   )

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

							SCREAMING_SNAKE_CASE_				,   SCREAMING_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
							SCREAMING_SNAKE_CASE_  :		str    				=   image_processing(_A,return_tensors="pt"   ).pixel_values

							SCREAMING_SNAKE_CASE_				,   SCREAMING_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]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							SCREAMING_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
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   image_processing(image_inputs[0],return_tensors="pt"   ).pixel_values

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[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
							SCREAMING_SNAKE_CASE_  :		Any    				=   image_processing(_A,return_tensors="pt"   ).pixel_values

							SCREAMING_SNAKE_CASE_				,   SCREAMING_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,
							    ),)






					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_  :		Dict    				=   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
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   image_processing(image_inputs[0],return_tensors="pt"   ).pixel_values

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		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
							SCREAMING_SNAKE_CASE_  :		int    				=   image_processing(_A,return_tensors="pt"   ).pixel_values

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[str]    				=   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,
							    ),)

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
