

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 ChineseCLIPImageProcessor



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=3    ,       A__=18    ,       A__=30    ,       A__=400    ,       A__=True    ,       A__=None    ,       A__=True    ,       A__=None    ,       A__=True    ,       A__=[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]    ,       A__=[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]    ,       A__=True    ,       ):
						A__							:    Dict        =		size if size is not None else {"""height""": 224, """width""": 224}
						A__							:    Union[str, Any]        =		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
						A__							:    str        =		parent
						A__							:    str        =		batch_size
						A__							:    Optional[int]        =		num_channels
						A__							:    Dict        =		image_size
						A__							:    Dict        =		min_resolution
						A__							:    Optional[Any]        =		max_resolution
						A__							:    Optional[int]        =		do_resize
						A__							:    Optional[Any]        =		size
						A__							:    Dict        =		do_center_crop
						A__							:    int        =		crop_size
						A__							:    Union[str, Any]        =		do_normalize
						A__							:    List[str]        =		image_mean
						A__							:    Tuple        =		image_std
						A__							:    Optional[Any]        =		do_convert_rgb






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






					def   __A			( self    ,       A__=False    ,       A__=False    ,       A__=False			):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							A__							:    List[Any]        =		[]
							for i in range(self.batch_size			):
								image_inputs.append(
								    np.random.randint(
								        255    ,       size=(self.num_channels, self.max_resolution, self.max_resolution)    ,       dtype=np.uinta			)			)
						else:
							A__							:    List[Any]        =		[]
							for i in range(self.batch_size			):
								A__   ,   A__							:    List[str]        =		np.random.choice(np.arange(self.min_resolution    ,       self.max_resolution			)    ,       2			)
								image_inputs.append(np.random.randint(255    ,       size=(self.num_channels, width, height)    ,       dtype=np.uinta			)			)

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

						if torchify:
							A__							:    Dict        =		[torch.from_numpy(A__			) for x in image_inputs]

						return image_inputs



@require_torch
@require_vision
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Tuple  							=       ChineseCLIPImageProcessor if is_vision_available() else None






					def   __A			( self			):
						A__							:    Any        =		ChineseCLIPImageProcessingTester(self    ,       do_center_crop=A__			)






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






					def   __A			( self			):
						A__							:    Optional[int]        =		self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(A__    ,       """do_resize"""			)			)
						self.assertTrue(hasattr(A__    ,       """size"""			)			)
						self.assertTrue(hasattr(A__    ,       """do_center_crop"""			)			)
						self.assertTrue(hasattr(A__    ,       """center_crop"""			)			)
						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   __A			( self			):
						A__							:    Optional[int]        =		self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size    ,       {"""height""": 224, """width""": 224}			)
						self.assertEqual(image_processor.crop_size    ,       {"""height""": 18, """width""": 18}			)

						A__							:    int        =		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   __A			( self			):
						pass






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

						# Test not batched input
						A__							:    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
						A__							:    Optional[Any]        =		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,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Any        =		self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						A__							:    List[str]        =		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__							:    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
						A__							:    int        =		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,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    int        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						A__							:    Union[str, Any]        =		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__							:    Optional[int]        =		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
						A__							:    List[Any]        =		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,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )



@require_torch
@require_vision
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Any  							=       ChineseCLIPImageProcessor if is_vision_available() else None






					def   __A			( self			):
						A__							:    Union[str, Any]        =		ChineseCLIPImageProcessingTester(self    ,       num_channels=4    ,       do_center_crop=A__			)
						A__							:    List[Any]        =		3






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






					def   __A			( self			):
						A__							:    int        =		self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(A__    ,       """do_resize"""			)			)
						self.assertTrue(hasattr(A__    ,       """size"""			)			)
						self.assertTrue(hasattr(A__    ,       """do_center_crop"""			)			)
						self.assertTrue(hasattr(A__    ,       """center_crop"""			)			)
						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   __A			( self			):
						pass






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Optional[Any]        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						A__							:    Any        =		self.image_processor_tester.prepare_inputs(equal_resolution=A__			)
						for image in image_inputs:
							self.assertIsInstance(A__    ,       Image.Image			)

						# Test not batched input
						A__							:    List[Any]        =		image_processing(image_inputs[0]    ,       return_tensors="""pt"""			).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,       (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )

						# Test batched
						A__							:    Optional[Any]        =		image_processing(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.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )    ,       )






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



