

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=3    ,       A__=30    ,       A__=400    ,       A__=True    ,       A__=None    ,       A__=True    ,       A__=[0.5, 0.5, 0.5]    ,       A__=[0.5, 0.5, 0.5]    ,       A__=True    ,       A__=1 / 255    ,       A__=True    ,       ):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						A__							:    str        =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
						A__							:    List[Any]        =		parent
						A__							:    List[Any]        =		batch_size
						A__							:    Union[str, Any]        =		num_channels
						A__							:    List[Any]        =		min_resolution
						A__							:    Union[str, Any]        =		max_resolution
						A__							:    Any        =		do_resize
						A__							:    int        =		size
						A__							:    Union[str, Any]        =		do_normalize
						A__							:    Dict        =		image_mean
						A__							:    Optional[Any]        =		image_std
						A__							:    Optional[int]        =		do_rescale
						A__							:    Optional[int]        =		rescale_factor
						A__							:    Any        =		do_pad






					def   __A			( self			):
						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_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def   __A			( self    ,       A__    ,       A__=False			):
						if not batched:
							A__							:    int        =		image_inputs[0]
							if isinstance(A__    ,       Image.Image			):
								A__   ,   A__							:    List[str]        =		image.size
							else:
								A__   ,   A__							:    List[Any]        =		image.shape[1], image.shape[2]
							if w < h:
								A__							:    Dict        =		int(self.size["""shortest_edge"""] * h / w			)
								A__							:    List[Any]        =		self.size["""shortest_edge"""]
							elif w > h:
								A__							:    int        =		self.size["""shortest_edge"""]
								A__							:    Any        =		int(self.size["""shortest_edge"""] * w / h			)
							else:
								A__							:    Any        =		self.size["""shortest_edge"""]
								A__							:    Dict        =		self.size["""shortest_edge"""]

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

						return expected_height, expected_width



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


					'''simple docstring'''






					UpperCAmelCase__:   int  							=       ConditionalDetrImageProcessor if is_vision_available() else None






					def   __A			( self			):
						A__							:    Union[str, Any]        =		ConditionalDetrImageProcessingTester(self			)






					@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__    ,       """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"""			)			)






					def   __A			( self			):
						A__							:    int        =		self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 18, """longest_edge""": 1333}			)
						self.assertEqual(image_processor.do_pad    ,       A__			)

						A__							:    Optional[Any]        =		self.image_processing_class.from_dict(
						    self.image_processor_dict    ,       size=42    ,       max_size=84    ,       pad_and_return_pixel_mask=A__			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42, """longest_edge""": 84}			)
						self.assertEqual(image_processor.do_pad    ,       A__			)






					def   __A			( self			):
						pass






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

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

						A__   ,   A__							:    List[str]        =		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
						A__   ,   A__							:    Dict        =		self.image_processor_tester.get_expected_values(A__    ,       batched=A__			)

						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,
						        expected_height,
						        expected_width,
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    int        =		self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						A__							:    List[str]        =		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
						A__							:    Any        =		image_processing(image_inputs[0]    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    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
						A__							:    Dict        =		image_processing(A__    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    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,
						    )    ,       )






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Tuple        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						A__							:    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
						A__							:    List[Any]        =		image_processing(image_inputs[0]    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    str        =		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
						A__							:    Union[str, Any]        =		image_processing(A__    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    List[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,
						    )    ,       )






					@slow
					def   __A			( self			):
						# prepare image and target
						A__							:    Union[str, Any]        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,       """r"""			) as f:
							A__							:    Optional[Any]        =		json.loads(f.read()			)

						A__							:    Dict        =		{"""image_id""": 3_9769, """annotations""": target}

						# encode them
						A__							:    Optional[Any]        =		ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""			)
						A__							:    Optional[Any]        =		image_processing(images=A__    ,       annotations=A__    ,       return_tensors="""pt"""			)

						# verify pixel values
						A__							:    Dict        =		torch.Size([1, 3, 800, 1066]			)
						self.assertEqual(encoding["""pixel_values"""].shape    ,       A__			)

						A__							:    List[str]        =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       A__    ,       atol=1e-4			)			)

						# verify area
						A__							:    List[str]        =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,       A__			)			)
						# verify boxes
						A__							:    Tuple        =		torch.Size([6, 4]			)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       A__			)
						A__							:    str        =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       A__    ,       atol=1e-3			)			)
						# verify image_id
						A__							:    List[str]        =		torch.tensor([3_9769]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       A__			)			)
						# verify is_crowd
						A__							:    str        =		torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       A__			)			)
						# verify class_labels
						A__							:    Union[str, Any]        =		torch.tensor([75, 75, 63, 65, 17, 17]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       A__			)			)
						# verify orig_size
						A__							:    str        =		torch.tensor([480, 640]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       A__			)			)
						# verify size
						A__							:    Any        =		torch.tensor([800, 1066]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       A__			)			)






					@slow
					def   __A			( self			):
						# prepare image, target and masks_path
						A__							:    str        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,       """r"""			) as f:
							A__							:    Union[str, Any]        =		json.loads(f.read()			)

						A__							:    Union[str, Any]        =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

						A__							:    str        =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""			)

						# encode them
						A__							:    Any        =		ConditionalDetrImageProcessor(format="""coco_panoptic"""			)
						A__							:    str        =		image_processing(images=A__    ,       annotations=A__    ,       masks_path=A__    ,       return_tensors="""pt"""			)

						# verify pixel values
						A__							:    Union[str, Any]        =		torch.Size([1, 3, 800, 1066]			)
						self.assertEqual(encoding["""pixel_values"""].shape    ,       A__			)

						A__							:    Any        =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       A__    ,       atol=1e-4			)			)

						# verify area
						A__							:    List[Any]        =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,       A__			)			)
						# verify boxes
						A__							:    Optional[int]        =		torch.Size([6, 4]			)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       A__			)
						A__							:    str        =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       A__    ,       atol=1e-3			)			)
						# verify image_id
						A__							:    Tuple        =		torch.tensor([3_9769]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       A__			)			)
						# verify is_crowd
						A__							:    Optional[Any]        =		torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       A__			)			)
						# verify class_labels
						A__							:    Dict        =		torch.tensor([17, 17, 63, 75, 75, 93]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       A__			)			)
						# verify masks
						A__							:    Optional[Any]        =		82_2873
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,       A__			)
						# verify orig_size
						A__							:    str        =		torch.tensor([480, 640]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       A__			)			)
						# verify size
						A__							:    List[Any]        =		torch.tensor([800, 1066]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       A__			)			)






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



