



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 OwlViTImageProcessor






class 	_a			(      unittest.TestCase  ):






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






			def 							_snake_case   (    self )	->					str:
									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,
									}






@require_torch
@require_vision
class 	_a			(      snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   Tuple	     =		OwlViTImageProcessor if is_vision_available() else None






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   Any					     =					OwlViTImageProcessingTester(self )






			@property
			def 							_snake_case   (    self )	->					List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()






			def 							_snake_case   (    self )	->					Any:
									lowerCAmelCase	:   List[str]					     =					self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(lowercase_    ,  """do_resize""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """size""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_center_crop""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """center_crop""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_normalize""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """image_mean""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """image_std""" ) )
									self.assertTrue(hasattr(lowercase_    ,  """do_convert_rgb""" ) )






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Any					     =					self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size    ,  {"""height""": 18, """width""": 18} )
									self.assertEqual(image_processor.crop_size    ,  {"""height""": 18, """width""": 18} )

									lowerCAmelCase	:   Union[str, Any]					     =					self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42    ,  crop_size=84 )
									self.assertEqual(image_processor.size    ,  {"""height""": 42, """width""": 42} )
									self.assertEqual(image_processor.crop_size    ,  {"""height""": 84, """width""": 84} )






			def 							_snake_case   (    self )	->					str:
									# Initialize image_processing
									lowerCAmelCase	:   List[str]					     =					self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									lowerCAmelCase	:   Any					     =					prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_ )

									for image in image_inputs:
															self.assertIsInstance(lowercase_    ,  Image.Image )

									# Test not batched input
									lowerCAmelCase	:   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
									lowerCAmelCase	:   Tuple					     =					image_processing(lowercase_    ,  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 							_snake_case   (    self )	->					List[str]:
									# Initialize image_processing
									lowerCAmelCase	:   Any					     =					self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									lowerCAmelCase	:   List[Any]					     =					prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_    ,  numpify=lowercase_ )
									for image in image_inputs:
															self.assertIsInstance(lowercase_    ,  np.ndarray )

									# Test not batched input
									lowerCAmelCase	:   List[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
									lowerCAmelCase	:   List[Any]					     =					image_processing(lowercase_    ,  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 							_snake_case   (    self )	->					Dict:
									# Initialize image_processing
									lowerCAmelCase	:   List[str]					     =					self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									lowerCAmelCase	:   Any					     =					prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=lowercase_    ,  torchify=lowercase_ )
									for image in image_inputs:
															self.assertIsInstance(lowercase_    ,  torch.Tensor )

									# Test not batched input
									lowerCAmelCase	:   Dict					     =					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
									lowerCAmelCase	:   Optional[Any]					     =					image_processing(lowercase_    ,  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"""],
									    )    ,  )






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


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(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf 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					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

