





'''simple docstring'''



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       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			# Initialise PyTorch model
			__lowercase   =							json.load(open(lowerCamelCase_						,				'''r''' ) )
			__lowercase   =							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}" )
			__lowercase   =							GPTNeoForCausalLM(lowerCamelCase_ )

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

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


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.'''
	)
	_SCREAMING_SNAKE_CASE    =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












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 CLIPImageProcessor





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=7			,  __lowerCamelCase=3			,  __lowerCamelCase=1_8			,  __lowerCamelCase=3_0			,  __lowerCamelCase=4_0_0			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]			,  __lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,  __lowerCamelCase=True			,  )	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							size if size is not None else {"shortest_edge": 2_0}
            _SCREAMING_SNAKE_CASE				:		int  			=							crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _SCREAMING_SNAKE_CASE				:		str  			=							parent
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_channels
            _SCREAMING_SNAKE_CASE				:		Any  			=							image_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							min_resolution
            _SCREAMING_SNAKE_CASE				:		int  			=							max_resolution
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							do_resize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_center_crop
            _SCREAMING_SNAKE_CASE				:		Dict  			=							crop_size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_normalize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_mean
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_std
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							do_convert_rgb




       def 			UpperCamelCase_	(					self							)	->   List[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,
            }







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)	->   Tuple:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
                 for i in range(self.batch_size							):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5			,  size=(self.num_channels, self.max_resolution, self.max_resolution)			,  dtype=np.uinta							)							)
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							[]
                 for i in range(self.batch_size							):
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							np.random.choice(np.arange(self.min_resolution			,  self.max_resolution							)			,  2							)
                      image_inputs.append(np.random.randint(2_5_5			,  size=(self.num_channels, width, height)			,  dtype=np.uinta							)							)

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

            if torchify:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[torch.from_numpy(__lowerCamelCase							) for x in image_inputs]

            return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							CLIPImageProcessingTester(self							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 2_0}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 1_8, "width": 1_8}							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict			,  size=4_2			,  crop_size=8_4							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 4_2}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 8_4, "width": 8_4}							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            pass




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

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		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
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  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 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random numpy tensors
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  numpify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  np.ndarray							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		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
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							image_processing(__lowerCamelCase			,  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 			UpperCamelCase_	(					self							)	->   Any:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PyTorch tensors
            _SCREAMING_SNAKE_CASE				:		int  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  torchify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  torch.Tensor							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  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 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							CLIPImageProcessingTester(self			,  num_channels=4							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							3




       @property
       def 			UpperCamelCase_	(					self							)	->   Dict:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            pass







       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							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
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							image_processing(__lowerCamelCase			,  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"],
                )			,  )