'''simple docstring'''

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    lowerCamelCase__  (   unittest.TestCase     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=7					,      lowerCAmelCase__=3					,      lowerCAmelCase__=18					,      lowerCAmelCase__=30					,      lowerCAmelCase__=400					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      lowerCAmelCase__=2					,      lowerCAmelCase__=True					,      lowerCAmelCase__=[0.5, 0.5, 0.5]					,      lowerCAmelCase__=[0.5, 0.5, 0.5]					,      )				->     Dict:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """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					,      lowerCAmelCase__					,      lowerCAmelCase__=False     )				->     Union[str, Any]:
        """simple docstring"""





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

             _UpperCamelCase  :List[Any]       =int((1_333 / 800) * size     )
             if max(lowerCAmelCase__					,      lowerCAmelCase__     ) > max_size:
                  _UpperCamelCase  :Optional[Any]       =max_size / max(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Any       =newh * scale
                  _UpperCamelCase  :Dict       =neww * scale

             _UpperCamelCase       ,      _UpperCamelCase  :Any       =int(newh + 0.5     ), int(neww + 0.5     )
             _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

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

        return expected_height, expected_width




@require_torch
@require_vision
class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						ViltImageProcessor if is_vision_available() else None



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =ViltImageProcessingTester(self     )



   @property
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        return self.image_processor_tester.prepare_image_processor_dict()



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_mean"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_std"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_normalize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_resize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """size"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """size_divisor"""     )     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size					,      {"""shortest_edge""": 30}     )

        _UpperCamelCase  :List[str]       =self.image_processing_class.from_dict(self.image_processor_dict					,      size=42     )
        self.assertEqual(image_processor.size					,      {"""shortest_edge""": 42}     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        pass



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :int       =self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCamelCase  :Union[str, Any]       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      Image.Image     )

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

        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :List[Any]       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Any       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _UpperCamelCase  :Optional[Any]       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__					,      numpify=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      np.ndarray     )

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

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :Dict       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        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[str]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCamelCase  :int       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__					,      torchify=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      torch.Tensor     )

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

        _UpperCamelCase       ,      _UpperCamelCase  :str       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :Tuple       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_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)

