

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 					__snake_case     (			unittest.TestCase				):

  def __init__(    self  ,						_A  ,						_A=7  ,						_A=3  ,						_A=18  ,						_A=30  ,						_A=400  ,						_A=True  ,						_A=None  ,						_A=2  ,						_A=True  ,						_A=[0.5, 0.5, 0.5]  ,						_A=[0.5, 0.5, 0.5]  ,						):
      SCREAMING_SNAKE_CASE_													=  size if size is not None else {'shortest_edge': 30}
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  min_resolution
      SCREAMING_SNAKE_CASE_													=  max_resolution
      SCREAMING_SNAKE_CASE_													=  do_resize
      SCREAMING_SNAKE_CASE_													=  size
      SCREAMING_SNAKE_CASE_													=  size_divisor
      SCREAMING_SNAKE_CASE_													=  do_normalize
      SCREAMING_SNAKE_CASE_													=  image_mean
      SCREAMING_SNAKE_CASE_													=  image_std

  def   lowerCAmelCase__   (    self):
      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   lowerCAmelCase__   (    self  ,						_A  ,						_A=False):
      if not batched:
          SCREAMING_SNAKE_CASE_													=  self.size['shortest_edge']
          SCREAMING_SNAKE_CASE_													=  image_inputs[0]
          if isinstance(_A  ,						Image.Image):
              SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  image.size
          else:
              SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  image.shape[1], image.shape[2]
          SCREAMING_SNAKE_CASE_													=  size / min(_A  ,						_A)
          if h < w:
              SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  size, scale * w
          else:
              SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  scale * h, size

          SCREAMING_SNAKE_CASE_													=  int((1333 / 800) * size)
          if max(_A  ,						_A) > max_size:
              SCREAMING_SNAKE_CASE_													=  max_size / max(_A  ,						_A)
              SCREAMING_SNAKE_CASE_													=  newh * scale
              SCREAMING_SNAKE_CASE_													=  neww * scale

          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  int(newh + 0.5), int(neww + 0.5)
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

      else:
          SCREAMING_SNAKE_CASE_													=  []
          for image in image_inputs:
              SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.get_expected_values([image])
              expected_values.append((expected_height, expected_width))
          SCREAMING_SNAKE_CASE_													=  max(_A  ,						key=lambda _A: item[0])[0]
          SCREAMING_SNAKE_CASE_													=  max(_A  ,						key=lambda _A: item[1])[1]

      return expected_height, expected_width






@require_torch
@require_vision
class 					__snake_case     (			lowerCAmelCase__   ,       unittest.TestCase				):
  __lowerCAmelCase   :		Any         =     ViltImageProcessor if is_vision_available() else None

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ViltImageProcessingTester(self)

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

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  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'))
      self.assertTrue(hasattr(_A  ,						'size_divisor'))

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size  ,						{'shortest_edge': 30})

      SCREAMING_SNAKE_CASE_													=  self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42)
      self.assertEqual(image_processor.size  ,						{'shortest_edge': 42})

  def   lowerCAmelCase__   (    self):
      pass

  def   lowerCAmelCase__   (    self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_													=  prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A  ,						Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_													=  image_processing(image_inputs[0]  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  image_processing(_A  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  image_processing(image_inputs[0]  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  image_processing(_A  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__   (    self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_													=  self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  image_processing(image_inputs[0]  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  image_processing(_A  ,						return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  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,
          )  ,						)





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    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  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}"""				)
    SCREAMING_SNAKE_CASE_													=  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__":
       UpperCamelCase__     :					Tuple								=			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."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


