


'''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 MobileNetVaImageProcessor





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=7     ,							__UpperCamelCase=3     ,							__UpperCamelCase=18     ,							__UpperCamelCase=30     ,							__UpperCamelCase=400     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							)       ->    Any:
          _a						=		size if size is not None else {"shortest_edge": 20}
          _a						=		crop_size if crop_size is not None else {"height": 18, "width": 18}
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		image_size
          _a						=		min_resolution
          _a						=		max_resolution
          _a						=		do_resize
          _a						=		size
          _a						=		do_center_crop
          _a						=		crop_size






     def  a_					(			self		)       ->    int:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				MobileNetVaImageProcessor if is_vision_available() else None






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		MobileNetVaImageProcessingTester(self		)






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






     def  a_					(			self		)       ->    Any:
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"do_resize"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"size"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"do_center_crop"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"crop_size"		)		)






     def  a_					(			self		)       ->    int:
          _a						=		self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size     ,							{"shortest_edge": 20}		)
          self.assertEqual(image_processor.crop_size     ,							{"height": 18, "width": 18}		)

          _a						=		self.image_processing_class.from_dict(self.image_processor_dict     ,							size=42     ,							crop_size=84		)
          self.assertEqual(image_processor.size     ,							{"shortest_edge": 42}		)
          self.assertEqual(image_processor.crop_size     ,							{"height": 84, "width": 84}		)






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






     def  a_					(			self		)       ->    Optional[int]:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							Image.Image		)

          # Test not batched input
          _a						=		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
          _a						=		image_processing(__UpperCamelCase     ,							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  a_					(			self		)       ->    str:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__UpperCamelCase     ,							numpify=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							np.ndarray		)

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

          # Test not batched input
          _a						=		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
          _a						=		image_processing(__UpperCamelCase     ,							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"],
              )     ,							)





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(__lowerCamelCase				):
          os.makedirs(__lowerCamelCase				)

     _a						=		model.state_dict()

     def to_tf_var_name(__lowerCamelCase		: str				):
          for patt, repl in iter(__lowerCamelCase				):
               _a						=		name.replace(__lowerCamelCase      ,  __lowerCamelCase				)
          return F"bert/{name}"

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		tf.get_variable(dtype=__lowerCamelCase      ,  shape=tensor.shape      ,  name=__lowerCamelCase      ,  initializer=tf.zeros_initializer()				)
          session.run(tf.variables_initializer([tf_var]				)				)
          session.run(__lowerCamelCase				)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--model_name"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="model name e.g. bert-base-uncased"				)
     parser.add_argument(
         "--cache_dir"      ,  type=__lowerCamelCase      ,  default=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory containing pytorch model"				)
     parser.add_argument("--pytorch_model_path"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="/path/to/<pytorch-model-name>.bin"				)
     parser.add_argument("--tf_cache_dir"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory in which to save tensorflow model"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name      ,  state_dict=torch.load(args.pytorch_model_path				)      ,  cache_dir=args.cache_dir      ,  )

     convert_pytorch_checkpoint_to_tf(model=__lowerCamelCase      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name				)


if __name__ == "__main__":
   main()

