



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_MAPPING,
         BeitForImageClassification,
         BeitForMaskedImageModeling,
         BeitForSemanticSegmentation,
         BeitModel,
     )
     from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     import PIL
     from PIL import Image

     from transformers import BeitImageProcessor
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1_0_0		,				SCREAMING_SNAKE_CASE=1_3		,				SCREAMING_SNAKE_CASE=3_0		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=3_2		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=3_7		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=1_0		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=[0, 1, 2, 3]		,				) ->			List[str]:
  a__												= parent
  a__												= 1_0_0
  a__												= batch_size
  a__												= image_size
  a__												= patch_size
  a__												= num_channels
  a__												= is_training
  a__												= use_labels
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= intermediate_size
  a__												= hidden_act
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= type_sequence_label_size
  a__												= initializer_range
  a__												= scope
  a__												= out_indices
  a__												= num_labels

  # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  a__												= (image_size // patch_size) ** 2
  a__												= num_patches + 1

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

  a__												= None
  a__												= None
  if self.use_labels:
   a__												= ids_tensor([self.batch_size]		,				self.type_sequence_label_size      )
   a__												= ids_tensor([self.batch_size, self.image_size, self.image_size]		,				self.num_labels      )

  a__												= self.get_config()

  return config, pixel_values, labels, pixel_labels

 def 						_UpperCAmelCase       (	self      ) ->			str:
  return BeitConfig(
      vocab_size=self.vocab_size		,				image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				is_decoder=SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				out_indices=self.out_indices		,				)

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= BeitModel(config=SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= BeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.type_sequence_label_size
  a__												= BeitForImageClassification(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)      )

  # test greyscale images
  a__												= 1
  a__												= BeitForImageClassification(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
  a__												= model(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.num_labels
  a__												= BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()
  a__												= model(SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(
      result.logits.shape		,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
  a__												= model(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(
      result.logits.shape		,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )



 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= self.prepare_config_and_inputs()
  a__							,			a__							,			a__							,			a__												= config_and_inputs
  a__												= {'''pixel_values''': pixel_values}
  return config, inputs_dict





@require_torch
class    __UpperCamelCase			(       _lowercase	,	_lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    str      						=					(
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 _lowercase   :    Dict      						=					(
     {
         '''feature-extraction''': BeitModel,
         '''image-classification''': BeitForImageClassification,
         '''image-segmentation''': BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _lowercase   :    List[Any]      						=					False
 _lowercase   :    Union[str, Any]      						=					False
 _lowercase   :    List[Any]      						=					False

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= BeitModelTester(self      )
  a__												= ConfigTester(self		,				config_class=SCREAMING_SNAKE_CASE		,				has_text_modality=SCREAMING_SNAKE_CASE		,				hidden_size=3_7      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  self.config_tester.run_common_tests()

 @unittest.skip(reason='''BEiT does not use inputs_embeds'''      )
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  pass

 @require_torch_multi_gpu
 @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''      )
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  pass

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   a__												= model_class(SCREAMING_SNAKE_CASE      )
   self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)      )
   a__												= model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,				nn.Linear      )      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   a__												= model_class(SCREAMING_SNAKE_CASE      )
   a__												= inspect.signature(model.forward      )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   a__												= [*signature.parameters.keys()]

   a__												= ['''pixel_values''']
   self.assertListEqual(arg_names[:1]		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  if not self.model_tester.is_training:
   return

  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()
  a__												= True

  for model_class in self.all_model_classes:
   # we don't test BeitForMaskedImageModeling
   if model_class in [*get_values(SCREAMING_SNAKE_CASE      ), BeitForMaskedImageModeling]:
    continue

   a__												= model_class(SCREAMING_SNAKE_CASE      )
   model.to(SCREAMING_SNAKE_CASE      )
   model.train()
   a__												= self._prepare_for_class(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				return_labels=SCREAMING_SNAKE_CASE      )
   a__												= model(**SCREAMING_SNAKE_CASE      ).loss
   loss.backward()

 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()
  if not self.model_tester.is_training:
   return

  a__												= False
  a__												= True

  for model_class in self.all_model_classes:
   # we don't test BeitForMaskedImageModeling
   if (
       model_class in [*get_values(SCREAMING_SNAKE_CASE      ), BeitForMaskedImageModeling]
       or not model_class.supports_gradient_checkpointing
   ):
    continue

   a__												= model_class(SCREAMING_SNAKE_CASE      )
   model.gradient_checkpointing_enable()
   model.to(SCREAMING_SNAKE_CASE      )
   model.train()
   a__												= self._prepare_for_class(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				return_labels=SCREAMING_SNAKE_CASE      )
   a__												= model(**SCREAMING_SNAKE_CASE      ).loss
   loss.backward()

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  a__												= _config_zero_init(SCREAMING_SNAKE_CASE      )
  for model_class in self.all_model_classes:
   a__												= model_class(config=SCREAMING_SNAKE_CASE      )
   for name, param in model.named_parameters():
    # we skip lambda parameters as these require special initial values
    # determined by config.layer_scale_init_value
    if "lambda" in name:
     continue
    if param.requires_grad:
     self.assertIn(
         ((param.data.mean() * 1e9).round() / 1e9).item()		,				[0.0, 1.0]		,				msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,				)



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   a__												= BeitModel.from_pretrained(SCREAMING_SNAKE_CASE      )
   self.assertIsNotNone(SCREAMING_SNAKE_CASE      )






def     __a					(		):
 a__												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
 return image

@require_torch
@require_vision
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 @cached_property
 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__												= BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      ).to(SCREAMING_SNAKE_CASE      )

  a__												= self.default_image_processor
  a__												= prepare_img()
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE      )

  # prepare bool_masked_pos
  a__												= torch.ones((1, 1_9_6)		,				dtype=torch.bool      ).to(SCREAMING_SNAKE_CASE      )

  # forward pass
  with torch.no_grad():
   a__												= model(pixel_values=SCREAMING_SNAKE_CASE		,				bool_masked_pos=SCREAMING_SNAKE_CASE      )
  a__												= outputs.logits

  # verify the logits
  a__												= torch.Size((1, 1_9_6, 8_1_9_2)      )
  self.assertEqual(logits.shape		,				SCREAMING_SNAKE_CASE      )

  a__												= torch.tensor(
      [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      ).to(SCREAMING_SNAKE_CASE      )

  self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]		,				SCREAMING_SNAKE_CASE		,				atol=1e-2      )      )

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      ).to(SCREAMING_SNAKE_CASE      )

  a__												= self.default_image_processor
  a__												= prepare_img()
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

  # forward pass
  with torch.no_grad():
   a__												= model(**SCREAMING_SNAKE_CASE      )
  a__												= outputs.logits

  # verify the logits
  a__												= torch.Size((1, 1_0_0_0)      )
  self.assertEqual(logits.shape		,				SCREAMING_SNAKE_CASE      )

  a__												= torch.tensor([-1.23_85, -1.09_87, -1.01_08]      ).to(SCREAMING_SNAKE_CASE      )

  self.assertTrue(torch.allclose(logits[0, :3]		,				SCREAMING_SNAKE_CASE		,				atol=1e-4      )      )

  a__												= 2_8_1
  self.assertEqual(logits.argmax(-1      ).item()		,				SCREAMING_SNAKE_CASE      )

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      ).to(
      SCREAMING_SNAKE_CASE      )

  a__												= self.default_image_processor
  a__												= prepare_img()
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

  # forward pass
  with torch.no_grad():
   a__												= model(**SCREAMING_SNAKE_CASE      )
  a__												= outputs.logits

  # verify the logits
  a__												= torch.Size((1, 2_1_8_4_1)      )
  self.assertEqual(logits.shape		,				SCREAMING_SNAKE_CASE      )

  a__												= torch.tensor([1.68_81, -0.27_87, 0.59_01]      ).to(SCREAMING_SNAKE_CASE      )

  self.assertTrue(torch.allclose(logits[0, :3]		,				SCREAMING_SNAKE_CASE		,				atol=1e-4      )      )

  a__												= 2_3_9_6
  self.assertEqual(logits.argmax(-1      ).item()		,				SCREAMING_SNAKE_CASE      )

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
  a__												= model.to(SCREAMING_SNAKE_CASE      )

  a__												= BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE		,				size=6_4_0		,				do_center_crop=SCREAMING_SNAKE_CASE      )

  a__												= load_dataset('''hf-internal-testing/fixtures_ade20k'''		,				split='''test'''      )
  a__												= Image.open(ds[0]['''file''']      )
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

  # forward pass
  with torch.no_grad():
   a__												= model(**SCREAMING_SNAKE_CASE      )
  a__												= outputs.logits

  # verify the logits
  a__												= torch.Size((1, 1_5_0, 1_6_0, 1_6_0)      )
  self.assertEqual(logits.shape		,				SCREAMING_SNAKE_CASE      )

  a__												= version.parse(PIL.__version__      ) < version.parse('''9.0.0'''      )

  if is_pillow_less_than_a:
   a__												= torch.tensor(
       [
           [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
           [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
           [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
       ]		,				device=SCREAMING_SNAKE_CASE		,				)
  else:
   a__												= torch.tensor(
       [
           [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
           [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
           [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
       ]		,				device=SCREAMING_SNAKE_CASE		,				)

  self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,				SCREAMING_SNAKE_CASE		,				atol=1e-4      )      )



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
  a__												= model.to(SCREAMING_SNAKE_CASE      )

  a__												= BeitImageProcessor(do_resize=SCREAMING_SNAKE_CASE		,				size=6_4_0		,				do_center_crop=SCREAMING_SNAKE_CASE      )

  a__												= load_dataset('''hf-internal-testing/fixtures_ade20k'''		,				split='''test'''      )
  a__												= Image.open(ds[0]['''file''']      )
  a__												= image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

  # forward pass
  with torch.no_grad():
   a__												= model(**SCREAMING_SNAKE_CASE      )

  a__												= outputs.logits.detach().cpu()

  a__												= image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE		,				target_sizes=[(5_0_0, 3_0_0)]      )
  a__												= torch.Size((5_0_0, 3_0_0)      )
  self.assertEqual(segmentation[0].shape		,				SCREAMING_SNAKE_CASE      )

  a__												= image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE      )
  a__												= torch.Size((1_6_0, 1_6_0)      )
  self.assertEqual(segmentation[0].shape		,				SCREAMING_SNAKE_CASE      )










import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 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(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   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=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 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=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





