





'''simple docstring'''






import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor




class 			UpperCAmelCase					(      unittest.TestCase       ):




      def __init__(      self      :			Dict      ,      __snake_case      :			Any      ,      __snake_case      :			Union[str, Any]=7      ,      __snake_case      :			Optional[int]=3      ,      __snake_case      :			List[Any]=18      ,      __snake_case      :			Tuple=30      ,      __snake_case      :			Tuple=4_00      ,      __snake_case      :			int=True      ,      __snake_case      :			str=None      ,      __snake_case      :			Optional[Any]=True      ,      __snake_case      :			Tuple=[0.5, 0.5, 0.5]      ,      __snake_case      :			Optional[Any]=[0.5, 0.5, 0.5]      ,      )    ->				Union[str, Any]:
            _lowerCAmelCase												=							size if size is not None else {"""height""": 18, """width""": 18}
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							num_channels
            _lowerCAmelCase												=							image_size
            _lowerCAmelCase												=							min_resolution
            _lowerCAmelCase												=							max_resolution
            _lowerCAmelCase												=							do_resize
            _lowerCAmelCase												=							size
            _lowerCAmelCase												=							do_normalize
            _lowerCAmelCase												=							image_mean
            _lowerCAmelCase												=							image_std




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }




@require_torch
@require_vision
class 			UpperCAmelCase					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      List[Any]											=       DPTImageProcessor if is_vision_available() else None




      def     lowercase__   (      self      :			List[str]  )    ->				str:
            _lowerCAmelCase												=							DPTImageProcessingTester(self  )




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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case      ,      """image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """image_std"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """size"""  )  )




      def     lowercase__   (      self      :			Tuple  )    ->				str:
            _lowerCAmelCase												=							self.image_processing_class.from_dict(self.image_processor_dict  )
            self.assertEqual(image_processor.size      ,      {"""height""": 18, """width""": 18}  )

            _lowerCAmelCase												=							self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42  )
            self.assertEqual(image_processor.size      ,      {"""height""": 42, """width""": 42}  )




      def     lowercase__   (      self      :			int  )    ->				int:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      Image.Image  )

            # Test not batched input
            _lowerCAmelCase												=							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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[Any]:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case      ,      numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      np.ndarray  )

            # Test not batched input
            _lowerCAmelCase												=							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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )




      def     lowercase__   (      self      :			Tuple  )    ->				Dict:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case      ,      torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      torch.Tensor  )

            # Test not batched input
            _lowerCAmelCase												=							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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      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.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )      ,      )













'''simple docstring'''






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      def get_masked_lm_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_layer_array(lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_attention_layer_array(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )
            _lowerCAmelCase												=							array.reshape(lowerCAmelCase  )

            if "kernel" in name:
                  _lowerCAmelCase												=							array.transpose()

            return torch.from_numpy(lowerCAmelCase  )

      print(f"Loading model based on config from {config_path}..."  )
      _lowerCAmelCase												=							BertConfig.from_json_file(lowerCAmelCase  )
      _lowerCAmelCase												=							BertForMaskedLM(lowerCAmelCase  )

      # Layers
      for layer_index in range(0							,     config.num_hidden_layers  ):
            _lowerCAmelCase												=							model.bert.encoder.layer[layer_index]

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/kernel"""							,     self_attn.query.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/bias"""							,     self_attn.query.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/kernel"""							,     self_attn.key.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/bias"""							,     self_attn.key.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/kernel"""							,     self_attn.value.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/bias"""							,     self_attn.value.bias.data.shape  )

            # Self-attention Output
            _lowerCAmelCase												=							layer.attention.output

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/kernel"""							,     self_output.dense.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/bias"""							,     self_output.dense.bias.data.shape  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

      # LM Head
      _lowerCAmelCase												=							model.cls.predictions.transform

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

      # Integration test - should load without any errors ;)
      _lowerCAmelCase												=							BertForMaskedLM.from_pretrained(lowerCAmelCase  )
      print(new_model.eval()  )

      print("""Model conversion was done sucessfully!"""  )


if __name__ == "__main__":
      A__    :       str              =argparse.ArgumentParser()
      parser.add_argument(
          '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
      )
      parser.add_argument(
          '''--bert_config_file''',
          type=str,
          required=True,
          help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''',
          type=str,
          required=True,
          help='''Path to the output PyTorch model.''',
      )
      A__    :       Dict              =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






