

"""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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:


 def get_masked_lm_array(__UpperCamelCase						):
  __magic_name__     =      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  __magic_name__     =      tf.train.load_variable(__lowerCAmelCase      ,   __lowerCAmelCase						)

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

  return torch.from_numpy(__lowerCAmelCase						)

 def get_encoder_array(__UpperCamelCase						):
  __magic_name__     =      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  __magic_name__     =      tf.train.load_variable(__lowerCAmelCase      ,   __lowerCAmelCase						)

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

  return torch.from_numpy(__lowerCAmelCase						)

 def get_encoder_layer_array(__UpperCamelCase      ,   __UpperCamelCase						):
  __magic_name__     =      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  __magic_name__     =      tf.train.load_variable(__lowerCAmelCase      ,   __lowerCAmelCase						)

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

  return torch.from_numpy(__lowerCAmelCase						)

 def get_encoder_attention_layer_array(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						):
  __magic_name__     =      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  __magic_name__     =      tf.train.load_variable(__lowerCAmelCase      ,   __lowerCAmelCase						)
  __magic_name__     =      array.reshape(__lowerCAmelCase						)

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

  return torch.from_numpy(__lowerCAmelCase						)

 print(f'''Loading model based on config from {config_path}...'''						)
 __magic_name__     =      BertConfig.from_json_file(__lowerCAmelCase						)
 __magic_name__     =      BertForMaskedLM(__lowerCAmelCase						)

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

  # Self-attention
  __magic_name__     =      layer.attention.self

  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_query_dense/kernel'''      ,   self_attn.query.weight.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_query_dense/bias'''      ,   self_attn.query.bias.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_key_dense/kernel'''      ,   self_attn.key.weight.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_key_dense/bias'''      ,   self_attn.key.bias.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_value_dense/kernel'''      ,   self_attn.value.weight.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_value_dense/bias'''      ,   self_attn.value.bias.data.shape						)

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

  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_output_dense/kernel'''      ,   self_output.dense.weight.data.shape						)
  __magic_name__     =      get_encoder_attention_layer_array(
      __lowerCAmelCase      ,   '''_output_dense/bias'''      ,   self_output.dense.bias.data.shape						)

  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_attention_layer_norm/gamma'''						)
  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_attention_layer_norm/beta'''						)

  # Intermediate
  __magic_name__     =      layer.intermediate

  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_intermediate_dense/kernel'''						)
  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_intermediate_dense/bias'''						)

  # Output
  __magic_name__     =      layer.output

  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_output_dense/kernel'''						)
  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_output_dense/bias'''						)

  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_output_layer_norm/gamma'''						)
  __magic_name__     =      get_encoder_layer_array(__lowerCAmelCase      ,   '''_output_layer_norm/beta'''						)

 # Embeddings
 __magic_name__     =      get_encoder_array('''_position_embedding_layer/embeddings'''						)
 __magic_name__     =      get_encoder_array('''_type_embedding_layer/embeddings'''						)
 __magic_name__     =      get_encoder_array('''_embedding_norm_layer/gamma'''						)
 __magic_name__     =      get_encoder_array('''_embedding_norm_layer/beta'''						)

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

 __magic_name__     =      get_masked_lm_array('''dense/kernel'''						)
 __magic_name__     =      get_masked_lm_array('''dense/bias'''						)

 __magic_name__     =      get_masked_lm_array('''layer_norm/gamma'''						)
 __magic_name__     =      get_masked_lm_array('''layer_norm/beta'''						)

 __magic_name__     =      get_masked_lm_array('''embedding_table'''						)

 # Pooling
 __magic_name__     =      BertPooler(config=__lowerCAmelCase						)
 __magic_name__     =      get_encoder_array('''_pooler_layer/kernel'''						)
 __magic_name__     =      get_encoder_array('''_pooler_layer/bias'''						)

 # Export final model
 model.save_pretrained(__lowerCAmelCase						)

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

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


if __name__ == "__main__":
 __lowerCamelCase      						=		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.",
 )
 __lowerCamelCase      						=		parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (EulerDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
       self.check_over_configs(beta_start=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 0.0_0_0_2       ) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_ ,						use_karras_sigmas=UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1E-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1E-3

