
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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    def get_masked_lm_array(__lowerCAmelCase							):
        snake_case__						=				F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        snake_case__						=				tf.train.load_variable(_lowerCamelCase    ,		_lowerCamelCase							)

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

        return torch.from_numpy(_lowerCamelCase							)

    def get_encoder_array(__lowerCAmelCase							):
        snake_case__						=				F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        snake_case__						=				tf.train.load_variable(_lowerCamelCase    ,		_lowerCamelCase							)

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

        return torch.from_numpy(_lowerCamelCase							)

    def get_encoder_layer_array(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        snake_case__						=				tf.train.load_variable(_lowerCamelCase    ,		_lowerCamelCase							)

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

        return torch.from_numpy(_lowerCamelCase							)

    def get_encoder_attention_layer_array(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        snake_case__						=				tf.train.load_variable(_lowerCamelCase    ,		_lowerCamelCase							)
        snake_case__						=				array.reshape(_lowerCamelCase							)

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

        return torch.from_numpy(_lowerCamelCase							)

    print(F"""Loading model based on config from {config_path}..."""							)
    snake_case__						=				BertConfig.from_json_file(_lowerCamelCase							)
    snake_case__						=				BertForMaskedLM(_lowerCamelCase							)

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

        # Self-attention
        snake_case__						=				layer.attention.self

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

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

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

        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_attention_layer_norm/gamma"							)
        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_attention_layer_norm/beta"							)

        # Intermediate
        snake_case__						=				layer.intermediate

        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_intermediate_dense/kernel"							)
        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_intermediate_dense/bias"							)

        # Output
        snake_case__						=				layer.output

        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_output_dense/kernel"							)
        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_output_dense/bias"							)

        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_output_layer_norm/gamma"							)
        snake_case__						=				get_encoder_layer_array(_lowerCamelCase    ,		"_output_layer_norm/beta"							)

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

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

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

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

    snake_case__						=				get_masked_lm_array("embedding_table"							)

    # Pooling
    snake_case__						=				BertPooler(config=_lowerCamelCase							)
    snake_case__						=				get_encoder_array("_pooler_layer/kernel"							)
    snake_case__						=				get_encoder_array("_pooler_layer/bias"							)

    # Export final model
    model.save_pretrained(_lowerCamelCase							)

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

    print("Model conversion was done sucessfully!"							)


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





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			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=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

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

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



