




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = 16       ,_SCREAMING_SNAKE_CASE = 88       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = 0.0       ,_SCREAMING_SNAKE_CASE = 32       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = "geglu"       ,_SCREAMING_SNAKE_CASE = None       ,)			->		Optional[Any]:
         super().__init__()
         UpperCAmelCase_      :							Optional[int]       =      nn.ModuleList(
             [
                 TransformeraDModel(
                     num_attention_heads=_SCREAMING_SNAKE_CASE       ,attention_head_dim=_SCREAMING_SNAKE_CASE       ,in_channels=_SCREAMING_SNAKE_CASE       ,num_layers=_SCREAMING_SNAKE_CASE       ,dropout=_SCREAMING_SNAKE_CASE       ,norm_num_groups=_SCREAMING_SNAKE_CASE       ,cross_attention_dim=_SCREAMING_SNAKE_CASE       ,attention_bias=_SCREAMING_SNAKE_CASE       ,sample_size=_SCREAMING_SNAKE_CASE       ,num_vector_embeds=_SCREAMING_SNAKE_CASE       ,activation_fn=_SCREAMING_SNAKE_CASE       ,num_embeds_ada_norm=_SCREAMING_SNAKE_CASE       ,)
                 for _ in range(2  )
             ]  )

         # Variables that can be set by a pipeline:

         # The ratio of transformer1 to transformer2's output states to be combined during inference
         UpperCAmelCase_      :							Tuple       =      0.5

         # The shape of `encoder_hidden_states` is expected to be
         # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
         UpperCAmelCase_      :							List[Any]       =      [77, 257]

         # Which transformer to use to encode which condition.
         # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
         UpperCAmelCase_      :							str       =      [1, 0]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE = True       ,)			->		str:
         UpperCAmelCase_      :							Optional[int]       =      hidden_states

         UpperCAmelCase_      :							Optional[int]       =      []
         UpperCAmelCase_      :							Any       =      0
         # attention_mask is not used yet
         for i in range(2  ):
              # for each of the two transformers, pass the corresponding condition tokens
              UpperCAmelCase_      :							Union[str, Any]       =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
              UpperCAmelCase_      :							int       =      self.transformer_index_for_condition[i]
              UpperCAmelCase_      :							str       =      self.transformers[transformer_index](
                  _SCREAMING_SNAKE_CASE       ,encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,timestep=_SCREAMING_SNAKE_CASE       ,cross_attention_kwargs=_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE       ,)[0]
              encoded_states.append(encoded_state - input_states  )
              tokens_start += self.condition_lengths[i]

         UpperCAmelCase_      :							str       =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
         UpperCAmelCase_      :							Union[str, Any]       =      output_states + input_states

         if not return_dict:
              return (output_states,)

         return TransformeraDModelOutput(sample=_SCREAMING_SNAKE_CASE  )





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )