





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					int = 16      ,				UpperCamelCase      :					int = 88      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					int = 1      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					int = 32      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					str = "geglu"      ,				UpperCamelCase      :					Optional[int] = None      ,				):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				Dict														=	nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=UpperCamelCase      ,				attention_head_dim=UpperCamelCase      ,				in_channels=UpperCamelCase      ,				num_layers=UpperCamelCase      ,				dropout=UpperCamelCase      ,				norm_num_groups=UpperCamelCase      ,				cross_attention_dim=UpperCamelCase      ,				attention_bias=UpperCamelCase      ,				sample_size=UpperCamelCase      ,				num_vector_embeds=UpperCamelCase      ,				activation_fn=UpperCamelCase      ,				num_embeds_ada_norm=UpperCamelCase      ,				)
            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
    _snake_case   :				int														=	0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    _snake_case   :				Optional[int]														=	[77, 2_57]

    # 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])`
    _snake_case   :				Optional[Any]														=	[1, 0]







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					str=None      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					bool = True      ,				):


    '''simple docstring'''





    _snake_case   :				str														=	hidden_states

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

    _snake_case   :				str														=	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    _snake_case   :				Optional[Any]														=	output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=UpperCamelCase							)











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




