



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 						snake_case  (		nn.Module     ):



   '''simple docstring'''






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

    """simple docstring"""







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

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


   def    UpperCamelCase_     (    self							:	Any ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	int=None ,						lowerCAmelCase							:	str=None ,						lowerCAmelCase							:	List[str]=None ,						lowerCAmelCase							:	bool = True ,						) -> Tuple:

    """simple docstring"""







    _snake_case			:     List[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			:     str 		=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
     _snake_case			:     Any 		=    self.transformer_index_for_condition[i]
     _snake_case			:     int 		=    self.transformers[transformer_index](
         a_ ,						encoder_hidden_states=a_ ,						timestep=a_ ,						cross_attention_kwargs=a_ ,						return_dict=a_ ,						)[0]
     encoded_states.append(encoded_state - input_states)
     tokens_start += self.condition_lengths[i]

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

    if not return_dict:
     return (output_states,)

    return TransformeraDModelOutput(sample=a_)






"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Any=7    ):
  """simple docstring"""

  _snake_case							:		Any								=  None
  if token is not None:
    _snake_case							:		Any								=  {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  # The id of a workflow (not of a workflow run)
  _snake_case							:		List[str]								=  """636036"""

  _snake_case							:		Union[str, Any]								=  F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

  _snake_case							:		str								=  requests.get(snake_case__		,   headers=snake_case__    ).json()

  return result["workflow_runs"]


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  get_daily_ci_runs(snake_case__    )
  _snake_case							:		str								=  None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      _snake_case							:		List[str]								=  workflow_run["""id"""]
      break

  return workflow_run_id


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_last_daily_ci_runs(snake_case__    )
  if workflow_run_id is not None:
    _snake_case							:		Optional[Any]								=  get_artifacts_links(worflow_run_id=snake_case__		,   token=snake_case__    )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        _snake_case							:		Optional[int]								=  artifacts_links[artifact_name]
        download_artifact(
            artifact_name=snake_case__		,   artifact_url=snake_case__		,   output_dir=snake_case__		,   token=snake_case__    )






def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[str]		,   snake_case__							:					int    ):
  """simple docstring"""

  get_last_daily_ci_artifacts(snake_case__		,   snake_case__		,   snake_case__    )

  _snake_case							:		int								=  {}
  for artifact_name in artifact_names:
    _snake_case							:		int								=  os.path.join(snake_case__		,   F"{artifact_name}.zip"    )
    if os.path.isfile(snake_case__    ):
      _snake_case							:		Tuple								=  {}
      with zipfile.ZipFile(snake_case__    ) as z:
        for filename in z.namelist():
          if not os.path.isdir(snake_case__    ):
            # read the file
            with z.open(snake_case__    ) as f:
              _snake_case							:		Any								=  f.read().decode("""UTF-8"""    )

  return results


