"""simple docstring"""




import argparse
from collections import defaultdict







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""{file}_{class_name}_{test_name}"""
		done_test[_id] += 1

		with open(lowerCamelCase__      ,	"""r"""  ) as f:
				lowerCAmelCase__   = f.readlines()

		lowerCAmelCase__   = f"""class {class_name}("""
		lowerCAmelCase__   = f"""{4 * ' '}def {test_name}("""
		lowerCAmelCase__   = f"""{8 * ' '}{correct_line.split()[0]}"""
		lowerCAmelCase__   = f"""{16 * ' '}{correct_line.split()[0]}"""
		lowerCAmelCase__   = False
		lowerCAmelCase__   = False
		lowerCAmelCase__   = False
		lowerCAmelCase__   = False
		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0

		lowerCAmelCase__   = []
		for line in lines:
				if line.startswith(lowerCamelCase__  ):
						lowerCAmelCase__   = True
				elif in_class and line.startswith(lowerCamelCase__  ):
						lowerCAmelCase__   = True
				elif in_class and in_func and (line.startswith(lowerCamelCase__  ) or line.startswith(lowerCamelCase__  )):
						lowerCAmelCase__   = len(line.split(correct_line.split()[0]  )[0]  )
						count += 1

						if count == done_test[_id]:
								lowerCAmelCase__   = True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								lowerCAmelCase__   = True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(f"""{spaces * ' '}{correct_line}"""  )
						lowerCAmelCase__   = lowerCAmelCase__   = lowerCAmelCase__   = lowerCAmelCase__   = False
				else:
						new_lines.append(lowerCamelCase__  )

		with open(lowerCamelCase__      ,	"""w"""  ) as f:
				for line in new_lines:
						f.write(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__=None  ):





		"""simple docstring"""




		if fail is not None:
				with open(lowerCamelCase__      ,	"""r"""  ) as f:
						lowerCAmelCase__   = {l.strip() for l in f.readlines()}
		else:
				lowerCAmelCase__   = None

		with open(lowerCamelCase__      ,	"""r"""  ) as f:
				lowerCAmelCase__   = f.readlines()

		lowerCAmelCase__   = defaultdict(lowerCamelCase__  )
		for line in correct_lines:
				lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = line.split(""";"""  )
				if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
						overwrite_file(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				Any    				=    argparse.ArgumentParser()
			parser.add_argument("--correct_filename", help="filename of tests with expected result")
			parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
			__lowerCAmelCase     :				str    				=    parser.parse_args()

			main(args.correct_filename, args.fail_filename)





"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




