
"""simple docstring"""




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     test_file.split(os.path.sep		)
							if components[0:2] != ["tests", "models"]:
														raise ValueError(
														    """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
														    f'''{test_file} instead.'''		)
							SCREAMING_SNAKE_CASE__		: str     =     components[-1]
							if not test_fn.endswith("""py"""		):
														raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''		)
							if not test_fn.startswith("""test_modeling_"""		):
														raise ValueError(
														    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     components[:-1] + [test_fn.replace(""".py"""     ,""""""		)]
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     """.""".join(_snake_case		)

							return test_module_path

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_module_path(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     importlib.import_module(_snake_case		)

							return test_module

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
							SCREAMING_SNAKE_CASE__		: str     =     get_test_module(_snake_case		)
							for attr in dir(_snake_case		):
														if attr.endswith("""ModelTester"""		):
																					tester_classes.append(getattr(_snake_case     ,_snake_case		)		)

    # sort with class names
							return sorted(_snake_case     ,key=lambda _snake_case		: x.__name__		)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     []
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     get_test_module(_snake_case		)
							for attr in dir(_snake_case		):
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     getattr(_snake_case     ,_snake_case		)
														# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
														# `all_model_classes` is not empty (which also excludes other special classes).
														SCREAMING_SNAKE_CASE__		: str     =     getattr(_snake_case     ,"""all_model_classes"""     ,[]		)
														if len(_snake_case		) > 0:
																					test_classes.append(_snake_case		)

    # sort with class names
							return sorted(_snake_case     ,key=lambda _snake_case		: x.__name__		)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_test_classes(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     set()
							for test_class in test_classes:
														model_classes.update(test_class.all_model_classes		)

							# sort with class names
							return sorted(_snake_case     ,key=lambda _snake_case		: x.__name__		)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: int     =     test_class()
							if hasattr(_snake_case     ,"""setUp"""		):
														test.setUp()

							SCREAMING_SNAKE_CASE__		: int     =     None
							if hasattr(_snake_case     ,"""model_tester"""		):
														# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
														if test.model_tester is not None:
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     test.model_tester.__class__

							return model_tester

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_test_classes(_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     []
							for test_class in test_classes:
														if model_class in test_class.all_model_classes:
																					target_test_classes.append(_snake_case		)

    # sort with class names
							return sorted(_snake_case     ,key=lambda _snake_case		: x.__name__		)

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: int     =     get_test_classes_for_model(_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for test_class in test_classes:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_model_tester_from_test_class(_snake_case		)
														if tester_class is not None:
																					tester_classes.append(_snake_case		)

    # sort with class names
							return sorted(_snake_case     ,key=lambda _snake_case		: x.__name__		)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     get_test_classes(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {test_class: get_model_tester_from_test_class(_snake_case		) for test_class in test_classes}
							return test_tester_mapping

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_model_classes(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     {
							    model_class: get_test_classes_for_model(_snake_case     ,_snake_case		) for model_class in model_classes
							}
							return model_test_mapping

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     get_model_classes(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {
							    model_class: get_tester_classes_for_model(_snake_case     ,_snake_case		) for model_class in model_classes
							}
							return model_to_tester_mapping

def lowercase_						(     _snake_case		):
							if isinstance(_snake_case     ,_snake_case		):
														return o
							elif isinstance(_snake_case     ,_snake_case		):
														return o.__name__
							elif isinstance(_snake_case     ,(list, tuple)		):
														return [to_json(_snake_case		) for x in o]
							elif isinstance(_snake_case     ,_snake_case		):
														return {to_json(_snake_case		): to_json(_snake_case		) for k, v in o.items()}
							else:
														return o






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(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]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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]

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

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




