



"""simple docstring"""





import argparse
import os
import re


lowercase  							=					'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase  							=					re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
lowercase  							=					re.compile(r'''\s*\(\s*"(\S[^"]+)"''')




def 	UpperCAmelCase     (		A	:						List[str]     ,				A	:						bool = False       ):

		'''simple docstring'''


		with open(A     ,				'r'     ,				encoding='utf-8'       ) as f:
				_UpperCAmelCase							 =		f.read()

		_UpperCAmelCase							 =		content.split('\n'       )
		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		0
		while line_idx < len(A       ):
				if _re_intro_mapping.search(lines[line_idx]       ) is not None:
						_UpperCAmelCase							 =		len(re.search(r'^(\s*)\S'     ,				lines[line_idx]       ).groups()[0]       ) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(' ' * indent + '('       ):
								new_lines.append(lines[line_idx]       )
								line_idx += 1

						_UpperCAmelCase							 =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										_UpperCAmelCase							 =		line_idx
										while not lines[line_idx].startswith(' ' * indent + ')'       ):
												line_idx += 1
										blocks.append('\n'.join(lines[start_idx : line_idx + 1]       )       )
								else:
										blocks.append(lines[line_idx]       )
								line_idx += 1

						# Sort blocks by their identifiers
						_UpperCAmelCase							 =		sorted(A     ,				key=lambda A       : _re_identifier.search(A       ).groups()[0]       )
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]       )
						line_idx += 1

		if overwrite:
				with open(A     ,				'w'     ,				encoding='utf-8'       ) as f:
						f.write('\n'.join(A       )       )
		elif "\n".join(A       ) != content:
				return True




def 	UpperCAmelCase     (		A	:						bool = False       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[os.path.join(A     ,				A       ) for f in os.listdir(A       ) if f.endswith('.py'       )]
		_UpperCAmelCase							 =		[sort_auto_mapping(A     ,				overwrite=A       ) for fname in fnames]

		if not overwrite and any(A       ):
				_UpperCAmelCase							 =		[f for f, d in zip(A     ,				A       ) if d]
				raise ValueError(
				    f'The following files have auto mappings that need sorting: {", ".join(A       )}. Run `make style` to fix'
				    ' this.'       )


if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser()
					parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
					lowercase  							=					parser.parse_args()

					sort_all_auto_mappings(not args.check_only)







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=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							 =		0.5

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

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

									_UpperCAmelCase							 =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									_UpperCAmelCase							 =		output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


