






"""simple docstring"""
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class      UpperCamelCase_			(nn.Module	):





	def __init__( self   :					int      ,      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      ,      )		->     int:
				super().__init__()
				UpperCAmelCase_	:					Optional[Any]     	=   nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowerCAmelCase_      ,      attention_head_dim=lowerCAmelCase_      ,      in_channels=lowerCAmelCase_      ,      num_layers=lowerCAmelCase_      ,      dropout=lowerCAmelCase_      ,      norm_num_groups=lowerCAmelCase_      ,      cross_attention_dim=lowerCAmelCase_      ,      attention_bias=lowerCAmelCase_      ,      sample_size=lowerCAmelCase_      ,      num_vector_embeds=lowerCAmelCase_      ,      activation_fn=lowerCAmelCase_      ,      num_embeds_ada_norm=lowerCAmelCase_      ,      )
				        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_	:					Union[str, Any]     	=   0.5

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Dict:
				UpperCAmelCase_	:					Dict     	=   hidden_states

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

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

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowerCAmelCase_		)











# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  help=(
				        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
				        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
				        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
				        '''with \'huggingface\'.'''
				    )						,  )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




