





'''simple docstring'''






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class  _A (					nn.Module		):

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


					"""simple docstring"""

					super().__init__()
					__snake_case					:      List[Any]							      =     nn.ModuleList(
					    [
					        TransformeraDModel(
					            num_attention_heads=__magic_name__  ,      attention_head_dim=__magic_name__  ,      in_channels=__magic_name__  ,      num_layers=__magic_name__  ,      dropout=__magic_name__  ,      norm_num_groups=__magic_name__  ,      cross_attention_dim=__magic_name__  ,      attention_bias=__magic_name__  ,      sample_size=__magic_name__  ,      num_vector_embeds=__magic_name__  ,      activation_fn=__magic_name__  ,      num_embeds_ada_norm=__magic_name__  ,      )
					        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					:      List[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					:      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])`
					__snake_case					:      int							      =     [1, 0]

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   int=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   bool = True  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     hidden_states

					__snake_case					:      int							      =     []
					__snake_case					:      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					:      Tuple							      =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
								__snake_case					:      Tuple							      =     self.transformer_index_for_condition[i]
								__snake_case					:      Optional[Any]							      =     self.transformers[transformer_index](
								    __magic_name__  ,      encoder_hidden_states=__magic_name__  ,      timestep=__magic_name__  ,      cross_attention_kwargs=__magic_name__  ,      return_dict=__magic_name__  ,      )[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[str]							      =     output_states + input_states

					if not return_dict:
								return (output_states,)

					return TransformeraDModelOutput(sample=__magic_name__			)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


UpperCAmelCase_           =				'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
  import torch

  print('''Torch version:''', torch.__version__)
  print('''Cuda available:''', torch.cuda.is_available())
  print('''Cuda version:''', torch.version.cuda)
  print('''CuDNN version:''', torch.backends.cudnn.version())
  print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
  print('''Torch version:''', None)

try:
  import transformers

  print('''transformers version:''', transformers.__version__)
except ImportError:
  print('''transformers version:''', None)