"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 							snake_case			(					A_							):




					"""simple docstring"""

					snake_case__							       =					42






class 							snake_case			(					A_ ,       A_							):




					"""simple docstring"""

					@register_to_config
					def __init__(							self  :      Optional[int]       ,lowerCamelCase__  :      Optional[Union[int, Tuple[int, int]]] = None       ,lowerCamelCase__  :      int = 3       ,lowerCamelCase__  :      int = 3       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      str = "positional"       ,lowerCamelCase__  :      int = 0       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,lowerCamelCase__  :      Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,lowerCamelCase__  :      Tuple[int] = (224, 448, 672, 896)       ,lowerCamelCase__  :      int = 2       ,lowerCamelCase__  :      float = 1       ,lowerCamelCase__  :      int = 1       ,lowerCamelCase__  :      str = "conv"       ,lowerCamelCase__  :      str = "conv"       ,lowerCamelCase__  :      str = "silu"       ,lowerCamelCase__  :      Optional[int] = 8       ,lowerCamelCase__  :      int = 32       ,lowerCamelCase__  :      float = 1e-5       ,lowerCamelCase__  :      str = "default"       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Optional[str] = None       ,lowerCamelCase__  :      Optional[int] = None       ,):
									super().__init__()

									UpperCAmelCase__			 =				sample_size
									UpperCAmelCase__			 =				block_out_channels[0] * 4

									# Check inputs
									if len(_A     ) != len(_A     ):
													raise ValueError(
													    f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''     )

									if len(_A     ) != len(_A     ):
													raise ValueError(
													    f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''     )

									# input
									UpperCAmelCase__			 =				nn.Convad(_A       ,block_out_channels[0]       ,kernel_size=3       ,padding=(1, 1)     )

									# time
									if time_embedding_type == "fourier":
													UpperCAmelCase__			 =				GaussianFourierProjection(embedding_size=block_out_channels[0]       ,scale=16     )
													UpperCAmelCase__			 =				2 * block_out_channels[0]
									elif time_embedding_type == "positional":
													UpperCAmelCase__			 =				Timesteps(block_out_channels[0]       ,_A       ,_A     )
													UpperCAmelCase__			 =				block_out_channels[0]

									UpperCAmelCase__			 =				TimestepEmbedding(_A       ,_A     )

									# class embedding
									if class_embed_type is None and num_class_embeds is not None:
													UpperCAmelCase__			 =				nn.Embedding(_A       ,_A     )
									elif class_embed_type == "timestep":
													UpperCAmelCase__			 =				TimestepEmbedding(_A       ,_A     )
									elif class_embed_type == "identity":
													UpperCAmelCase__			 =				nn.Identity(_A       ,_A     )
									else:
													UpperCAmelCase__			 =				None

									UpperCAmelCase__			 =				nn.ModuleList([]     )
									UpperCAmelCase__			 =				None
									UpperCAmelCase__			 =				nn.ModuleList([]     )

									# down
									UpperCAmelCase__			 =				block_out_channels[0]
									for i, down_block_type in enumerate(_A     ):
													UpperCAmelCase__			 =				output_channel
													UpperCAmelCase__			 =				block_out_channels[i]
													UpperCAmelCase__			 =				i == len(_A     ) - 1

													UpperCAmelCase__			 =				get_down_block(
													    _A       ,num_layers=_A       ,in_channels=_A       ,out_channels=_A       ,temb_channels=_A       ,add_downsample=not is_final_block       ,resnet_eps=_A       ,resnet_act_fn=_A       ,resnet_groups=_A       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,downsample_padding=_A       ,resnet_time_scale_shift=_A       ,downsample_type=_A       ,)
													self.down_blocks.append(_A     )

									# mid
									UpperCAmelCase__			 =				UNetMidBlockaD(
									    in_channels=block_out_channels[-1]       ,temb_channels=_A       ,resnet_eps=_A       ,resnet_act_fn=_A       ,output_scale_factor=_A       ,resnet_time_scale_shift=_A       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,resnet_groups=_A       ,add_attention=_A       ,)

									# up
									UpperCAmelCase__			 =				list(reversed(_A     )     )
									UpperCAmelCase__			 =				reversed_block_out_channels[0]
									for i, up_block_type in enumerate(_A     ):
													UpperCAmelCase__			 =				output_channel
													UpperCAmelCase__			 =				reversed_block_out_channels[i]
													UpperCAmelCase__			 =				reversed_block_out_channels[min(i + 1       ,len(_A     ) - 1     )]

													UpperCAmelCase__			 =				i == len(_A     ) - 1

													UpperCAmelCase__			 =				get_up_block(
													    _A       ,num_layers=layers_per_block + 1       ,in_channels=_A       ,out_channels=_A       ,prev_output_channel=_A       ,temb_channels=_A       ,add_upsample=not is_final_block       ,resnet_eps=_A       ,resnet_act_fn=_A       ,resnet_groups=_A       ,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,resnet_time_scale_shift=_A       ,upsample_type=_A       ,)
													self.up_blocks.append(_A     )
													UpperCAmelCase__			 =				output_channel

									# out
									UpperCAmelCase__			 =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,32     )
									UpperCAmelCase__			 =				nn.GroupNorm(num_channels=block_out_channels[0]       ,num_groups=_A       ,eps=_A     )
									UpperCAmelCase__			 =				nn.SiLU()
									UpperCAmelCase__			 =				nn.Convad(block_out_channels[0]       ,_A       ,kernel_size=3       ,padding=1     )







					def 						__lowerCAmelCase		(							self  :      Dict       ,lowerCamelCase__  :      torch.FloatTensor       ,lowerCamelCase__  :      Union[torch.Tensor, float, int]       ,lowerCamelCase__  :      Optional[torch.Tensor] = None       ,lowerCamelCase__  :      bool = True       ,):
									# 0. center input if necessary
									if self.config.center_input_sample:
													UpperCAmelCase__			 =				2 * sample - 1.0

									# 1. time
									UpperCAmelCase__			 =				timestep
									if not torch.is_tensor(_A     ):
													UpperCAmelCase__			 =				torch.tensor([timesteps]       ,dtype=torch.long       ,device=sample.device     )
									elif torch.is_tensor(_A     ) and len(timesteps.shape     ) == 0:
													UpperCAmelCase__			 =				timesteps[None].to(sample.device     )

									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									UpperCAmelCase__			 =				timesteps * torch.ones(sample.shape[0]       ,dtype=timesteps.dtype       ,device=timesteps.device     )

									UpperCAmelCase__			 =				self.time_proj(_A     )

									# timesteps does not contain any weights and will always return f32 tensors
									# but time_embedding might actually be running in fp16. so we need to cast here.
									# there might be better ways to encapsulate this.
									UpperCAmelCase__			 =				t_emb.to(dtype=self.dtype     )
									UpperCAmelCase__			 =				self.time_embedding(_A     )

									if self.class_embedding is not None:
													if class_labels is None:
																	raise ValueError('class_labels should be provided when doing class conditioning'     )

													if self.config.class_embed_type == "timestep":
																	UpperCAmelCase__			 =				self.time_proj(_A     )

													UpperCAmelCase__			 =				self.class_embedding(_A     ).to(dtype=self.dtype     )
													UpperCAmelCase__			 =				emb + class_emb

									# 2. pre-process
									UpperCAmelCase__			 =				sample
									UpperCAmelCase__			 =				self.conv_in(_A     )

									# 3. down
									UpperCAmelCase__			 =				(sample,)
									for downsample_block in self.down_blocks:
													if hasattr(_A       ,'skip_conv'     ):
																	UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				downsample_block(
																	    hidden_states=_A       ,temb=_A       ,skip_sample=_A     )
													else:
																	UpperCAmelCase__ ,      UpperCAmelCase__			 =				downsample_block(hidden_states=_A       ,temb=_A     )

													down_block_res_samples += res_samples

									# 4. mid
									UpperCAmelCase__			 =				self.mid_block(_A       ,_A     )

									# 5. up
									UpperCAmelCase__			 =				None
									for upsample_block in self.up_blocks:
													UpperCAmelCase__			 =				down_block_res_samples[-len(upsample_block.resnets     ) :]
													UpperCAmelCase__			 =				down_block_res_samples[: -len(upsample_block.resnets     )]

													if hasattr(_A       ,'skip_conv'     ):
																	UpperCAmelCase__ ,      UpperCAmelCase__			 =				upsample_block(_A       ,_A       ,_A       ,_A     )
													else:
																	UpperCAmelCase__			 =				upsample_block(_A       ,_A       ,_A     )

        # 6. post-process
									UpperCAmelCase__			 =				self.conv_norm_out(_A     )
									UpperCAmelCase__			 =				self.conv_act(_A     )
									UpperCAmelCase__			 =				self.conv_out(_A     )

									if skip_sample is not None:
													sample += skip_sample

									if self.config.time_embedding_type == "fourier":
													UpperCAmelCase__			 =				timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]     )))     )
													UpperCAmelCase__			 =				sample / timesteps

									if not return_dict:
													return (sample,)

									return UNetaDOutput(sample=_A     )





import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types







class 	lowerCamelCase						(    unittest.TestCase				,					A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		int:
         snake_case						=   load_tool("translation"  )
         self.tool.setup()
         snake_case						=   load_tool("translation"							,       remote=_A  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[int]:
         snake_case						=   self.tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		int:
         snake_case						=   self.remote_tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   self.tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		str:
         snake_case						=   self.remote_tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Any  )		->		Tuple:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         # There is a single output
         if len(self.tool.outputs  ) == 1:
             snake_case						=   [outputs]

         self.assertListEqual(output_types(_A  )							,       self.tool.outputs  )





     def 						UpperCAmelCase(self :       List[str]  )		->		List[Any]:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

         for output, output_type in zip(_A							,       self.tool.outputs  ):
             snake_case						=   AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(_A							,       _A  )  )





     def 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]

         snake_case						=   []

         for _input, input_type in zip(_A							,       self.tool.inputs  ):
             if isinstance(_A							,       _A  ):
                 _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input  ) for _input_type in input_type]  )
             else:
                 _inputs.append(AGENT_TYPE_MAPPING[input_type](_input  )  )

        # Should not raise an error
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

