'''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 get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  torch.FloatTensor

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      @register_to_config
      def __init__(       self						:				Union[str, Any]						,					A						:				int = 65536						,					A						:				Optional[int] = None						,					A						:				int = 2						,					A						:				int = 2						,					A						:				int = 0						,					A						:				str = "fourier"						,					A						:				bool = True						,					A						:				bool = False						,					A						:				float = 0.0						,					A						:				Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,					A						:				Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,					A						:				Tuple[str] = "UNetMidBlock1D"						,					A						:				str = None						,					A						:				Tuple[int] = (32, 32, 64)						,					A						:				str = None						,					A						:				int = 8						,					A						:				int = 1						,					A						:				bool = False						,					):
             super().__init__()
             _UpperCAmelCase     :  List[str]							      =    sample_size

             # time
             if time_embedding_type == "fourier":
                    _UpperCAmelCase     :  List[str]							      =    GaussianFourierProjection(
                        embedding_size=8						,					set_W_to_weight=A						,					log=A						,					flip_sin_to_cos=A       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    2 * block_out_channels[0]
             elif time_embedding_type == "positional":
                    _UpperCAmelCase     :  Dict							      =    Timesteps(
                        block_out_channels[0]						,					flip_sin_to_cos=A						,					downscale_freq_shift=A       )
                    _UpperCAmelCase     :  str							      =    block_out_channels[0]

             if use_timestep_embedding:
                    _UpperCAmelCase     :  List[str]							      =    block_out_channels[0] * 4
                    _UpperCAmelCase     :  List[str]							      =    TimestepEmbedding(
                        in_channels=A						,					time_embed_dim=A						,					act_fn=A						,					out_dim=block_out_channels[0]						,					)

             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList([]       )
             _UpperCAmelCase     :  Tuple							      =    None
             _UpperCAmelCase     :  Optional[Any]							      =    nn.ModuleList([]       )
             _UpperCAmelCase     :  str							      =    None

             # down
             _UpperCAmelCase     :  Optional[int]							      =    in_channels
             for i, down_block_type in enumerate(A       ):
                    _UpperCAmelCase     :  Union[str, Any]							      =    output_channel
                    _UpperCAmelCase     :  Tuple							      =    block_out_channels[i]

                    if i == 0:
                           input_channel += extra_in_channels

                    _UpperCAmelCase     :  str							      =    i == len(A       ) - 1

                    _UpperCAmelCase     :  Union[str, Any]							      =    get_down_block(
                        A						,					num_layers=A						,					in_channels=A						,					out_channels=A						,					temb_channels=block_out_channels[0]						,					add_downsample=not is_final_block or downsample_each_block						,					)
                    self.down_blocks.append(A       )

             # mid
             _UpperCAmelCase     :  Optional[int]							      =    get_mid_block(
                 A						,					in_channels=block_out_channels[-1]						,					mid_channels=block_out_channels[-1]						,					out_channels=block_out_channels[-1]						,					embed_dim=block_out_channels[0]						,					num_layers=A						,					add_downsample=A						,					)

             # up
             _UpperCAmelCase     :  int							      =    list(reversed(A       )       )
             _UpperCAmelCase     :  Union[str, Any]							      =    reversed_block_out_channels[0]
             if out_block_type is None:
                    _UpperCAmelCase     :  Optional[Any]							      =    out_channels
             else:
                    _UpperCAmelCase     :  Any							      =    block_out_channels[0]

             for i, up_block_type in enumerate(A       ):
                    _UpperCAmelCase     :  Dict							      =    output_channel
                    _UpperCAmelCase     :  int							      =    (
                        reversed_block_out_channels[i + 1] if i < len(A       ) - 1 else final_upsample_channels
                    )

                    _UpperCAmelCase     :  Optional[Any]							      =    i == len(A       ) - 1

                    _UpperCAmelCase     :  Optional[Any]							      =    get_up_block(
                        A						,					num_layers=A						,					in_channels=A						,					out_channels=A						,					temb_channels=block_out_channels[0]						,					add_upsample=not is_final_block						,					)
                    self.up_blocks.append(A       )
                    _UpperCAmelCase     :  Tuple							      =    output_channel

             # out
             _UpperCAmelCase     :  Optional[int]							      =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,					32       )
             _UpperCAmelCase     :  List[str]							      =    get_out_block(
                 out_block_type=A						,					num_groups_out=A						,					embed_dim=block_out_channels[0]						,					out_channels=A						,					act_fn=A						,					fc_dim=block_out_channels[-1] // 4						,					)





      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				bool = True						,					):
             _UpperCAmelCase     :  List[Any]							      =    timestep
             if not torch.is_tensor(A       ):
                    _UpperCAmelCase     :  Optional[int]							      =    torch.tensor([timesteps]						,					dtype=torch.long						,					device=sample.device       )
             elif torch.is_tensor(A       ) and len(timesteps.shape       ) == 0:
                    _UpperCAmelCase     :  Tuple							      =    timesteps[None].to(sample.device       )

             _UpperCAmelCase     :  Dict							      =    self.time_proj(A       )
             if self.config.use_timestep_embedding:
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.time_mlp(A       )
             else:
                    _UpperCAmelCase     :  str							      =    timestep_embed[..., None]
                    _UpperCAmelCase     :  Tuple							      =    timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
                    _UpperCAmelCase     :  List[Any]							      =    timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

             # 2. down
             _UpperCAmelCase     :  Dict							      =    ()
             for downsample_block in self.down_blocks:
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    downsample_block(hidden_states=A						,					temb=A       )
                    down_block_res_samples += res_samples

             # 3. mid
             if self.mid_block:
                    _UpperCAmelCase     :  Optional[int]							      =    self.mid_block(A						,					A       )

             # 4. up
             for i, upsample_block in enumerate(self.up_blocks       ):
                    _UpperCAmelCase     :  int							      =    down_block_res_samples[-1:]
                    _UpperCAmelCase     :  Optional[int]							      =    down_block_res_samples[:-1]
                    _UpperCAmelCase     :  Optional[Any]							      =    upsample_block(A						,					res_hidden_states_tuple=A						,					temb=A       )

             # 5. post-process
             if self.out_block:
                    _UpperCAmelCase     :  Optional[Any]							      =    self.out_block(A						,					A       )

             if not return_dict:
                    return (sample,)

             return UNetaDOutput(sample=A       )



'''simple docstring'''







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image


_lowercase :  List[str]    							=       ["text", "image", "audio"]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       []

						for input_type in input_types:
												if input_type == "text":
																		inputs.append('''Text input'''							)
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''							).resize((512, 512)							)							)
												elif input_type == "audio":
																		inputs.append(torch.ones(3000							)							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		inputs.append(create_inputs(__SCREAMING_SNAKE_CASE							)							)
												else:
																		raise ValueError(F'''Invalid type requested: {input_type}'''							)

						return inputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []

						for output in outputs:
												if isinstance(__SCREAMING_SNAKE_CASE      ,      (str, AgentText)							):
																		output_types.append('''text'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (Image.Image, AgentImage)							):
																		output_types.append('''image'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (torch.Tensor, AgentAudio)							):
																		output_types.append('''audio'''							)
												else:
																		raise ValueError(F'''Invalid output: {output}'''							)

						return output_types






@is_tool_test
class  lowerCAmelCase__   :






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''inputs'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''outputs'''		)		)

											lowercase_	:     Optional[Any]													=       self.tool.inputs
											for _input in inputs:
																	if isinstance(_input				,							__SCREAMING_SNAKE_CASE		):
																							for __input in _input:
																													self.assertTrue(__input in authorized_types		)
																	else:
																							self.assertTrue(_input in authorized_types		)

											lowercase_	:     int													=       self.tool.outputs
											for _output in outputs:
																	self.assertTrue(_output in authorized_types		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     Tuple													=       self.tool(*__SCREAMING_SNAKE_CASE		)

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

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''description'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''default_checkpoint'''		)		)
											self.assertTrue(self.tool.description.startswith('''This is a tool that'''		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     int													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[Any]													=       [outputs]

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

											for output, output_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.outputs		):
																	lowercase_	:     Optional[int]													=       AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       create_inputs(self.tool.inputs		)

											lowercase_	:     int													=       []

											for _input, input_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.inputs		):
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							_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
											lowercase_	:     Optional[Any]													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Dict													=       [outputs]

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



