
"""simple docstring"""







from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin







@dataclass
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):





					@register_to_config
					def __init__(				self   :							Any							,						snake_case_   :							int = 16							,						snake_case_   :							int = 88							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							int = 1							,						snake_case_   :							float = 0.0							,						snake_case_   :							int = 32							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							bool = False							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							str = "geglu"							,						snake_case_   :							bool = True							,						snake_case_   :							bool = True							,						):
												super().__init__()
												__snake_case     			=       num_attention_heads
												__snake_case     			=       attention_head_dim
												__snake_case     			=       num_attention_heads * attention_head_dim

												__snake_case     			=       in_channels

												__snake_case     			=       torch.nn.GroupNorm(num_groups=snake_case_							,						num_channels=snake_case_							,						eps=1e-6							,						affine=snake_case_    )
												__snake_case     			=       nn.Linear(snake_case_							,						snake_case_    )

												# 3. Define transformers blocks
												__snake_case     			=       nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            snake_case_							,						snake_case_							,						snake_case_							,						dropout=snake_case_							,						cross_attention_dim=snake_case_							,						activation_fn=snake_case_							,						attention_bias=snake_case_							,						double_self_attention=snake_case_							,						norm_elementwise_affine=snake_case_							,						)
												        for d in range(snake_case_    )
												    ]    )

												__snake_case     			=       nn.Linear(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							List[str]							,						snake_case_   :							Tuple=None							,						snake_case_   :							int=None							,						snake_case_   :							List[Any]=None							,						snake_case_   :							Optional[int]=1							,						snake_case_   :							str=None							,						snake_case_   :							bool = True							,						):
												__snake_case			,   __snake_case			,   __snake_case			,   __snake_case     			=       hidden_states.shape
												__snake_case     			=       batch_frames // num_frames

												__snake_case     			=       hidden_states

												__snake_case     			=       hidden_states[None, :].reshape(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )
												__snake_case     			=       hidden_states.permute(0							,						2							,						1							,						3							,						4    )

												__snake_case     			=       self.norm(snake_case_    )
												__snake_case     			=       hidden_states.permute(0							,						3							,						4							,						2							,						1    ).reshape(batch_size * height * width							,						snake_case_							,						snake_case_    )

												__snake_case     			=       self.proj_in(snake_case_    )

												# 2. Blocks
												for block in self.transformer_blocks:
																			__snake_case     			=       block(
																			    snake_case_							,						encoder_hidden_states=snake_case_							,						timestep=snake_case_							,						cross_attention_kwargs=snake_case_							,						class_labels=snake_case_							,						)

												# 3. Output
												__snake_case     			=       self.proj_out(snake_case_    )
												__snake_case     			=       (
												    hidden_states[None, None, :]
												    .reshape(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )
												    .permute(0							,						3							,						4							,						1							,						2    )
												    .contiguous()
												)
												__snake_case     			=       hidden_states.reshape(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

												__snake_case     			=       hidden_states + residual

												if not return_dict:
																			return (output,)

												return TransformerTemporalModelOutput(sample=snake_case_    )


"""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


_SCREAMING_SNAKE_CASE	       =      ["""text""", """image""", """audio"""]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							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((5_12, 5_12) ) )
														elif input_type == "audio":
																					inputs.append(torch.ones(30_00 ) )
														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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							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 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												self.assertTrue(hasattr(self.tool							,						"inputs"    )    )
												self.assertTrue(hasattr(self.tool							,						"outputs"    )    )

												__snake_case     			=       self.tool.inputs
												for _input in inputs:
																			if isinstance(_input							,						snake_case_    ):
																										for __input in _input:
																																	self.assertTrue(__input in authorized_types    )
																			else:
																										self.assertTrue(_input in authorized_types    )

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





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												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 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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

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





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )

												__snake_case     			=       []

												for _input, input_type in zip(snake_case_							,						self.tool.inputs    ):
																			if isinstance(snake_case_							,						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
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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