





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__A     =    logging.get_logger(__name__)

enable_full_determinism()



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







	'''simple docstring'''
	lowercase_		=      UNetaDModel
	lowercase_		=      "sample"

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Any:



			'''simple docstring'''



			lowerCamelCase__:				Optional[int]        =4
			lowerCamelCase__:				Optional[Any]        =3
			lowerCamelCase__:				Any        =(32, 32)

			lowerCamelCase__:				str        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
			lowerCamelCase__:				Optional[int]        =torch.tensor([10]).to(UpperCAmelCase_)

			return {"sample": noise, "timestep": time_step}

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



			'''simple docstring'''



			return (3, 32, 32)

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[Any]:



			'''simple docstring'''



			return (3, 32, 32)







	def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Union[str, Any]:



			'''simple docstring'''



			lowerCamelCase__:				List[Any]        ={
			    "block_out_channels": (32, 64),
			    "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
			    "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
			    "attention_head_dim": 3,
			    "out_channels": 3,
			    "in_channels": 3,
			    "layers_per_block": 2,
			    "sample_size": 32,
			}
			lowerCamelCase__:				Dict        =self.dummy_input
			return init_dict, inputs_dict



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







	'''simple docstring'''
	lowercase_		=      UNetaDModel
	lowercase_		=      "sample"

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Dict:



			'''simple docstring'''



			lowerCamelCase__:				List[str]        =4
			lowerCamelCase__:				Optional[Any]        =4
			lowerCamelCase__:				Union[str, Any]        =(32, 32)

			lowerCamelCase__:				int        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
			lowerCamelCase__:				List[str]        =torch.tensor([10]).to(UpperCAmelCase_)

			return {"sample": noise, "timestep": time_step}

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->str:



			'''simple docstring'''



			return (4, 32, 32)

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->List[str]:



			'''simple docstring'''



			return (4, 32, 32)

	def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->str:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        ={
			    "sample_size": 32,
			    "in_channels": 4,
			    "out_channels": 4,
			    "layers_per_block": 2,
			    "block_out_channels": (32, 64),
			    "attention_head_dim": 32,
			    "down_block_types": ("DownBlock2D", "DownBlock2D"),
			    "up_block_types": ("UpBlock2D", "UpBlock2D"),
			}
			lowerCamelCase__:				int        =self.dummy_input
			return init_dict, inputs_dict

	def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)

			self.assertIsNotNone(UpperCAmelCase_)
			self.assertEqual(len(loading_info["missing_keys"])	,			0)

			model.to(UpperCAmelCase_)
			lowerCamelCase__:				int        =model(**self.dummy_input).sample

			assert image is not None, "Make sure output is not None"

	@unittest.skipIf(torch_device != "cuda"	,			"This test is supposed to run on GPU")
	def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



			'''simple docstring'''



			lowerCamelCase__:				List[Any]        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			lowerCamelCase__:				Tuple        =model(**self.dummy_input).sample

			assert image is not None, "Make sure output is not None"

	@unittest.skipIf(torch_device != "cuda"	,			"This test is supposed to run on GPU")
	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[str]:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)
			model_accelerate.to(UpperCAmelCase_)
			model_accelerate.eval()

			lowerCamelCase__:				List[Any]        =torch.randn(
			    1	,			model_accelerate.config.in_channels	,			model_accelerate.config.sample_size	,			model_accelerate.config.sample_size	,			generator=torch.manual_seed(0)	,			)
			lowerCamelCase__:				List[str]        =noise.to(UpperCAmelCase_)
			lowerCamelCase__:				Dict        =torch.tensor([10] * noise.shape[0]).to(UpperCAmelCase_)

			lowerCamelCase__:				List[str]        =model_accelerate(UpperCAmelCase_	,			UpperCAmelCase_)["sample"]

			# two models don't need to stay in the device at the same time
			del model_accelerate
			torch.cuda.empty_cache()
			gc.collect()

			lowerCamelCase__:				Optional[int]        =UNetaDModel.from_pretrained(
			    "fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_	,			low_cpu_mem_usage=UpperCAmelCase_)
			model_normal_load.to(UpperCAmelCase_)
			model_normal_load.eval()
			lowerCamelCase__:				Union[str, Any]        =model_normal_load(UpperCAmelCase_	,			UpperCAmelCase_)["sample"]

			assert torch_all_close(UpperCAmelCase_	,			UpperCAmelCase_	,			rtol=1E-3)







	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Any        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
			model.eval()
			model.to(UpperCAmelCase_)

			lowerCamelCase__:				Union[str, Any]        =torch.randn(
			    1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	,			generator=torch.manual_seed(0)	,			)
			lowerCamelCase__:				int        =noise.to(UpperCAmelCase_)
			lowerCamelCase__:				Dict        =torch.tensor([10] * noise.shape[0]).to(UpperCAmelCase_)

			with torch.no_grad():
					lowerCamelCase__:				int        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

			lowerCamelCase__:				List[str]        =output[0, -1, -3:, -3:].flatten().cpu()
			# fmt: off
			lowerCamelCase__:				List[str]        =torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
			# fmt: on

			self.assertTrue(torch_all_close(UpperCAmelCase_	,			UpperCAmelCase_	,			rtol=1E-3))



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







	'''simple docstring'''
	lowercase_		=      UNetaDModel
	lowercase_		=      "sample"

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       Any=(32, 32))	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				int        =4
			lowerCamelCase__:				str        =3

			lowerCamelCase__:				Dict        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
			lowerCamelCase__:				Tuple        =torch.tensor(batch_size * [10]).to(dtype=torch.intaa	,			device=UpperCAmelCase_)

			return {"sample": noise, "timestep": time_step}

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->List[Any]:



			'''simple docstring'''



			return (3, 32, 32)

	@property
	def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Optional[Any]:



			'''simple docstring'''



			return (3, 32, 32)

	def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[int]:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        ={
			    "block_out_channels": [32, 64, 64, 64],
			    "in_channels": 3,
			    "layers_per_block": 1,
			    "out_channels": 3,
			    "time_embedding_type": "fourier",
			    "norm_eps": 1E-6,
			    "mid_block_scale_factor": math.sqrt(2.0),
			    "norm_num_groups": None,
			    "down_block_types": [
			        "SkipDownBlock2D",
			        "AttnSkipDownBlock2D",
			        "SkipDownBlock2D",
			        "SkipDownBlock2D",
			    ],
			    "up_block_types": [
			        "SkipUpBlock2D",
			        "SkipUpBlock2D",
			        "AttnSkipUpBlock2D",
			        "SkipUpBlock2D",
			    ],
			}
			lowerCamelCase__:				str        =self.dummy_input
			return init_dict, inputs_dict

	@slow
	def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"	,			output_loading_info=UpperCAmelCase_)
			self.assertIsNotNone(UpperCAmelCase_)
			self.assertEqual(len(loading_info["missing_keys"])	,			0)

			model.to(UpperCAmelCase_)
			lowerCamelCase__:				Tuple        =self.dummy_input
			lowerCamelCase__:				Union[str, Any]        =floats_tensor((4, 3) + (256, 256)).to(UpperCAmelCase_)
			lowerCamelCase__:				List[Any]        =noise
			lowerCamelCase__:				Dict        =model(**UpperCAmelCase_)

			assert image is not None, "Make sure output is not None"

	@slow
	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Dict:



			'''simple docstring'''



			lowerCamelCase__:				Any        =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
			model.to(UpperCAmelCase_)

			lowerCamelCase__:				Dict        =4
			lowerCamelCase__:				Optional[Any]        =3
			lowerCamelCase__:				str        =(256, 256)

			lowerCamelCase__:				Dict        =torch.ones((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
			lowerCamelCase__:				List[Any]        =torch.tensor(batch_size * [1E-4]).to(UpperCAmelCase_)

			with torch.no_grad():
					lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

			lowerCamelCase__:				str        =output[0, -3:, -3:, -1].flatten().cpu()
			# fmt: off
			lowerCamelCase__:				Dict        =torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10_980.7_129, -20_028.8_535, 8148.2822, 2342.2905, 567.7608])
			# fmt: on

			self.assertTrue(torch_all_close(UpperCAmelCase_	,			UpperCAmelCase_	,			rtol=1E-2))

	def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Union[str, Any]:



			'''simple docstring'''



			lowerCamelCase__:				List[str]        =UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
			model.to(UpperCAmelCase_)

			lowerCamelCase__:				Optional[int]        =4
			lowerCamelCase__:				Optional[int]        =3
			lowerCamelCase__:				Optional[Any]        =(32, 32)

			lowerCamelCase__:				int        =torch.ones((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
			lowerCamelCase__:				Optional[int]        =torch.tensor(batch_size * [1E-4]).to(UpperCAmelCase_)

			with torch.no_grad():
					lowerCamelCase__:				int        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

			lowerCamelCase__:				Optional[int]        =output[0, -3:, -3:, -1].flatten().cpu()
			# fmt: off
			lowerCamelCase__:				Dict        =torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
			# fmt: on

			self.assertTrue(torch_all_close(UpperCAmelCase_	,			UpperCAmelCase_	,			rtol=1E-2))







	def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



			'''simple docstring'''



			pass








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


__A     =    ["text", "image", "audio"]





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =[]

  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(__a     ,   __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

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

  return output_types






@is_tool_test
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:				Tuple        =self.tool.inputs
   for _input in inputs:
     if isinstance(_input	,			UpperCAmelCase_):
       for __input in _input:
         self.assertTrue(__input in authorized_types)
     else:
       self.assertTrue(_input in authorized_types)

   lowerCamelCase__:				Optional[Any]        =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				str        =self.tool(*UpperCAmelCase_)

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

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				str        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				Dict        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =[outputs]

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

   for output, output_type in zip(UpperCAmelCase_	,			self.tool.outputs):
     lowerCamelCase__:				Any        =AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(UpperCAmelCase_	,			UpperCAmelCase_))







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =create_inputs(self.tool.inputs)

   lowerCamelCase__:				int        =[]

   for _input, input_type in zip(UpperCAmelCase_	,			self.tool.inputs):
     if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       _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
   lowerCamelCase__:				Union[str, Any]        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				str        =[outputs]

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

