





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
		import torch

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		['''image_processor''', '''tokenizer''']
	__UpperCamelCase        =		'''OneFormerImageProcessor'''
	__UpperCamelCase        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')




	def __init__(	self       :    str				,			__lowerCamelCase       :    List[str]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    int = 77				,			__lowerCamelCase       :    int = 77				,			**__lowerCamelCase       :    Union[str, Any]      )					->   Union[str, Any]:

			'''simple docstring'''




			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.'      )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.'      )

			__lowercase					=    max_seq_length
			__lowercase					=    task_seq_length

			super().__init__(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[Any]=None				,			__lowerCamelCase       :    str=77      )					->   Union[str, Any]:

			'''simple docstring'''




			if text_list is None:
					raise ValueError('tokens cannot be None.'      )

			__lowercase					=    self.tokenizer(__lowerCamelCase				,			padding='max_length'				,			max_length=__lowerCamelCase				,			truncation=__lowerCamelCase      )

			__lowercase	,			__lowercase					=    tokens['attention_mask'], tokens['input_ids']

			__lowercase					=    []
			for attn_mask, input_id in zip(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    torch.tensor(__lowerCamelCase      ) * torch.tensor(__lowerCamelCase      )
					token_inputs.append(token.unsqueeze(0      )      )

			__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )
			return token_inputs




	def __call__(	self       :    int				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    Dict=None				,			**__lowerCamelCase       :    List[str]      )					->   List[Any]:

			'''simple docstring'''




			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.'      )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.'      )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs      ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.'      )

			__lowercase					=    self.image_processor(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      )

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    [task_inputs]

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ) and all(isinstance(__lowerCamelCase				,			__lowerCamelCase      ) for task_input in task_inputs      ):
					__lowercase					=    []
					for task in task_inputs:
							__lowercase					=    F"the task is {task}"
							task_token_inputs.append(__lowerCamelCase      )
					__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.task_seq_length      )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.'      )

			if hasattr(__lowerCamelCase				,			'text_inputs'      ):
					__lowercase					=    encoded_inputs.text_inputs

					__lowercase					=    []
					for texts in texts_list:
							__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.max_seq_length      )
							text_inputs.append(text_input_list.unsqueeze(0      )      )

					__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )

			return encoded_inputs




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    List[Any]=None				,			**__lowerCamelCase       :    Optional[int]      )					->   Union[str, Any]:

			'''simple docstring'''




			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.'      )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.'      )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs      ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.'      )

			__lowercase					=    self.image_processor.encode_inputs(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      )

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    [task_inputs]

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ) and all(isinstance(__lowerCamelCase				,			__lowerCamelCase      ) for task_input in task_inputs      ):
					__lowercase					=    []
					for task in task_inputs:
							__lowercase					=    F"the task is {task}"
							task_token_inputs.append(__lowerCamelCase      )
					__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.task_seq_length      )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.'      )

			if hasattr(__lowerCamelCase				,			'text_inputs'      ):
					__lowercase					=    encoded_inputs.text_inputs

					__lowercase					=    []
					for texts in texts_list:
							__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.max_seq_length      )
							text_inputs.append(text_input_list.unsqueeze(0      )      )

					__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )

			return encoded_inputs




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			*__lowerCamelCase       :    Any				,			**__lowerCamelCase       :    List[Any]      )					->   Any:

			'''simple docstring'''




			return self.image_processor.post_process_semantic_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]				,			*__lowerCamelCase       :    Optional[int]				,			**__lowerCamelCase       :    str      )					->   Tuple:

			'''simple docstring'''




			return self.image_processor.post_process_instance_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Optional[Any]				,			*__lowerCamelCase       :    Tuple				,			**__lowerCamelCase       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			return self.image_processor.post_process_panoptic_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )











import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




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

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

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




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

			'''simple docstring'''




			return (4, 14, 16)




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

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    'block_out_channels': (32, 64, 128, 256),
			    'in_channels': 14,
			    'out_channels': 14,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1.0,
			    'out_block_type': 'OutConv1DBlock',
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    model(**self.dummy_input      )

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




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'				,			subfolder='unet'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    model.config.in_channels
			__lowercase					=    16
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

			__lowercase					=    output.abs().sum()
			__lowercase					=    output.abs().max()

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




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

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

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




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

			'''simple docstring'''




			return (4, 14, 16)




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

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

			self.assertIsNotNone(__lowerCamelCase      )
			__lowercase					=    torch.Size((inputs_dict['sample'].shape[0], 1)      )
			self.assertEqual(output.shape				,			__lowerCamelCase				,			'Input and output shapes do not match'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




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

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'in_channels': 14,
			    'out_channels': 14,
			    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
			    'up_block_types': [],
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': [32, 64, 128, 256],
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'use_timestep_embedding': True,
			    'freq_shift': 1.0,
			    'flip_sin_to_cos': False,
			    'time_embedding_type': 'positional',
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    value_function(**self.dummy_input      )

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




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    value_function.config.in_channels
			__lowercase					=    14
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




