



'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers.generation import (
						    MaxLengthCriteria,
						    MaxNewTokensCriteria,
						    MaxTimeCriteria,
						    StoppingCriteriaList,
						    validate_stopping_criteria,
						)







@require_torch
class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self	,				lowercase							):
				A_     :						List[Any]     =					3
				A_     :						Any     =					250

				A_     :						Optional[int]     =					ids_tensor((batch_size, length)	,				lowercase							)
				A_     :						Any     =					torch.ones((batch_size, length)	,				device=lowercase	,				dtype=torch.float							) / length
				return input_ids, scores


	def    _a     (self							):
				A_,   A_     :						Union[str, Any]     =					self._get_tensors(5							)

				A_     :						Union[str, Any]     =					StoppingCriteriaList(
				    [
				        MaxLengthCriteria(max_length=10							),
				        MaxTimeCriteria(max_time=0.1							),
				    ]							)

				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						Union[str, Any]     =					self._get_tensors(9							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						Dict     =					self._get_tensors(10							)
				self.assertTrue(criteria(lowercase	,				lowercase							)							)


	def    _a     (self							):
				A_     :						Any     =					MaxLengthCriteria(max_length=10							)

				A_,   A_     :						Optional[Any]     =					self._get_tensors(5							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						int     =					self._get_tensors(9							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						Any     =					self._get_tensors(10							)
				self.assertTrue(criteria(lowercase	,				lowercase							)							)


	def    _a     (self							):
				A_     :						int     =					MaxNewTokensCriteria(start_length=5	,				max_new_tokens=5							)

				A_,   A_     :						str     =					self._get_tensors(5							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						Union[str, Any]     =					self._get_tensors(9							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_,   A_     :						str     =					self._get_tensors(10							)
				self.assertTrue(criteria(lowercase	,				lowercase							)							)

				A_     :						List[str]     =					StoppingCriteriaList([criteria]							)
				self.assertEqual(criteria_list.max_length	,				10							)


	def    _a     (self							):
				A_,   A_     :						Dict     =					self._get_tensors(5							)

				A_     :						List[Any]     =					MaxTimeCriteria(max_time=0.1							)
				self.assertFalse(criteria(lowercase	,				lowercase							)							)

				A_     :						Dict     =					MaxTimeCriteria(max_time=0.1	,				initial_timestamp=time.time() - 0.2							)
				self.assertTrue(criteria(lowercase	,				lowercase							)							)


	def    _a     (self							):
				validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10							)]							)	,				10							)

				with self.assertWarns(lowercase							):
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10							)]							)	,				11							)

				A_     :						str     =					validate_stopping_criteria(StoppingCriteriaList()	,				11							)

				self.assertEqual(len(lowercase							)	,				1							)




'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()