




"""simple docstring"""




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 					__magic_name__							:






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

							torch.manual_seed(0				)
							lowerCamelCase       =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDConditionModel(
							    sample_size=32	,				layers_per_block=1	,				block_out_channels=[32, 64]	,				down_block_types=[
							        """ResnetDownsampleBlock2D""",
							        """SimpleCrossAttnDownBlock2D""",
							    ]	,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""	,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]	,				in_channels=3	,				out_channels=6	,				cross_attention_dim=32	,				encoder_hid_dim=32	,				attention_head_dim=8	,				addition_embed_type="""text"""	,				addition_embed_type_num_heads=2	,				cross_attention_norm="""group_norm"""	,				resnet_time_scale_shift="""scale_shift"""	,				act_fn="""gelu"""	,				)
							unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

							torch.manual_seed(0				)
							lowerCamelCase       =      DDPMScheduler(
							    num_train_timesteps=1_000	,				beta_schedule="""squaredcos_cap_v2"""	,				beta_start=0.0_001	,				beta_end=0.02	,				thresholding=__UpperCamelCase	,				dynamic_thresholding_ratio=0.95	,				sample_max_value=1.0	,				prediction_type="""epsilon"""	,				variance_type="""learned_range"""	,				)

							torch.manual_seed(0				)
							lowerCamelCase       =      IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

							torch.manual_seed(0				)
							lowerCamelCase       =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDConditionModel(
							    sample_size=32	,				layers_per_block=[1, 2]	,				block_out_channels=[32, 64]	,				down_block_types=[
							        """ResnetDownsampleBlock2D""",
							        """SimpleCrossAttnDownBlock2D""",
							    ]	,				mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""	,				up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]	,				in_channels=6	,				out_channels=6	,				cross_attention_dim=32	,				encoder_hid_dim=32	,				attention_head_dim=8	,				addition_embed_type="""text"""	,				addition_embed_type_num_heads=2	,				cross_attention_norm="""group_norm"""	,				resnet_time_scale_shift="""scale_shift"""	,				act_fn="""gelu"""	,				class_embed_type="""timestep"""	,				mid_block_scale_factor=1.414	,				time_embedding_act_fn="""gelu"""	,				time_embedding_dim=32	,				)
							unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

							torch.manual_seed(0				)
							lowerCamelCase       =      DDPMScheduler(
							    num_train_timesteps=1_000	,				beta_schedule="""squaredcos_cap_v2"""	,				beta_start=0.0_001	,				beta_end=0.02	,				thresholding=__UpperCamelCase	,				dynamic_thresholding_ratio=0.95	,				sample_max_value=1.0	,				prediction_type="""epsilon"""	,				variance_type="""learned_range"""	,				)

							torch.manual_seed(0				)
							lowerCamelCase       =      DDPMScheduler(
							    num_train_timesteps=1_000	,				beta_schedule="""squaredcos_cap_v2"""	,				beta_start=0.0_001	,				beta_end=0.02	,				)

							torch.manual_seed(0				)
							lowerCamelCase       =      IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_dummy_components()
							lowerCamelCase       =      self.pipeline_class(**__UpperCamelCase				)
							pipe.to(__UpperCamelCase				)
							pipe.set_progress_bar_config(disable=__UpperCamelCase				)

							lowerCamelCase       =      self.get_dummy_inputs(__UpperCamelCase				)

							lowerCamelCase       =      inputs["""prompt"""]
							lowerCamelCase       =      inputs["""generator"""]
							lowerCamelCase       =      inputs["""num_inference_steps"""]
							lowerCamelCase       =      inputs["""output_type"""]

							if "image" in inputs:
										lowerCamelCase       =      inputs["""image"""]
							else:
										lowerCamelCase       =      None

							if "mask_image" in inputs:
										lowerCamelCase       =      inputs["""mask_image"""]
							else:
										lowerCamelCase       =      None

							if "original_image" in inputs:
										lowerCamelCase       =      inputs["""original_image"""]
							else:
										lowerCamelCase       =      None

							lowerCamelCase   ,     lowerCamelCase       =      pipe.encode_prompt(__UpperCamelCase				)

							# inputs with prompt converted to embeddings
							lowerCamelCase       =      {
							    """prompt_embeds""": prompt_embeds,
							    """negative_prompt_embeds""": negative_prompt_embeds,
							    """generator""": generator,
							    """num_inference_steps""": num_inference_steps,
							    """output_type""": output_type,
							}

							if image is not None:
										lowerCamelCase       =      image

							if mask_image is not None:
										lowerCamelCase       =      mask_image

							if original_image is not None:
										lowerCamelCase       =      original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
										setattr(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase				)

							lowerCamelCase       =      pipe(**__UpperCamelCase				)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__UpperCamelCase				)
										lowerCamelCase       =      self.pipeline_class.from_pretrained(__UpperCamelCase				)
										pipe_loaded.to(__UpperCamelCase				)
										pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase				)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(__UpperCamelCase	,				__UpperCamelCase				) is None	,				f'`{optional_component}` did not stay set to None after loading.'	,				)

							lowerCamelCase       =      self.get_dummy_inputs(__UpperCamelCase				)

							lowerCamelCase       =      inputs["""generator"""]
							lowerCamelCase       =      inputs["""num_inference_steps"""]
							lowerCamelCase       =      inputs["""output_type"""]

							# inputs with prompt converted to embeddings
							lowerCamelCase       =      {
							    """prompt_embeds""": prompt_embeds,
							    """negative_prompt_embeds""": negative_prompt_embeds,
							    """generator""": generator,
							    """num_inference_steps""": num_inference_steps,
							    """output_type""": output_type,
							}

							if image is not None:
										lowerCamelCase       =      image

							if mask_image is not None:
										lowerCamelCase       =      mask_image

							if original_image is not None:
										lowerCamelCase       =      original_image

							lowerCamelCase       =      pipe_loaded(**__UpperCamelCase				)[0]

							lowerCamelCase       =      np.abs(to_np(__UpperCamelCase				) - to_np(__UpperCamelCase				)				).max()
							self.assertLess(__UpperCamelCase	,				1e-4				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_dummy_components()
							lowerCamelCase       =      self.pipeline_class(**__UpperCamelCase				)
							pipe.to(__UpperCamelCase				)
							pipe.set_progress_bar_config(disable=__UpperCamelCase				)

							lowerCamelCase       =      self.get_dummy_inputs(__UpperCamelCase				)
							lowerCamelCase       =      pipe(**__UpperCamelCase				)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__UpperCamelCase				)
										lowerCamelCase       =      self.pipeline_class.from_pretrained(__UpperCamelCase				)
										pipe_loaded.to(__UpperCamelCase				)
										pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase				)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

							lowerCamelCase       =      self.get_dummy_inputs(__UpperCamelCase				)
							lowerCamelCase       =      pipe_loaded(**__UpperCamelCase				)[0]

							lowerCamelCase       =      np.abs(to_np(__UpperCamelCase				) - to_np(__UpperCamelCase				)				).max()
							self.assertLess(__UpperCamelCase	,				1e-4				)









"""simple docstring"""




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 					a__				(       snake_case__       )		->      List[str]:
			return getitem, k



def 					a__				(       snake_case__ ,				snake_case__       )		->      Optional[Any]:
			return setitem, k, v



def 					a__				(       snake_case__       )		->      str:
			return delitem, k



def 					a__				(       snake_case__ ,				snake_case__ ,				*snake_case__       )		->      Union[str, Any]:
			try:
						return fun(snake_case__ ,				*snake_case__       ), None
			except Exception as e:
						return None, e


lowerCAmelCase							:    Optional[int]  							=							(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

lowerCAmelCase							:    List[Any]  							=							[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

lowerCAmelCase							:    List[Any]  							=							[
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

lowerCAmelCase							:    List[Any]  							=							[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

lowerCAmelCase							:    Tuple  							=							[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

lowerCAmelCase							:    Tuple  							=							[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations""" ,				(
        pytest.param(_add_items ,				id="""add items"""       ),
        pytest.param(_overwrite_items ,				id="""overwrite items"""       ),
        pytest.param(_delete_items ,				id="""delete items"""       ),
        pytest.param(_access_absent_items ,				id="""access absent items"""       ),
        pytest.param(_add_with_resize_up ,				id="""add with resize up"""       ),
        pytest.param(_add_with_resize_down ,				id="""add with resize down"""       ),
    ) ,				)
def 					a__				(       snake_case__       )		->      Any:
			lowerCamelCase       =      HashMap(initial_block_size=4       )
			lowerCamelCase       =      {}
			for _, (fun, *args) in enumerate(snake_case__       ):
						lowerCamelCase   ,     lowerCamelCase       =      _run_operation(snake_case__ ,				snake_case__ ,				*snake_case__       )
						lowerCamelCase   ,     lowerCamelCase       =      _run_operation(snake_case__ ,				snake_case__ ,				*snake_case__       )
						assert my_res == py_res
						assert str(snake_case__       ) == str(snake_case__       )
						assert set(snake_case__       ) == set(snake_case__       )
						assert len(snake_case__       ) == len(snake_case__       )
						assert set(my.items()       ) == set(py.items()       )



def 					a__				(       )		->      int:
			def is_public(snake_case__       ) -> bool:
						return not name.startswith("""_"""       )

			lowerCamelCase       =      {name for name in dir({}       ) if is_public(snake_case__       )}
			lowerCamelCase       =      {name for name in dir(HashMap()       ) if is_public(snake_case__       )}

			assert dict_public_names > hash_public_names



