
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowerCamelCase_				:							Any       =    logging.get_logger(__name__)
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		bool = True					)       ->   str:
       __snake_case						       =   {}
       for name in model.attn_processors.keys():
              __snake_case						       =   name.endswith('attn2.processor'					) and not (
                  name.startswith('transformer_in'					) or '''temp_attentions''' in name.split('.'					)
              )
              __snake_case						       =   model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith('mid_block'					):
                     __snake_case						       =   model.config.block_out_channels[-1]
              elif name.startswith('up_blocks'					):
                     __snake_case						       =   int(name[len('up_blocks.'					)]					)
                     __snake_case						       =   list(reversed(model.config.block_out_channels					)					)[block_id]
              elif name.startswith('down_blocks'					):
                     __snake_case						       =   int(name[len('down_blocks.'					)]					)
                     __snake_case						       =   model.config.block_out_channels[block_id]
              elif name.startswith('transformer_in'					):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     __snake_case						       =   8 * model.config.attention_head_dim

              __snake_case						       =   LoRAAttnProcessor(hidden_size=__A    ,cross_attention_dim=__A					)
              __snake_case						       =   lora_attn_procs[name].to(model.device					)

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs







@skip_mps
class  _lowerCamelCase			(_lowerCamelCase							,					_lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			UNetaDConditionModel
     lowercase__				  =			"""sample"""

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   4
            __snake_case						       =   4
            __snake_case						       =   4
            __snake_case						       =   (32, 32)

            __snake_case						       =   floats_tensor((batch_size, num_channels, num_frames) + sizes    ).to(lowerCamelCase_    )
            __snake_case						       =   torch.tensor([10]    ).to(lowerCamelCase_    )
            __snake_case						       =   floats_tensor((batch_size, 4, 32)    ).to(lowerCamelCase_    )

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

     @property
     def 						__lowerCamelCase				(  self    ):
            return (4, 4, 32, 32)

     @property
     def 						__lowerCamelCase				(  self    ):
            return (4, 4, 32, 32)

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                '''block_out_channels''': (32, 64),
                '''down_block_types''': (
                    '''CrossAttnDownBlock3D''',
                    '''DownBlock3D''',
                ),
                '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
                '''cross_attention_dim''': 32,
                '''attention_head_dim''': 8,
                '''out_channels''': 4,
                '''in_channels''': 4,
                '''layers_per_block''': 1,
                '''sample_size''': 32,
            }
            __snake_case						       =   self.dummy_input
            return init_dict, inputs_dict

     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()							,       reason='XFormers attention is only available with CUDA and `xformers` installed'							,       )
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()
            __snake_case						       =   self.model_class(**lowerCamelCase_    )

            model.enable_xformers_memory_efficient_attention()

            assert (
                model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                == "XFormersAttnProcessor"
            ), "xformers is not enabled"

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   32

            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )

                   if isinstance(lowerCamelCase_							,       lowerCamelCase_    ):
                          __snake_case						       =   output.sample

            self.assertIsNotNone(lowerCamelCase_    )
            __snake_case						       =   inputs_dict['''sample'''].shape
            self.assertEqual(output.shape							,       lowerCamelCase_							,       'Input and output shapes do not match'    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            with torch.no_grad():
                   # Warmup pass when using mps (see #372)
                   if torch_device == "mps" and isinstance(lowerCamelCase_							,       lowerCamelCase_    ):
                          model(**self.dummy_input    )

                   __snake_case						       =   model(**lowerCamelCase_    )
                   if isinstance(lowerCamelCase_							,       lowerCamelCase_    ):
                          __snake_case						       =   first.sample

                   __snake_case						       =   model(**lowerCamelCase_    )
                   if isinstance(lowerCamelCase_							,       lowerCamelCase_    ):
                          __snake_case						       =   second.sample

            __snake_case						       =   first.cpu().numpy()
            __snake_case						       =   second.cpu().numpy()
            __snake_case						       =   out_a[~np.isnan(lowerCamelCase_    )]
            __snake_case						       =   out_a[~np.isnan(lowerCamelCase_    )]
            __snake_case						       =   np.amax(np.abs(out_a - out_a    )    )
            self.assertLessEqual(lowerCamelCase_							,       1E-5    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            model.set_attention_slice('auto'    )
            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )
            assert output is not None

            model.set_attention_slice('max'    )
            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )
            assert output is not None

            model.set_attention_slice(2    )
            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )
            assert output is not None

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            __snake_case						       =   create_lora_layers(lowerCamelCase_    )

            # make sure we can set a list of attention processors
            model.set_attn_processor(lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.0}    ).sample
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample

            assert (samplea - samplea).abs().max() < 3E-3
            assert (samplea - samplea).abs().max() < 3E-3

            # sample 2 and sample 3 should be different
            assert (samplea - samplea).abs().max() > 3E-3

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            __snake_case						       =   create_lora_layers(lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(lowerCamelCase_    )
                   self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_							,       'pytorch_lora_weights.bin'    )    )    )
                   torch.manual_seed(0    )
                   __snake_case						       =   self.model_class(**lowerCamelCase_    )
                   new_model.to(lowerCamelCase_    )
                   new_model.load_attn_procs(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   new_model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample

            assert (sample - new_sample).abs().max() < 1E-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1E-4

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            __snake_case						       =   create_lora_layers(lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(lowerCamelCase_							,       safe_serialization=lowerCamelCase_    )
                   self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_							,       'pytorch_lora_weights.safetensors'    )    )    )
                   torch.manual_seed(0    )
                   __snake_case						       =   self.model_class(**lowerCamelCase_    )
                   new_model.to(lowerCamelCase_    )
                   new_model.load_attn_procs(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   new_model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.5}    ).sample

            assert (sample - new_sample).abs().max() < 3E-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1E-4

     def 						__lowerCamelCase				(  self    ):
            # enable deterministic behavior for gradient checkpointing
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            __snake_case						       =   create_lora_layers(lowerCamelCase_							,       mock_weights=lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(lowerCamelCase_    )
                   self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_							,       'pytorch_lora_weights.bin'    )    )    )
                   torch.manual_seed(0    )
                   __snake_case						       =   self.model_class(**lowerCamelCase_    )
                   new_model.to(lowerCamelCase_    )
                   new_model.load_attn_procs(lowerCamelCase_							,       weight_name='pytorch_lora_weights.bin'    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            __snake_case						       =   create_lora_layers(lowerCamelCase_							,       mock_weights=lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(lowerCamelCase_    )
                   self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_							,       'pytorch_lora_weights.bin'    )    )    )
                   torch.manual_seed(0    )
                   __snake_case						       =   self.model_class(**lowerCamelCase_    )
                   new_model.to(lowerCamelCase_    )
                   with self.assertRaises(lowerCamelCase_    ) as e:
                          new_model.load_attn_procs(lowerCamelCase_							,       use_safetensors=lowerCamelCase_    )
                   self.assertIn('Error no file named pytorch_lora_weights.safetensors'							,       str(e.exception    )    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   8

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            __snake_case						       =   create_lora_layers(lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_							,       cross_attention_kwargs={'scale': 0.0}    ).sample

            model.set_attn_processor(AttnProcessor()    )

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            assert (sample - new_sample).abs().max() < 1E-4
            assert (sample - old_sample).abs().max() < 3E-3

     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()							,       reason='XFormers attention is only available with CUDA and `xformers` installed'							,       )
     def 						__lowerCamelCase				(  self    ):
            # enable deterministic behavior for gradient checkpointing
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()

            __snake_case						       =   4

            torch.manual_seed(0    )
            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            __snake_case						       =   create_lora_layers(lowerCamelCase_    )
            model.set_attn_processor(lowerCamelCase_    )

            # default
            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

                   model.enable_xformers_memory_efficient_attention()
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

                   model.disable_xformers_memory_efficient_attention()
                   __snake_case						       =   model(**lowerCamelCase_    ).sample

            assert (sample - on_sample).abs().max() < 1E-4
            assert (sample - off_sample).abs().max() < 1E-4

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.prepare_init_args_and_inputs_for_common()
            __snake_case						       =   32

            __snake_case						       =   self.model_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                   __snake_case						       =   model(**lowerCamelCase_    )[0]

            self.assertEqual(output.shape							,       output_a.shape							,       'Shape doesn\'t match'    )
            assert np.abs(output.cpu() - output_a.cpu()    ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.






import math
from collections.abc import Generator







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										# only need to check for factors up to sqrt(i)
										__magic_name__				:						Tuple     				=				int(math.sqrt(__A					)					) + 1
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)				,2					))
					# It's useless to test even numbers as they will not be prime
					if max_n > 2:
										yield 2  # Because 2 will not be tested, it's necessary to yield it now
					for i in (n for n in numbers if n > 1):
										__magic_name__				:						str     				=				int(math.sqrt(__A					)					) + 1
										for j in range(3				,__A				,2					):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		):





					'''simple docstring'''






					from timeit import timeit

					__magic_name__				:						Dict     				=				'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''fast_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)


if __name__ == "__main__":
	__lowerCamelCase :    Union[str, Any]      						=      int(input('''Calculate primes up to:\n>> ''').strip())
	for ret in primes(number):
		print(ret)
	benchmark()



