
from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__				=							{"""tokenization_bert_japanese""": ["""BertJapaneseTokenizer""", """CharacterTokenizer""", """MecabTokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
							import sys

							SCREAMING_SNAKE_CASE__				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self,						lowercase_ = False )    ->							Union[str, Any]:
					snake_case							=					10

					if only_cross_attention:
						snake_case							=					12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						snake_case							=					query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Optional[int]:
					snake_case							=					2

					snake_case							=					torch.rand(lowercase_,						lowercase_,						3,						2 )
					snake_case							=					torch.rand(lowercase_,						4,						lowercase_ )
					snake_case							=					None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}






				def 					_lowerCamelCase    (       self )    ->							str:
					# self and cross attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is not None )
					self.assertTrue(attn.to_v is not None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					# only self attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is None )
					self.assertTrue(attn.to_v is None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all() )







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					# the initial conversion succeeds
					snake_case							=					DiffusionPipeline.from_pretrained(
					    'hf-internal-testing/tiny-stable-diffusion-pipe',						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(lowercase_ )

						# can also load the converted weights
						snake_case							=					DiffusionPipeline.from_pretrained(lowercase_,						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )
					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )





