

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     =      {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      [
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      [
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      [
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase_     =      _LazyModule(__name__, globals()["__file__"], _import_structure)








'''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 ) )





