

'''simple docstring'''


import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import RegNetForImageClassification, RegNetModel
						from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=3    ,			_snake_case=32    ,			_snake_case=3    ,			_snake_case=10    ,			_snake_case=[10, 20, 30, 40]    ,			_snake_case=[1, 1, 2, 1]    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case="relu"    ,			_snake_case=3    ,			_snake_case=None    ,			)							->				str:
								UpperCAmelCase_		:      Optional[Any]       	=					parent
								UpperCAmelCase_		:      Dict       	=					batch_size
								UpperCAmelCase_		:      int       	=					image_size
								UpperCAmelCase_		:      Dict       	=					num_channels
								UpperCAmelCase_		:      Optional[Any]       	=					embeddings_size
								UpperCAmelCase_		:      Tuple       	=					hidden_sizes
								UpperCAmelCase_		:      Union[str, Any]       	=					depths
								UpperCAmelCase_		:      int       	=					is_training
								UpperCAmelCase_		:      Optional[int]       	=					use_labels
								UpperCAmelCase_		:      str       	=					hidden_act
								UpperCAmelCase_		:      List[str]       	=					num_labels
								UpperCAmelCase_		:      int       	=					scope
								UpperCAmelCase_		:      Tuple       	=					len(_snake_case)


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      List[str]       	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:      List[Any]       	=					None
								if self.use_labels:
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size]    ,			self.num_labels)

								UpperCAmelCase_		:      int       	=					self.get_config()

								return config, pixel_values, labels


							def 						_snake_case							(			self)							->				Union[str, Any]:
								return RegNetConfig(
								    num_channels=self.num_channels    ,			embeddings_size=self.embeddings_size    ,			hidden_sizes=self.hidden_sizes    ,			depths=self.depths    ,			hidden_act=self.hidden_act    ,			num_labels=self.num_labels    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Any:
								UpperCAmelCase_		:      List[str]       	=					RegNetModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Any       	=					model(_snake_case)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Any       	=					self.num_labels
								UpperCAmelCase_		:      List[str]       	=					RegNetForImageClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Any       	=					model(_snake_case    ,			labels=_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))





							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      int       	=					self.prepare_config_and_inputs()
								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					config_and_inputs
								UpperCAmelCase_		:      Dict       	=					{'pixel_values': pixel_values}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Optional[Any]=   (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							_lowerCamelCase		:      Any=   (
							    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_lowerCamelCase		:      List[str]=   False
							_lowerCamelCase		:      int=   False
							_lowerCamelCase		:      Union[str, Any]=   False
							_lowerCamelCase		:      List[str]=   False


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					RegNetModelTester(self)
								UpperCAmelCase_		:      List[Any]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			has_text_modality=_snake_case)


							def 						_snake_case							(			self)							->				int:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


							def 						_snake_case							(			self)							->				List[str]:
								return


							@unittest.skip(reason='RegNet does not use inputs_embeds')
							def 						_snake_case							(			self)							->				str:
								pass


							@unittest.skip(reason='RegNet does not support input and output embeddings')
							def 						_snake_case							(			self)							->				str:
								pass


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Optional[int]       	=					model_class(_snake_case)
									UpperCAmelCase_		:      str       	=					inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_		:      Optional[Any]       	=					[*signature.parameters.keys()]

									UpperCAmelCase_		:      str       	=					['pixel_values']
									self.assertListEqual(arg_names[:1]    ,			_snake_case)


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Dict       	=					model_class(config=_snake_case)
									for name, module in model.named_modules():
										if isinstance(_snake_case    ,			(nn.BatchNormad, nn.GroupNorm)):
											self.assertTrue(
											    torch.all(module.weight == 1)    ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,			)
											self.assertTrue(
											    torch.all(module.bias == 0)    ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,			)


							def 						_snake_case							(			self)							->				Any:
								def check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      Tuple       	=					model_class(_snake_case)
									model.to(_snake_case)
									model.eval()

									with torch.no_grad():
										UpperCAmelCase_		:      Optional[Any]       	=					model(**self._prepare_for_class(_snake_case    ,			_snake_case))

									UpperCAmelCase_		:      Optional[int]       	=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_		:      Any       	=					self.model_tester.num_stages
									self.assertEqual(len(_snake_case)    ,			expected_num_stages + 1)

									# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])    ,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]    ,			)

								UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Optional[Any]       	=					['basic', 'bottleneck']
								for model_class in self.all_model_classes:
									for layer_type in layers_type:
										UpperCAmelCase_		:      Optional[int]       	=					layer_type
										UpperCAmelCase_		:      Union[str, Any]       	=					True
										check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										UpperCAmelCase_		:      Optional[Any]       	=					True

										check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)





							@slow
							def 						_snake_case							(			self)							->				List[Any]:
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_		:      Tuple       	=					RegNetModel.from_pretrained(_snake_case)
									self.assertIsNotNone(_snake_case)







def 						SCREAMING_SNAKE_CASE(  )    ->    List[Any]:
	UpperCAmelCase_		:      Dict       	=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







@require_torch
@require_vision
class 					lowercase						(  unittest.TestCase ):


							@cached_property
							def 						_snake_case							(			self)							->				Optional[Any]:
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
								    if is_vision_available()
								    else None
								)





							@slow
							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      List[str]       	=					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					self.default_image_processor
								UpperCAmelCase_		:      str       	=					prepare_img()
								UpperCAmelCase_		:      Optional[Any]       	=					image_processor(images=_snake_case    ,			return_tensors='pt').to(_snake_case)

								# forward pass
								with torch.no_grad():
									UpperCAmelCase_		:      Optional[int]       	=					model(**_snake_case)

								# verify the logits
								UpperCAmelCase_		:      Optional[Any]       	=					torch.Size((1, 1000))
								self.assertEqual(outputs.logits.shape    ,			_snake_case)

								UpperCAmelCase_		:      Tuple       	=					torch.tensor([-0.4_180, -1.5_051, -3.4_836]).to(_snake_case)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,			_snake_case    ,			atol=1e-4))









'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class 					lowercase						(  a_ ):
							_lowerCamelCase		:      torch.FloatTensor



class 					lowercase						(  a_,					a_ ):


							@register_to_config
							def __init__(			self    ,			_snake_case = 6_5536    ,			_snake_case = None    ,			_snake_case = 2    ,			_snake_case = 2    ,			_snake_case = 0    ,			_snake_case = "fourier"    ,			_snake_case = True    ,			_snake_case = False    ,			_snake_case = 0.0    ,			_snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,			_snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,			_snake_case = "UNetMidBlock1D"    ,			_snake_case = None    ,			_snake_case = (32, 32, 64)    ,			_snake_case = None    ,			_snake_case = 8    ,			_snake_case = 1    ,			_snake_case = False    ,			)							->				List[str]:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					sample_size

								# time
								if time_embedding_type == "fourier":
									UpperCAmelCase_		:      Tuple       	=					GaussianFourierProjection(
									    embedding_size=8    ,			set_W_to_weight=_snake_case    ,			log=_snake_case    ,			flip_sin_to_cos=_snake_case)
									UpperCAmelCase_		:      int       	=					2 * block_out_channels[0]
								elif time_embedding_type == "positional":
									UpperCAmelCase_		:      Optional[Any]       	=					Timesteps(
									    block_out_channels[0]    ,			flip_sin_to_cos=_snake_case    ,			downscale_freq_shift=_snake_case)
									UpperCAmelCase_		:      List[Any]       	=					block_out_channels[0]

								if use_timestep_embedding:
									UpperCAmelCase_		:      Dict       	=					block_out_channels[0] * 4
									UpperCAmelCase_		:      List[Any]       	=					TimestepEmbedding(
									    in_channels=_snake_case    ,			time_embed_dim=_snake_case    ,			act_fn=_snake_case    ,			out_dim=block_out_channels[0]    ,			)

								UpperCAmelCase_		:      int       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Optional[int]       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Any       	=					None

								# down
								UpperCAmelCase_		:      Dict       	=					in_channels
								for i, down_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      int       	=					output_channel
									UpperCAmelCase_		:      Optional[int]       	=					block_out_channels[i]

									if i == 0:
										input_channel += extra_in_channels

									UpperCAmelCase_		:      int       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Any       	=					get_down_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_downsample=not is_final_block or downsample_each_block    ,			)
									self.down_blocks.append(_snake_case)

								# mid
								UpperCAmelCase_		:      Optional[int]       	=					get_mid_block(
								    _snake_case    ,			in_channels=block_out_channels[-1]    ,			mid_channels=block_out_channels[-1]    ,			out_channels=block_out_channels[-1]    ,			embed_dim=block_out_channels[0]    ,			num_layers=_snake_case    ,			add_downsample=_snake_case    ,			)

								# up
								UpperCAmelCase_		:      Union[str, Any]       	=					list(reversed(_snake_case))
								UpperCAmelCase_		:      Optional[int]       	=					reversed_block_out_channels[0]
								if out_block_type is None:
									UpperCAmelCase_		:      Tuple       	=					out_channels
								else:
									UpperCAmelCase_		:      int       	=					block_out_channels[0]

								for i, up_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					output_channel
									UpperCAmelCase_		:      Optional[Any]       	=					(
									    reversed_block_out_channels[i + 1] if i < len(_snake_case) - 1 else final_upsample_channels
									)

									UpperCAmelCase_		:      str       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Union[str, Any]       	=					get_up_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_upsample=not is_final_block    ,			)
									self.up_blocks.append(_snake_case)
									UpperCAmelCase_		:      Dict       	=					output_channel

								# out
								UpperCAmelCase_		:      Union[str, Any]       	=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,			32)
								UpperCAmelCase_		:      Any       	=					get_out_block(
								    out_block_type=_snake_case    ,			num_groups_out=_snake_case    ,			embed_dim=block_out_channels[0]    ,			out_channels=_snake_case    ,			act_fn=_snake_case    ,			fc_dim=block_out_channels[-1] // 4    ,			)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = True    ,			)							->				Union[UNetaDOutput, Tuple]:
								UpperCAmelCase_		:      Union[str, Any]       	=					timestep
								if not torch.is_tensor(_snake_case):
									UpperCAmelCase_		:      Union[str, Any]       	=					torch.tensor([timesteps]    ,			dtype=torch.long    ,			device=sample.device)
								elif torch.is_tensor(_snake_case) and len(timesteps.shape) == 0:
									UpperCAmelCase_		:      Tuple       	=					timesteps[None].to(sample.device)

								UpperCAmelCase_		:      Any       	=					self.time_proj(_snake_case)
								if self.config.use_timestep_embedding:
									UpperCAmelCase_		:      int       	=					self.time_mlp(_snake_case)
								else:
									UpperCAmelCase_		:      int       	=					timestep_embed[..., None]
									UpperCAmelCase_		:      List[Any]       	=					timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
									UpperCAmelCase_		:      int       	=					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

								# 2. down
								UpperCAmelCase_		:      Optional[Any]       	=					()
								for downsample_block in self.down_blocks:
									UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					downsample_block(hidden_states=_snake_case    ,			temb=_snake_case)
									down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
									UpperCAmelCase_		:      List[Any]       	=					self.mid_block(_snake_case    ,			_snake_case)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks):
									UpperCAmelCase_		:      int       	=					down_block_res_samples[-1:]
									UpperCAmelCase_		:      Tuple       	=					down_block_res_samples[:-1]
									UpperCAmelCase_		:      List[Any]       	=					upsample_block(_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			temb=_snake_case)

								# 5. post-process
								if self.out_block:
									UpperCAmelCase_		:      Optional[Any]       	=					self.out_block(_snake_case    ,			_snake_case)

								if not return_dict:
									return (sample,)

								return UNetaDOutput(sample=_snake_case)






