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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class        UpperCamelCase						(  SCREAMING_SNAKE_CASE_   ):
					__UpperCamelCase							= 42




class        UpperCamelCase						(  SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_   ):




					@register_to_config
					def __init__(			self			:       Dict   ,_lowerCAmelCase			:       Tuple = 32   ,_lowerCAmelCase			:       List[str] = 64   ,_lowerCAmelCase			:       Union[str, Any] = 20   ,_lowerCAmelCase			:       List[str] = 768   ,_lowerCAmelCase			:       Union[str, Any]=77   ,_lowerCAmelCase			:       Tuple=4   ,_lowerCAmelCase			:       Any = 0.0   ,_lowerCAmelCase			:       List[Any] = "silu"   ,_lowerCAmelCase			:       Union[str, Any] = None   ,_lowerCAmelCase			:       int = None   ,_lowerCAmelCase			:       Optional[int] = "linear"   ,_lowerCAmelCase			:       Any = "prd"   ,_lowerCAmelCase			:       Optional[Any] = None   ,_lowerCAmelCase			:       Optional[Any] = None   ,_lowerCAmelCase			:       Union[str, Any] = None   ,):


							"""simple docstring"""




							super().__init__()
							__snake_case									=  num_attention_heads
							__snake_case									=  attention_head_dim
							__snake_case									=  num_attention_heads * attention_head_dim
							__snake_case									=  additional_embeddings

							__snake_case									=  time_embed_dim or inner_dim
							__snake_case									=  embedding_proj_dim or embedding_dim
							__snake_case									=  clip_embed_dim or embedding_dim

							__snake_case									=  Timesteps(_lowerCAmelCase   ,_lowerCAmelCase   ,0    )
							__snake_case									=  TimestepEmbedding(_lowerCAmelCase   ,_lowerCAmelCase   ,out_dim=_lowerCAmelCase   ,act_fn=_lowerCAmelCase    )

							__snake_case									=  nn.Linear(_lowerCAmelCase   ,_lowerCAmelCase    )

							if embedding_proj_norm_type is None:
									__snake_case									=  None
							elif embedding_proj_norm_type == "layer":
									__snake_case									=  nn.LayerNorm(_lowerCAmelCase    )
							else:
									raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""    )

							__snake_case									=  nn.Linear(_lowerCAmelCase   ,_lowerCAmelCase    )

							if encoder_hid_proj_type is None:
									__snake_case									=  None
							elif encoder_hid_proj_type == "linear":
									__snake_case									=  nn.Linear(_lowerCAmelCase   ,_lowerCAmelCase    )
							else:
									raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""    )

							__snake_case									=  nn.Parameter(torch.zeros(1   ,num_embeddings + additional_embeddings   ,_lowerCAmelCase    )    )

							if added_emb_type == "prd":
									__snake_case									=  nn.Parameter(torch.zeros(1   ,1   ,_lowerCAmelCase    )    )
							elif added_emb_type is None:
									__snake_case									=  None
							else:
									raise ValueError(
									    F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""    )

							__snake_case									=  nn.ModuleList(
							    [
							        BasicTransformerBlock(
							            _lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,dropout=_lowerCAmelCase   ,activation_fn="gelu"   ,attention_bias=_lowerCAmelCase   ,)
							        for d in range(_lowerCAmelCase    )
							    ]    )

							if norm_in_type == "layer":
									__snake_case									=  nn.LayerNorm(_lowerCAmelCase    )
							elif norm_in_type is None:
									__snake_case									=  None
							else:
									raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""    )

							__snake_case									=  nn.LayerNorm(_lowerCAmelCase    )

							__snake_case									=  nn.Linear(_lowerCAmelCase   ,_lowerCAmelCase    )

							__snake_case									=  torch.full(
							    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]   ,-10_000.0    )
							causal_attention_mask.triu_(1    )
							__snake_case									=  causal_attention_mask[None, ...]
							self.register_buffer("causal_attention_mask"   ,_lowerCAmelCase   ,persistent=_lowerCAmelCase    )

							__snake_case									=  nn.Parameter(torch.zeros(1   ,_lowerCAmelCase    )    )
							__snake_case									=  nn.Parameter(torch.zeros(1   ,_lowerCAmelCase    )    )




					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 				UpperCamelCase_    (			self			:       Any    ):


							"""simple docstring"""




							__snake_case									=  {}

							def fn_recursive_add_processors(_lowerCAmelCase			:       Optional[Any]   ,_lowerCAmelCase			:       Optional[int]   ,_lowerCAmelCase			:       List[Any]    ):
									if hasattr(_lowerCAmelCase   ,"set_processor"    ):
											__snake_case									=  module.processor

									for sub_name, child in module.named_children():
											fn_recursive_add_processors(F"""{name}.{sub_name}"""   ,_lowerCAmelCase   ,_lowerCAmelCase    )

									return processors

							for name, module in self.named_children():
									fn_recursive_add_processors(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase    )

							return processors




					def 				UpperCamelCase_    (			self			:       Any   ,_lowerCAmelCase			:       Union[str, Any]    ):


							"""simple docstring"""




							__snake_case									=  len(self.attn_processors.keys()    )

							if isinstance(_lowerCAmelCase   ,_lowerCAmelCase    ) and len(_lowerCAmelCase    ) != count:
									raise ValueError(
									    F"""A dict of processors was passed, but the number of processors {len(_lowerCAmelCase    )} does not match the"""
									    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""    )

							def fn_recursive_attn_processor(_lowerCAmelCase			:       str   ,_lowerCAmelCase			:       str   ,_lowerCAmelCase			:       Any    ):
									if hasattr(_lowerCAmelCase   ,"set_processor"    ):
											if not isinstance(_lowerCAmelCase   ,_lowerCAmelCase    ):
													module.set_processor(_lowerCAmelCase    )
											else:
													module.set_processor(processor.pop(F"""{name}.processor"""    )    )

									for sub_name, child in module.named_children():
											fn_recursive_attn_processor(F"""{name}.{sub_name}"""   ,_lowerCAmelCase   ,_lowerCAmelCase    )

							for name, module in self.named_children():
									fn_recursive_attn_processor(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase    )




					def 				UpperCamelCase_    (			self			:       Optional[int]    ):


							"""simple docstring"""




							self.set_attn_processor(AttnProcessor()    )




					def 				UpperCamelCase_    (			self			:       List[str]   ,_lowerCAmelCase			:       Union[str, Any]   ,_lowerCAmelCase			:       Union[str, Any]   ,_lowerCAmelCase			:       List[Any]   ,_lowerCAmelCase			:       Any = None   ,_lowerCAmelCase			:       Optional[Any] = None   ,_lowerCAmelCase			:       Union[str, Any] = True   ,):


							"""simple docstring"""




							__snake_case									=  hidden_states.shape[0]

							__snake_case									=  timestep
							if not torch.is_tensor(_lowerCAmelCase    ):
									__snake_case									=  torch.tensor([timesteps]   ,dtype=torch.long   ,device=hidden_states.device    )
							elif torch.is_tensor(_lowerCAmelCase    ) and len(timesteps.shape    ) == 0:
									__snake_case									=  timesteps[None].to(hidden_states.device    )

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							__snake_case									=  timesteps * torch.ones(_lowerCAmelCase   ,dtype=timesteps.dtype   ,device=timesteps.device    )

							__snake_case									=  self.time_proj(_lowerCAmelCase    )

							# timesteps does not contain any weights and will always return f32 tensors
							# but time_embedding might be fp16, so we need to cast here.
							__snake_case									=  timesteps_projected.to(dtype=self.dtype    )
							__snake_case									=  self.time_embedding(_lowerCAmelCase    )

							if self.embedding_proj_norm is not None:
									__snake_case									=  self.embedding_proj_norm(_lowerCAmelCase    )

							__snake_case									=  self.embedding_proj(_lowerCAmelCase    )
							if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
									__snake_case									=  self.encoder_hidden_states_proj(_lowerCAmelCase    )
							elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
									raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"    )

							__snake_case									=  self.proj_in(_lowerCAmelCase    )

							__snake_case									=  self.positional_embedding.to(hidden_states.dtype    )

							__snake_case									=  []
							__snake_case									=  0

							if encoder_hidden_states is not None:
									additional_embeds.append(_lowerCAmelCase    )
									additional_embeddings_len += encoder_hidden_states.shape[1]

							if len(proj_embeddings.shape    ) == 2:
									__snake_case									=  proj_embeddings[:, None, :]

							if len(hidden_states.shape    ) == 2:
									__snake_case									=  hidden_states[:, None, :]

							__snake_case									=  additional_embeds + [
							    proj_embeddings,
							    time_embeddings[:, None, :],
							    hidden_states,
							]

							if self.prd_embedding is not None:
									__snake_case									=  self.prd_embedding.to(hidden_states.dtype    ).expand(_lowerCAmelCase   ,-1   ,-1    )
									additional_embeds.append(_lowerCAmelCase    )

							__snake_case									=  torch.cat(
							    _lowerCAmelCase   ,dim=1   ,)

							# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
							__snake_case									=  additional_embeddings_len + proj_embeddings.shape[1] + 1
							if positional_embeddings.shape[1] < hidden_states.shape[1]:
									__snake_case									=  F.pad(
									    _lowerCAmelCase   ,(
									        0,
									        0,
									        additional_embeddings_len,
									        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
									    )   ,value=0.0   ,)

							__snake_case									=  hidden_states + positional_embeddings

							if attention_mask is not None:
									__snake_case									=  (1 - attention_mask.to(hidden_states.dtype    )) * -10_000.0
									__snake_case									=  F.pad(_lowerCAmelCase   ,(0, self.additional_embeddings)   ,value=0.0    )
									__snake_case									=  (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype    )
									__snake_case									=  attention_mask.repeat_interleave(self.config.num_attention_heads   ,dim=0    )

							if self.norm_in is not None:
									__snake_case									=  self.norm_in(_lowerCAmelCase    )

							for block in self.transformer_blocks:
									__snake_case									=  block(_lowerCAmelCase   ,attention_mask=_lowerCAmelCase    )

							__snake_case									=  self.norm_out(_lowerCAmelCase    )

							if self.prd_embedding is not None:
									__snake_case									=  hidden_states[:, -1]
							else:
									__snake_case									=  hidden_states[:, additional_embeddings_len:]

							__snake_case									=  self.proj_to_clip_embeddings(_lowerCAmelCase    )

							if not return_dict:
									return (predicted_image_embedding,)

							return PriorTransformerOutput(predicted_image_embedding=_lowerCAmelCase    )







					def 				UpperCamelCase_    (			self			:       int   ,_lowerCAmelCase			:       Optional[int]    ):


							"""simple docstring"""




							__snake_case									=  (prior_latents * self.clip_std) + self.clip_mean
							return prior_latents










"""simple docstring"""







import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ViTMSNForImageClassification, ViTMSNModel
	from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class 					A_:







			"""simple docstring"""



			def __init__(	self				,   A				,   A=13				,   A=30				,   A=2				,   A=3				,   A=True				,   A=True				,   A=32				,   A=5				,   A=4				,   A=37				,   A="gelu"				,   A=0.1				,   A=0.1				,   A=10				,   A=0.0_2				,   A=None				,   ):
						_lowerCamelCase					:					Dict	       =    parent
						_lowerCamelCase					:					List[Any]	       =    batch_size
						_lowerCamelCase					:					int	       =    image_size
						_lowerCamelCase					:					List[Any]	       =    patch_size
						_lowerCamelCase					:					Optional[int]	       =    num_channels
						_lowerCamelCase					:					Union[str, Any]	       =    is_training
						_lowerCamelCase					:					Optional[Any]	       =    use_labels
						_lowerCamelCase					:					Dict	       =    hidden_size
						_lowerCamelCase					:					Tuple	       =    num_hidden_layers
						_lowerCamelCase					:					List[Any]	       =    num_attention_heads
						_lowerCamelCase					:					str	       =    intermediate_size
						_lowerCamelCase					:					str	       =    hidden_act
						_lowerCamelCase					:					str	       =    hidden_dropout_prob
						_lowerCamelCase					:					int	       =    attention_probs_dropout_prob
						_lowerCamelCase					:					Any	       =    type_sequence_label_size
						_lowerCamelCase					:					int	       =    initializer_range
						_lowerCamelCase					:					Union[str, Any]	       =    scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_lowerCamelCase					:					Dict	       =    (image_size // patch_size) ** 2
						_lowerCamelCase					:					Any	       =    num_patches + 1
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						_lowerCamelCase					:					Union[str, Any]	       =    None
						if self.use_labels:
									_lowerCamelCase					:					List[str]	       =    ids_tensor([self.batch_size]				,   self.type_sequence_label_size							)

						_lowerCamelCase					:					Dict	       =    self.get_config()

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self							):
						return ViTMSNConfig(
						    image_size=self.image_size				,   patch_size=self.patch_size				,   num_channels=self.num_channels				,   hidden_size=self.hidden_size				,   num_hidden_layers=self.num_hidden_layers				,   num_attention_heads=self.num_attention_heads				,   intermediate_size=self.intermediate_size				,   hidden_act=self.hidden_act				,   hidden_dropout_prob=self.hidden_dropout_prob				,   attention_probs_dropout_prob=self.attention_probs_dropout_prob				,   initializer_range=self.initializer_range				,   )
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					int	       =    ViTMSNModel(config=A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					List[Any]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[Any]	       =    self.type_sequence_label_size
						_lowerCamelCase					:					List[str]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					Optional[Any]	       =    model(A				,   labels=A							)
						print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'							)
						print('Labels: {labels}'							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						_lowerCamelCase					:					Optional[Any]	       =    1
						_lowerCamelCase					:					List[Any]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()

						_lowerCamelCase					:					Any	       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						_lowerCamelCase					:					Union[str, Any]	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.prepare_config_and_inputs()
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    config_and_inputs
						_lowerCamelCase					:					List[str]	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class 					A_(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			a_       :			Union[str, Any]												=			(
			    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			a_       :			Tuple												=			False
			a_       :			Union[str, Any]												=			False
			a_       :			Tuple												=			False
			a_       :			List[str]												=			False
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    ViTMSNModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			@unittest.skip(reason='ViTMSN does not use inputs_embeds'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					List[str]	       =    model_class(A							)
									self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)							)
									_lowerCamelCase					:					Dict	       =    model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,   nn.Linear							)							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    self.model_tester.prepare_config_and_inputs_for_common()

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

									_lowerCamelCase					:					Union[str, Any]	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCamelCase					:					Any	       =    ViTMSNModel.from_pretrained(A							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Optional[Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'							) if is_vision_available() else None

			@slow
			def    _lowerCAmelCase		(	self							):
						torch.manual_seed(2							)
						_lowerCamelCase					:					Tuple	       =    ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'							).to(A							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					Optional[int]	       =    prepare_img()
						_lowerCamelCase					:					int	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

						# forward pass
						with torch.no_grad():
									_lowerCamelCase					:					str	       =    model(**A							)

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

						_lowerCamelCase					:					Any	       =    torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]							).to(A							)

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





