
"""simple docstring"""




from typing import Any, Dict, Optional

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

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings



@maybe_allow_in_graph
class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = "geglu"   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = "layer_norm"   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   only_cross_attention

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

											if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
																		raise ValueError(
																		    F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
																		    F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''    )

											# Define 3 blocks. Each block has its own normalization layer.
											# 1. Self-Attn
											if self.use_ada_layer_norm:
																		SCREAMING_SNAKE_CASE__						:       int           =   AdaLayerNorm(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   AdaLayerNormZero(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   Attention(
											    query_dim=SCREAMING_SNAKE_CASE__   ,       heads=SCREAMING_SNAKE_CASE__   ,       dim_head=SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       bias=SCREAMING_SNAKE_CASE__   ,       cross_attention_dim=cross_attention_dim if only_cross_attention else None   ,       upcast_attention=SCREAMING_SNAKE_CASE__   ,       )

											# 2. Cross-Attn
											if cross_attention_dim is not None or double_self_attention:
																		# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
																		# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
																		# the second cross attention block.
																		SCREAMING_SNAKE_CASE__						:       Any           =   (
																		    AdaLayerNorm(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		    if self.use_ada_layer_norm
																		    else nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
																		)
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   Attention(
																		    query_dim=SCREAMING_SNAKE_CASE__   ,       cross_attention_dim=cross_attention_dim if not double_self_attention else None   ,       heads=SCREAMING_SNAKE_CASE__   ,       dim_head=SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       bias=SCREAMING_SNAKE_CASE__   ,       upcast_attention=SCREAMING_SNAKE_CASE__   ,       )  # is self-attn if encoder_hidden_states is none
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   None
																		SCREAMING_SNAKE_CASE__						:       str           =   None

											# 3. Feed-forward
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FeedForward(SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       activation_fn=SCREAMING_SNAKE_CASE__   ,       final_dropout=SCREAMING_SNAKE_CASE__    )

											# let chunk size default to None
											SCREAMING_SNAKE_CASE__						:       Tuple           =   None
											SCREAMING_SNAKE_CASE__						:       Dict           =   0



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   chunk_size
											SCREAMING_SNAKE_CASE__						:       str           =   dim



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Any:



											"""simple docstring"""




											if self.use_ada_layer_norm:
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.norma(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.norma(
																		    SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       hidden_dtype=hidden_states.dtype    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.norma(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.attna(
											    SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )
											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   gate_msa.unsqueeze(1    ) * attn_output
											SCREAMING_SNAKE_CASE__						:       Dict           =   attn_output + hidden_states

											# 2. Cross-Attention
											if self.attna is not None:
																		SCREAMING_SNAKE_CASE__						:       str           =   (
																		    self.norma(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__    )
																		)

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.attna(
																		    SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   attn_output + hidden_states

											# 3. Feed-forward
											SCREAMING_SNAKE_CASE__						:       Any           =   self.norma(SCREAMING_SNAKE_CASE__    )

											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

											if self._chunk_size is not None:
																		# "feed_forward_chunk_size" can be used to save memory
																		if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																									raise ValueError(
																									    F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''    )

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																		SCREAMING_SNAKE_CASE__						:       int           =   torch.cat(
																		    [self.ff(SCREAMING_SNAKE_CASE__    ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__   ,       dim=self._chunk_dim    )]   ,       dim=self._chunk_dim   ,       )
											else:
																		SCREAMING_SNAKE_CASE__						:       int           =   self.ff(SCREAMING_SNAKE_CASE__    )

											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   gate_mlp.unsqueeze(1    ) * ff_output

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ff_output + hidden_states

											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 4   ,       SCREAMING_SNAKE_CASE__ = 0.0   ,       SCREAMING_SNAKE_CASE__ = "geglu"   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(dim * mult    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   dim_out if dim_out is not None else dim

											if activation_fn == "gelu":
																		SCREAMING_SNAKE_CASE__						:       str           =   GELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											if activation_fn == "gelu-approximate":
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   GELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       approximate="""tanh"""    )
											elif activation_fn == "geglu":
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   GEGLU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif activation_fn == "geglu-approximate":
																		SCREAMING_SNAKE_CASE__						:       int           =   ApproximateGELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   nn.ModuleList([]    )
											# project in
											self.net.append(SCREAMING_SNAKE_CASE__    )
											# project dropout
											self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__    )    )
											# project out
											self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )
											# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
											if final_dropout:
																		self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__    )    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											for module in self.net:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   module(SCREAMING_SNAKE_CASE__    )
											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = "none"    )					->		List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   approximate



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											if gate.device.type != "mps":
																		return F.gelu(SCREAMING_SNAKE_CASE__   ,       approximate=self.approximate    )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa    )   ,       approximate=self.approximate    ).to(dtype=gate.dtype    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.proj(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.gelu(SCREAMING_SNAKE_CASE__    )
											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Dict           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       dim_out * 2    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											if gate.device.type != "mps":
																		return F.gelu(SCREAMING_SNAKE_CASE__    )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa    )    ).to(dtype=gate.dtype    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.proj(SCREAMING_SNAKE_CASE__    ).chunk(2   ,       dim=-1    )
											return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__    )






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       str           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.proj(SCREAMING_SNAKE_CASE__    )
											return x * torch.sigmoid(1.702 * x    )






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Embedding(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   nn.SiLU()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       embedding_dim * 2    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__    )    )    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   torch.chunk(SCREAMING_SNAKE_CASE__   ,       2    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.norm(SCREAMING_SNAKE_CASE__    ) * (1 + scale) + shift
											return x






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											super().__init__()

											SCREAMING_SNAKE_CASE__						:       Any           =   CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nn.SiLU()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       6 * embedding_dim   ,       bias=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__   ,       eps=1E-6    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       hidden_dtype=SCREAMING_SNAKE_CASE__    )    )    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   emb.chunk(6   ,       dim=1    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.norm(SCREAMING_SNAKE_CASE__    ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
											return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 1E-5    )					->		int:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   num_groups
											SCREAMING_SNAKE_CASE__						:       Any           =   eps

											if act_fn is None:
																		SCREAMING_SNAKE_CASE__						:       Any           =   None
											else:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   get_activation(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       out_dim * 2    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											if self.act:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.act(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.linear(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   emb[:, :, None, None]
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   emb.chunk(2   ,       dim=1    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   F.group_norm(SCREAMING_SNAKE_CASE__   ,       self.num_groups   ,       eps=self.eps    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   x * (1 + scale) + shift
											return x









'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Any=1_3  ,						UpperCamelCase__      :					Optional[int]=3_2  ,						UpperCamelCase__      :					Any=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					str=[1_0, 2_0, 3_0, 4_0]  ,						UpperCamelCase__      :					str=[2, 2, 3, 2]  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					str=3_7  ,						UpperCamelCase__      :					Union[str, Any]="gelu"  ,						UpperCamelCase__      :					Dict=1_0  ,						UpperCamelCase__      :					Union[str, Any]=0.0_2  ,						UpperCamelCase__      :					int=["stage2", "stage3", "stage4"]  ,						UpperCamelCase__      :					List[str]=[2, 3, 4]  ,						UpperCamelCase__      :					Any=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  num_stages
       UpperCamelCase					=  hidden_sizes
       UpperCamelCase					=  depths
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  out_features
       UpperCamelCase					=  out_indices
       UpperCamelCase					=  scope






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_labels )

       UpperCamelCase					=  self.get_config()
       return config, pixel_values, labels






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return ConvNextConfig(
           num_channels=self.num_channels  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_stages=self.num_stages  ,						hidden_act=self.hidden_act  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						out_features=self.out_features  ,						out_indices=self.out_indices  ,						num_labels=self.num_labels  ,						)






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )
       # 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 // 3_2, self.image_size // 3_2)  ,						)






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps )  ,						len(config.out_features ) )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[1], 4, 4] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						len(config.out_features ) )
       self.parent.assertListEqual(model.channels  ,						config.hidden_sizes[1:] )

       # verify backbone works with out_features=None
       UpperCamelCase					=  None
       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps )  ,						1 )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[-1], 1, 1] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						1 )
       self.parent.assertListEqual(model.channels  ,						[config.hidden_sizes[-1]] )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  config_and_inputs
       UpperCamelCase					=  {'pixel_values': pixel_values}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            ConvNextModel,
            ConvNextForImageClassification,
            ConvNextBackbone,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (
        {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
        if is_torch_available()
        else {}
    )

    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       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  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return






    @unittest.skip(reason='ConvNext does not use inputs_embeds' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not support input and output embeddings' )
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not use feedforward chunking' )
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.eval()

          with torch.no_grad():
             UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          UpperCamelCase					=  self.model_tester.num_stages
          self.assertEqual(len(UpperCamelCase__ )  ,						expected_num_stages + 1 )

          # ConvNext'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 // 4, self.model_tester.image_size // 4]  ,						)

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCamelCase					=  True

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					Dict ):


       """simple docstring"""







       for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  ConvNextModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )







def 				__lowerCamelCase				(   )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

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






    """simple docstring"""





    @cached_property
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' ).to(UpperCamelCase__ )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='pt' ).to(UpperCamelCase__ )

       # forward pass
       with torch.no_grad():
          UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(UpperCamelCase__ )

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

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (ConvNextBackbone,) if is_torch_available() else ()
    _SCREAMING_SNAKE_CASE	  =  ConvNextConfig

    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )

