
import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)



def      _a     (   __UpperCamelCase       :  Any    ):
			lowerCAmelCase__ :    Dict				      =			CLIPSegTextConfig()
			lowerCAmelCase__ :    int				      =			CLIPSegVisionConfig(patch_size=16    )

			lowerCAmelCase__ :    List[Any]				      =			True if '''refined''' in model_name else False
			lowerCAmelCase__ :    str				      =			16 if '''rd16''' in model_name else 64

			lowerCAmelCase__ :    Optional[int]				      =			CLIPSegConfig.from_text_vision_configs(
			    __UpperCamelCase	,__UpperCamelCase	,use_complex_transposed_convolution=__UpperCamelCase	,reduce_dim=__UpperCamelCase	,)
			return config



def      _a     (   __UpperCamelCase       :  Optional[Any]    ):
			# update prefixes
			if "clip_model" in name:
						lowerCAmelCase__ :    Union[str, Any]				      =			name.replace('''clip_model'''	,'''clip'''    )
			if "transformer" in name:
						if "visual" in name:
									lowerCAmelCase__ :    Optional[Any]				      =			name.replace('''visual.transformer'''	,'''vision_model'''    )
						else:
									lowerCAmelCase__ :    int				      =			name.replace('''transformer'''	,'''text_model'''    )
			if "resblocks" in name:
						lowerCAmelCase__ :    List[str]				      =			name.replace('''resblocks'''	,'''encoder.layers'''    )
			if "ln_1" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''ln_1'''	,'''layer_norm1'''    )
			if "ln_2" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''ln_2'''	,'''layer_norm2'''    )
			if "c_fc" in name:
						lowerCAmelCase__ :    str				      =			name.replace('''c_fc'''	,'''fc1'''    )
			if "c_proj" in name:
						lowerCAmelCase__ :    List[Any]				      =			name.replace('''c_proj'''	,'''fc2'''    )
			if "attn" in name and "self" not in name:
						lowerCAmelCase__ :    Any				      =			name.replace('''attn'''	,'''self_attn'''    )
			# text encoder
			if "token_embedding" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''token_embedding'''	,'''text_model.embeddings.token_embedding'''    )
			if "positional_embedding" in name and "visual" not in name:
						lowerCAmelCase__ :    Optional[int]				      =			name.replace('''positional_embedding'''	,'''text_model.embeddings.position_embedding.weight'''    )
			if "ln_final" in name:
						lowerCAmelCase__ :    List[str]				      =			name.replace('''ln_final'''	,'''text_model.final_layer_norm'''    )
			# vision encoder
			if "visual.class_embedding" in name:
						lowerCAmelCase__ :    int				      =			name.replace('''visual.class_embedding'''	,'''vision_model.embeddings.class_embedding'''    )
			if "visual.conv1" in name:
						lowerCAmelCase__ :    Optional[Any]				      =			name.replace('''visual.conv1'''	,'''vision_model.embeddings.patch_embedding'''    )
			if "visual.positional_embedding" in name:
						lowerCAmelCase__ :    int				      =			name.replace('''visual.positional_embedding'''	,'''vision_model.embeddings.position_embedding.weight'''    )
			if "visual.ln_pre" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''visual.ln_pre'''	,'''vision_model.pre_layrnorm'''    )
			if "visual.ln_post" in name:
						lowerCAmelCase__ :    List[str]				      =			name.replace('''visual.ln_post'''	,'''vision_model.post_layernorm'''    )
			# projection layers
			if "visual.proj" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''visual.proj'''	,'''visual_projection.weight'''    )
			if "text_projection" in name:
						lowerCAmelCase__ :    Union[str, Any]				      =			name.replace('''text_projection'''	,'''text_projection.weight'''    )
			# decoder
			if "trans_conv" in name:
						lowerCAmelCase__ :    int				      =			name.replace('''trans_conv'''	,'''transposed_convolution'''    )
			if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
						lowerCAmelCase__ :    Dict				      =			'''decoder.''' + name
			if "blocks" in name:
						lowerCAmelCase__ :    Dict				      =			name.replace('''blocks'''	,'''decoder.layers'''    )
			if "linear1" in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''linear1'''	,'''mlp.fc1'''    )
			if "linear2" in name:
						lowerCAmelCase__ :    int				      =			name.replace('''linear2'''	,'''mlp.fc2'''    )
			if "norm1" in name and "layer_" not in name:
						lowerCAmelCase__ :    Tuple				      =			name.replace('''norm1'''	,'''layer_norm1'''    )
			if "norm2" in name and "layer_" not in name:
						lowerCAmelCase__ :    Any				      =			name.replace('''norm2'''	,'''layer_norm2'''    )

			return name



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Union[str, Any]    ):
			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__ :    Union[str, Any]				      =			orig_state_dict.pop(__UpperCamelCase    )

						if key.startswith('''clip_model'''    ) and "attn.in_proj" in key:
									lowerCAmelCase__ :    Union[str, Any]				      =			key.split('''.'''    )
									if "visual" in key:
												lowerCAmelCase__ :    Dict				      =			int(key_split[4]    )
												lowerCAmelCase__ :    int				      =			config.vision_config.hidden_size
												lowerCAmelCase__ :    Optional[int]				      =			'''vision_model'''
									else:
												lowerCAmelCase__ :    Any				      =			int(key_split[3]    )
												lowerCAmelCase__ :    int				      =			config.text_config.hidden_size
												lowerCAmelCase__ :    Union[str, Any]				      =			'''text_model'''

									if "weight" in key:
												lowerCAmelCase__ :    int				      =			val[:dim, :]
												lowerCAmelCase__ :    Optional[int]				      =			val[
												    dim : dim * 2, :
												]
												lowerCAmelCase__ :    Optional[int]				      =			val[-dim:, :]
									else:
												lowerCAmelCase__ :    Optional[int]				      =			val[:dim]
												lowerCAmelCase__ :    List[Any]				      =			val[dim : dim * 2]
												lowerCAmelCase__ :    Union[str, Any]				      =			val[-dim:]
						elif "self_attn" in key and "out_proj" not in key:
									lowerCAmelCase__ :    str				      =			key.split('''.'''    )
									lowerCAmelCase__ :    Tuple				      =			int(key_split[1]    )
									lowerCAmelCase__ :    Optional[Any]				      =			config.reduce_dim
									if "weight" in key:
												lowerCAmelCase__ :    Dict				      =			val[:dim, :]
												lowerCAmelCase__ :    int				      =			val[dim : dim * 2, :]
												lowerCAmelCase__ :    Dict				      =			val[-dim:, :]
									else:
												lowerCAmelCase__ :    Any				      =			val[:dim]
												lowerCAmelCase__ :    Optional[Any]				      =			val[dim : dim * 2]
												lowerCAmelCase__ :    Dict				      =			val[-dim:]
						else:
									lowerCAmelCase__ :    Any				      =			rename_key(__UpperCamelCase    )
									if "visual_projection" in new_name or "text_projection" in new_name:
												lowerCAmelCase__ :    Dict				      =			val.T
									lowerCAmelCase__ :    str				      =			val

			return orig_state_dict



def      _a     (   ):
			lowerCAmelCase__ :    int				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCAmelCase__ :    Dict				      =			Image.open(requests.get(__UpperCamelCase	,stream=__UpperCamelCase    ).raw    )
			return image



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Any	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  Union[str, Any]    ):
			lowerCAmelCase__ :    Optional[Any]				      =			get_clipseg_config(__UpperCamelCase    )
			lowerCAmelCase__ :    Optional[int]				      =			CLIPSegForImageSegmentation(__UpperCamelCase    )
			model.eval()

			lowerCAmelCase__ :    int				      =			torch.load(__UpperCamelCase	,map_location='''cpu'''    )

			# remove some keys
			for key in state_dict.copy().keys():
						if key.startswith('''model'''    ):
									state_dict.pop(__UpperCamelCase	,__UpperCamelCase    )

    # rename some keys
			lowerCAmelCase__ :    int				      =			convert_state_dict(__UpperCamelCase	,__UpperCamelCase    )
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Union[str, Any]				      =			model.load_state_dict(__UpperCamelCase	,strict=__UpperCamelCase    )

			if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
						raise ValueError('''Missing keys that are not expected: {}'''.format(__UpperCamelCase    )    )
			if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
						raise ValueError(f'''Unexpected keys: {unexpected_keys}'''    )

			lowerCAmelCase__ :    Dict				      =			ViTImageProcessor(size=352    )
			lowerCAmelCase__ :    Union[str, Any]				      =			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''    )
			lowerCAmelCase__ :    Any				      =			CLIPSegProcessor(image_processor=__UpperCamelCase	,tokenizer=__UpperCamelCase    )

			lowerCAmelCase__ :    Tuple				      =			prepare_img()
			lowerCAmelCase__ :    int				      =			['''a glass''', '''something to fill''', '''wood''', '''a jar''']

			lowerCAmelCase__ :    Any				      =			processor(text=__UpperCamelCase	,images=[image] * len(__UpperCamelCase    )	,padding='''max_length'''	,return_tensors='''pt'''    )

			with torch.no_grad():
						lowerCAmelCase__ :    int				      =			model(**__UpperCamelCase    )

			# verify values
			lowerCAmelCase__ :    Optional[int]				      =			torch.tensor([0.1_110, -0.1_882, 0.1_645]    )
			lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor([0.2_692, -0.7_197, -0.1_328]    )
			if model_name == "clipseg-rd64-refined":
						lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor(
						    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]    )
			elif model_name == "clipseg-rd64":
						lowerCAmelCase__ :    Any				      =			torch.tensor(
						    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]    )
			elif model_name == "clipseg-rd16":
						lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor(
						    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]    )
			else:
						raise ValueError(f'''Model name {model_name} not supported.'''    )

			assert torch.allclose(outputs.logits[0, :3, :3]	,__UpperCamelCase	,atol=1e-3    )
			assert torch.allclose(outputs.conditional_embeddings[0, :3]	,__UpperCamelCase	,atol=1e-3    )
			assert torch.allclose(outputs.pooled_output[0, :3]	,__UpperCamelCase	,atol=1e-3    )
			print('''Looks ok!'''    )

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and processor to {pytorch_dump_folder_path}'''    )
						model.save_pretrained(__UpperCamelCase    )
						processor.save_pretrained(__UpperCamelCase    )

			if push_to_hub:
						print(f'''Pushing model and processor for {model_name} to the hub'''    )
						model.push_to_hub(f'''CIDAS/{model_name}'''    )
						processor.push_to_hub(f'''CIDAS/{model_name}'''    )


if __name__ == "__main__":
							A__       :   Union[str, Any]   						=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""clipseg-rd64""",
							    type=str,
							    choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
							    help=(
							        """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
							        """ reduce dimension)"""
							    ),
							)
							parser.add_argument(
							    """--checkpoint_path""",
							    default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
							    type=str,
							    help=(
							        """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
							        """ the decoder weights."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)

							A__       :   Optional[Any]   						=					parser.parse_args()
							convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import DonutSwinModel
							from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=32 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=3 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=[1, 2, 1] ,	SCREAMING_SNAKE_CASE__=[2, 2, 4] ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=2.0 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.02 ,	SCREAMING_SNAKE_CASE__=1E-5 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=10 ,	SCREAMING_SNAKE_CASE__=8 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			parent
								lowerCAmelCase__ :    str				      =			batch_size
								lowerCAmelCase__ :    Tuple				      =			image_size
								lowerCAmelCase__ :    Union[str, Any]				      =			patch_size
								lowerCAmelCase__ :    int				      =			num_channels
								lowerCAmelCase__ :    Tuple				      =			embed_dim
								lowerCAmelCase__ :    Optional[int]				      =			depths
								lowerCAmelCase__ :    Tuple				      =			num_heads
								lowerCAmelCase__ :    Tuple				      =			window_size
								lowerCAmelCase__ :    Dict				      =			mlp_ratio
								lowerCAmelCase__ :    int				      =			qkv_bias
								lowerCAmelCase__ :    Dict				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Dict				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    Tuple				      =			drop_path_rate
								lowerCAmelCase__ :    int				      =			hidden_act
								lowerCAmelCase__ :    Union[str, Any]				      =			use_absolute_embeddings
								lowerCAmelCase__ :    Dict				      =			patch_norm
								lowerCAmelCase__ :    Any				      =			layer_norm_eps
								lowerCAmelCase__ :    Tuple				      =			initializer_range
								lowerCAmelCase__ :    Optional[Any]				      =			is_training
								lowerCAmelCase__ :    str				      =			scope
								lowerCAmelCase__ :    List[str]				      =			use_labels
								lowerCAmelCase__ :    Optional[Any]				      =			type_sequence_label_size
								lowerCAmelCase__ :    int				      =			encoder_stride
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								lowerCAmelCase__ :    Tuple				      =			None
								if self.use_labels:
											lowerCAmelCase__ :    Tuple				      =			ids_tensor([self.batch_size] ,	self.type_sequence_label_size    )

								lowerCAmelCase__ :    Tuple				      =			self.get_config()

								return config, pixel_values, labels
					def lowercase_			(		self    ):
								"""simple docstring"""







								return DonutSwinConfig(
								    image_size=self.image_size ,	patch_size=self.patch_size ,	num_channels=self.num_channels ,	embed_dim=self.embed_dim ,	depths=self.depths ,	num_heads=self.num_heads ,	window_size=self.window_size ,	mlp_ratio=self.mlp_ratio ,	qkv_bias=self.qkv_bias ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	drop_path_rate=self.drop_path_rate ,	hidden_act=self.hidden_act ,	use_absolute_embeddings=self.use_absolute_embeddings ,	path_norm=self.patch_norm ,	layer_norm_eps=self.layer_norm_eps ,	initializer_range=self.initializer_range ,	encoder_stride=self.encoder_stride ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel(config=SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()
								lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
								lowerCAmelCase__ :    int				      =			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

								self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, expected_seq_len, expected_dim)    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				
								) :    Optional[Any]				      =			config_and_inputs
								lowerCAmelCase__ :    Union[str, Any]				      =			{'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class 						lowercase       (  __UpperCamelCase	,		__UpperCamelCase	,		unittest.TestCase					):
					__a					=    (DonutSwinModel,) if is_torch_available() else ()
					__a					=    {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
					__a					=    True

					__a					=    False
					__a					=    False
					__a					=    False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			DonutSwinModelTester(self    )
								lowerCAmelCase__ :    Union[str, Any]				      =			ConfigTester(self ,	config_class=SCREAMING_SNAKE_CASE__ ,	embed_dim=37    )
					def lowercase_			(		self    ):
								"""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 lowercase_			(		self    ):
								"""simple docstring"""







								return
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								pass
					def lowercase_			(		self    ):
								"""simple docstring"""







								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(SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(model.get_input_embeddings() ,	(nn.Module)    )
											lowerCAmelCase__ :    Dict				      =			model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__ ,	nn.Linear    )    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Tuple				      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Union[str, Any]				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Optional[Any]				      =			inspect.signature(model.forward    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase__ :    Tuple				      =			[*signature.parameters.keys()]

											lowerCAmelCase__ :    List[str]				      =			['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    List[Any]				      =			True

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    int				      =			True
											lowerCAmelCase__ :    Union[str, Any]				      =			False
											lowerCAmelCase__ :    Tuple				      =			True
											lowerCAmelCase__ :    str				      =			model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
														lowerCAmelCase__ :    Dict				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
											lowerCAmelCase__ :    List[Any]				      =			outputs.attentions
											lowerCAmelCase__ :    str				      =			len(self.model_tester.depths    )
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											lowerCAmelCase__ :    Dict				      =			True
											lowerCAmelCase__ :    Optional[Any]				      =			config.window_size**2
											lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
														lowerCAmelCase__ :    Tuple				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
											lowerCAmelCase__ :    List[Any]				      =			outputs.attentions
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
											lowerCAmelCase__ :    Optional[int]				      =			len(SCREAMING_SNAKE_CASE__    )

											# Check attention is always last and order is fine
											lowerCAmelCase__ :    Any				      =			True
											lowerCAmelCase__ :    List[Any]				      =			True
											lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
														lowerCAmelCase__ :    Any				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

											if hasattr(self.model_tester ,	'''num_hidden_states_types'''    ):
														lowerCAmelCase__ :    Tuple				      =			self.model_tester.num_hidden_states_types
											else:
														# also another +1 for reshaped_hidden_states
														lowerCAmelCase__ :    str				      =			2
											self.assertEqual(out_len + added_hidden_states ,	len(SCREAMING_SNAKE_CASE__    )    )

											lowerCAmelCase__ :    List[str]				      =			outputs.attentions

											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()

								with torch.no_grad():
											lowerCAmelCase__ :    List[str]				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

								lowerCAmelCase__ :    List[str]				      =			outputs.hidden_states

								lowerCAmelCase__ :    Optional[Any]				      =			getattr(
								    self.model_tester ,	'''expected_num_hidden_layers''' ,	len(self.model_tester.depths    ) + 1    )
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								# DonutSwin has a different seq_length
								lowerCAmelCase__ :    int				      =			(
								    config.patch_size
								    if isinstance(config.patch_size ,	collections.abc.Iterable    )
								    else (config.patch_size, config.patch_size)
								)

								lowerCAmelCase__ :    Dict				      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)

								lowerCAmelCase__ :    List[str]				      =			outputs.reshaped_hidden_states
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			reshaped_hidden_states[0].shape
								lowerCAmelCase__ :    Dict				      =			(
								    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	height * width    ).permute(0 ,	2 ,	1    )
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Optional[int]				      =			(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,	collections.abc.Iterable    )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Any				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Union[str, Any]				      =			True

											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    Union[str, Any]				      =			3

								lowerCAmelCase__ :    int				      =			(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,	collections.abc.Iterable    )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								lowerCAmelCase__ :    str				      =			(
								    config.patch_size
								    if isinstance(config.patch_size ,	collections.abc.Iterable    )
								    else (config.patch_size, config.patch_size)
								)

								lowerCAmelCase__ :    Optional[int]				      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCAmelCase__ :    Any				      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Dict				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Union[str, Any]				      =			_config_zero_init(SCREAMING_SNAKE_CASE__    )
								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Union[str, Any]				      =			model_class(config=SCREAMING_SNAKE_CASE__    )
											for name, param in model.named_parameters():
														if "embeddings" not in name and param.requires_grad:
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item() ,	[0.0, 1.0] ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,	)



