


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__			:      Union[str, Any]								=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__			:      Tuple								=[]
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
				)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
				)
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
				)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
				    )
				)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
				    )
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

				# q, k, v projections in self/cross-attention in decoder for conditional DETR
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
				)

				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
				)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
				)
				rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
				rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
				)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					state_dict.pop(A__				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					val






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					key.replace('backbone.0.body',      'backbone.conv_encoder.model'				)
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					value
						else:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					value

			return new_state_dict






def 						a__			(				A__,      A__=False				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					''
			if is_panoptic:
						SCREAMING_SNAKE_CASE_		:     Any					=					'conditional_detr.'

			# first: transformer encoder
			for i in range(6				):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
						SCREAMING_SNAKE_CASE_		:     str					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''				)
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE_		:     str					=					in_proj_weight[:2_5_6, :]
						SCREAMING_SNAKE_CASE_		:     Any					=					in_proj_bias[:2_5_6]
						SCREAMING_SNAKE_CASE_		:     int					=					in_proj_weight[2_5_6:5_1_2, :]
						SCREAMING_SNAKE_CASE_		:     Dict					=					in_proj_bias[2_5_6:5_1_2]
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					in_proj_weight[-2_5_6:, :]
						SCREAMING_SNAKE_CASE_		:     Any					=					in_proj_bias[-2_5_6:]






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     int					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_		:     int					=					Image.open(requests.get(A__,      stream=A__				).raw				)

			return im






@torch.no_grad()
def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'resnet101'
			if "dc5" in model_name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					True
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'panoptic' in model_name
			if is_panoptic:
						SCREAMING_SNAKE_CASE_		:     Any					=					2_5_0
			else:
						SCREAMING_SNAKE_CASE_		:     Any					=					9_1
						SCREAMING_SNAKE_CASE_		:     List[str]					=					'huggingface/label-files'
						SCREAMING_SNAKE_CASE_		:     List[str]					=					'coco-detection-id2label.json'
						SCREAMING_SNAKE_CASE_		:     Any					=					json.load(open(hf_hub_download(A__,      A__,      repo_type='dataset'				),      'r'				)				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					{int(A__				): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					idalabel
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					{v: k for k, v in idalabel.items()}

			# load image processor
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'coco_panoptic' if is_panoptic else 'coco_detection'
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					ConditionalDetrImageProcessor(format=A__				)

			# prepare image
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					prepare_img()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processor(images=A__,      return_tensors='pt'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					encoding['pixel_values']

			logger.info(F'''Converting model {model_name}...'''				)

			# load original model from torch hub
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.hub.load('DeppMeng/ConditionalDETR',      A__,      pretrained=A__				).eval()
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'conditional_detr.' + src
						rename_key(A__,      A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					rename_backbone_keys(A__				)
			# query, key and value matrices need special treatment
			read_in_q_k_v(A__,      is_panoptic=A__				)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			SCREAMING_SNAKE_CASE_		:     Tuple					=					'conditional_detr.model.' if is_panoptic else 'model.'
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith('conditional_detr'				)
									    and not key.startswith('class_labels_classifier'				)
									    and not key.startswith('bbox_predictor'				)
									):
												SCREAMING_SNAKE_CASE_		:     Any					=					state_dict.pop(A__				)
												SCREAMING_SNAKE_CASE_		:     str					=					val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												SCREAMING_SNAKE_CASE_		:     Dict					=					state_dict.pop(A__				)
												SCREAMING_SNAKE_CASE_		:     Any					=					val
									elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
												continue
									else:
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					state_dict.pop(A__				)
												SCREAMING_SNAKE_CASE_		:     Optional[int]					=					val
						else:
									if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
												SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict.pop(A__				)
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					val
    # finally, create HuggingFace model and load state dict
			SCREAMING_SNAKE_CASE_		:     Any					=					ConditionalDetrForSegmentation(A__				) if is_panoptic else ConditionalDetrForObjectDetection(A__				)
			model.load_state_dict(A__				)
			model.eval()
			model.push_to_hub(repo_id=A__,      organization='DepuMeng',      commit_message='Add model'				)
			# verify our conversion
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					conditional_detr(A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					model(A__				)
			assert torch.allclose(outputs.logits,      original_outputs['pred_logits'],      atol=1E-4				)
			assert torch.allclose(outputs.pred_boxes,      original_outputs['pred_boxes'],      atol=1E-4				)
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks,      original_outputs['pred_masks'],      atol=1E-4				)

			# Save model and image processor
			logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
			Path(A__				).mkdir(exist_ok=A__				)
			model.save_pretrained(A__				)
			image_processor.save_pretrained(A__				)


if __name__ == "__main__":
				lowerCAmelCase__			:      int								=argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='conditional_detr_resnet50',
				    type=str,
				    help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=3			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_0_0_0			,						lowerCAmelCase__=[3, 3, 6, 4]			,						lowerCAmelCase__=[4_8, 5_6, 1_1_2, 2_2_0]			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_channels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					is_training
										SCREAMING_SNAKE_CASE_		:     str					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     int					=					num_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					layer_depths
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					embed_dims






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_config()

										return config, pixel_values, labels






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return SwiftFormerConfig(
										    depths=self.layer_depths			,						embed_dims=self.embed_dims			,						mlp_ratio=4			,						downsamples=[True, True, True, True]			,						hidden_act='gelu'			,						num_labels=self.num_labels			,						down_patch_size=3			,						down_stride=2			,						down_pad=1			,						drop_rate=0.0			,						drop_path_rate=0.0			,						use_layer_scale=lowerCAmelCase__			,						layer_scale_init_value=1E-5			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerModel(config=lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     int					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7)       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.num_labels
										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(lowerCAmelCase__			,						labels=lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()

										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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







@require_torch
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      =						(
							    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     str					=					ConfigTester(
										    self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__			,						hidden_size=3_7			,						num_attention_heads=1_2			,						num_hidden_layers=1_2			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__			,						nn.Linear       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE_		:     Tuple					=					['pixel_values']
													self.assertListEqual(arg_names[:1]			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SwiftFormerModel.from_pretrained(lowerCAmelCase__       )
													self.assertIsNotNone(lowerCAmelCase__       )






							@unittest.skip(reason='SwiftFormer does not output attentions'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Tuple					=					model_class(lowerCAmelCase__       )
													model.to(lowerCAmelCase__       )
													model.eval()

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:     str					=					model(**self._prepare_for_class(lowerCAmelCase__			,						lowerCAmelCase__       )       )

													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs.hidden_states

													SCREAMING_SNAKE_CASE_		:     Any					=					8
													self.assertEqual(len(lowerCAmelCase__       )			,						lowerCAmelCase__       )  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(lowerCAmelCase__       )       ):
																self.assertEqual(
																    hidden_states[i].shape			,						torch.Size(
																        [
																            self.model_tester.batch_size,
																            self.model_tester.embed_dims[i // 2],
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																        ]       )			,						)

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					True
													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_		:     Dict					=					True

													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def _config_zero_init(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     List[str]					=					copy.deepcopy(lowerCAmelCase__       )
													for key in configs_no_init.__dict__.keys():
																if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						1E-10       )
																if isinstance(getattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )			,						lowerCAmelCase__       ):
																			SCREAMING_SNAKE_CASE_		:     int					=					_config_zero_init(getattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
													return configs_no_init

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

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






							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
			return image







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





							"""simple docstring"""







							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.default_image_processor
										SCREAMING_SNAKE_CASE_		:     Any					=					prepare_img()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processor(images=lowerCAmelCase__			,						return_tensors='pt'       ).to(lowerCAmelCase__       )

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model(**lowerCAmelCase__       )

										# verify the logits
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Size((1, 1_0_0_0)       )
										self.assertEqual(outputs.logits.shape			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )





