






import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a_							=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_							=    []
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}.multihead_attn.out_proj.weight",
	        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
	    )
	)
	rename_keys.append(
	    (
	        F"transformer.decoder.layers.{i}.multihead_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"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''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'''),
    ]
)


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				List[str]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						state_dict.pop(_lowerCamelCase   )
						lowerCAmelCase__       =						val


def        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		lowerCAmelCase__       =						key.replace("backbone.0.body"    ,   "backbone.conv_encoder.model"   )
																		lowerCAmelCase__       =						value
												else:
																		lowerCAmelCase__       =						value

						return new_state_dict


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						''

						# 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)
												lowerCAmelCase__       =						state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
												lowerCAmelCase__       =						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
												lowerCAmelCase__       =						in_proj_weight[:256, :]
												lowerCAmelCase__       =						in_proj_bias[:256]
												lowerCAmelCase__       =						in_proj_weight[256:512, :]
												lowerCAmelCase__       =						in_proj_bias[256:512]
												lowerCAmelCase__       =						in_proj_weight[-256:, :]
												lowerCAmelCase__       =						in_proj_bias[-256:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												lowerCAmelCase__       =						state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
												lowerCAmelCase__       =						state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"   )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__       =						in_proj_weight[:256, :]
												lowerCAmelCase__       =						in_proj_bias[:256]
												lowerCAmelCase__       =						in_proj_weight[256:512, :]
												lowerCAmelCase__       =						in_proj_bias[256:512]
												lowerCAmelCase__       =						in_proj_weight[-256:, :]
												lowerCAmelCase__       =						in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												lowerCAmelCase__       =						state_dict.pop(
												    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"   )
												lowerCAmelCase__       =						state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"   )
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												lowerCAmelCase__       =						in_proj_weight_cross_attn[:256, :]
												lowerCAmelCase__       =						in_proj_bias_cross_attn[:256]
												lowerCAmelCase__       =						in_proj_weight_cross_attn[256:512, :]
												lowerCAmelCase__       =						in_proj_bias_cross_attn[256:512]
												lowerCAmelCase__       =						in_proj_weight_cross_attn[-256:, :]
												lowerCAmelCase__       =						in_proj_bias_cross_attn[-256:]


def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Optional[Any]   ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						image.size
						lowerCAmelCase__       =						max(_lowerCamelCase    ,   _lowerCamelCase   )
						lowerCAmelCase__       =						800 if 'detection' in checkpoint_url else 1_000
						lowerCAmelCase__       =						target_max_size / current_max_size
						lowerCAmelCase__       =						image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

						return resized_image


def        _a  (     UpperCamelCase_					:				Tuple   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						F.to_tensor(_lowerCamelCase   )
						lowerCAmelCase__       =						F.normalize(_lowerCamelCase    ,   mean=[0.485, 0.456, 0.406]    ,   std=[0.229, 0.224, 0.225]   )
						return image


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any   ) ->   List[str]:
						"""simple docstring"""







						logger.info("Converting model..."   )

						# load original state dict
						lowerCAmelCase__       =						torch.hub.load_state_dict_from_url(_lowerCamelCase    ,   map_location="cpu"   )
						# rename keys
						for src, dest in rename_keys:
												rename_key(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase   )
						lowerCAmelCase__       =						rename_backbone_keys(_lowerCamelCase   )
						# query, key and value matrices need special treatment
						read_in_q_k_v(_lowerCamelCase   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						lowerCAmelCase__       =						'model.'
						for key in state_dict.copy().keys():
												if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																		lowerCAmelCase__       =						state_dict.pop(_lowerCamelCase   )
																		lowerCAmelCase__       =						val
    # create HuggingFace model and load state dict
						lowerCAmelCase__       =						TableTransformerConfig(
						    backbone="resnet18"    ,   mask_loss_coefficient=1    ,   dice_loss_coefficient=1    ,   ce_loss_coefficient=1    ,   bbox_loss_coefficient=5    ,   giou_loss_coefficient=2    ,   eos_coefficient=0.4    ,   class_cost=1    ,   bbox_cost=5    ,   giou_cost=2    ,   )

						if "detection" in checkpoint_url:
												lowerCAmelCase__       =						15
												lowerCAmelCase__       =						2
												lowerCAmelCase__       =						{0: 'table', 1: 'table rotated'}
												lowerCAmelCase__       =						idalabel
												lowerCAmelCase__       =						{v: k for k, v in idalabel.items()}
						else:
												lowerCAmelCase__       =						125
												lowerCAmelCase__       =						6
												lowerCAmelCase__       =						{
												    0: 'table',
												    1: 'table column',
												    2: 'table row',
												    3: 'table column header',
												    4: 'table projected row header',
												    5: 'table spanning cell',
												}
												lowerCAmelCase__       =						idalabel
												lowerCAmelCase__       =						{v: k for k, v in idalabel.items()}

						lowerCAmelCase__       =						DetrImageProcessor(
						    format="coco_detection"    ,   max_size=800 if "detection" in checkpoint_url else 1_000   )
						lowerCAmelCase__       =						TableTransformerForObjectDetection(_lowerCamelCase   )
						model.load_state_dict(_lowerCamelCase   )
						model.eval()

						# verify our conversion
						lowerCAmelCase__       =						'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
						lowerCAmelCase__       =						hf_hub_download(repo_id="nielsr/example-pdf"    ,   repo_type="dataset"    ,   filename=_lowerCamelCase   )
						lowerCAmelCase__       =						Image.open(_lowerCamelCase   ).convert("RGB"   )
						lowerCAmelCase__       =						normalize(resize(_lowerCamelCase    ,   _lowerCamelCase   )   ).unsqueeze(0   )

						lowerCAmelCase__       =						model(_lowerCamelCase   )

						if "detection" in checkpoint_url:
												lowerCAmelCase__       =						(1, 15, 3)
												lowerCAmelCase__       =						torch.tensor(
												    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
												lowerCAmelCase__       =						torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

						else:
												lowerCAmelCase__       =						(1, 125, 7)
												lowerCAmelCase__       =						torch.tensor(
												    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
												lowerCAmelCase__       =						torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :3, :3]    ,   _lowerCamelCase    ,   atol=1e-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,   _lowerCamelCase    ,   atol=1e-4   )
						print("Looks ok!"   )

						if pytorch_dump_folder_path is not None:
												# Save model and image processor
												logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."   )
												Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
												model.save_pretrained(_lowerCamelCase   )
												image_processor.save_pretrained(_lowerCamelCase   )

						if push_to_hub:
												# Push model to HF hub
												logger.info("Pushing model to the hub..."   )
												lowerCAmelCase__       =						(
												    'microsoft/table-transformer-detection'
												    if 'detection' in checkpoint_url
												    else 'microsoft/table-transformer-structure-recognition'
												)
												model.push_to_hub(_lowerCamelCase   )
												image_processor.push_to_hub(_lowerCamelCase   )


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
	    type=str,
	    choices=[
	        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
	        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
	    ],
	    help='''URL of the Table Transformer checkpoint 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.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)
	a_							=    parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor


class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "hidden_sizes"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_attention_heads"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_encoder_blocks"       )       )





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=64		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=[2, 2, 2, 2]		,    __UpperCAmelCase=[8, 4, 2, 1]		,    __UpperCAmelCase=[16, 32, 64, 128]		,    __UpperCAmelCase=[1, 4, 8, 16]		,    __UpperCAmelCase=[1, 2, 4, 8]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						num_encoder_blocks
							lowerCAmelCase__       =						sr_ratios
							lowerCAmelCase__       =						depths
							lowerCAmelCase__       =						hidden_sizes
							lowerCAmelCase__       =						downsampling_rates
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						hidden_act
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						scope

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





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

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

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

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							return SegformerConfig(
							    image_size=self.image_size		,    num_channels=self.num_channels		,    num_encoder_blocks=self.num_encoder_blocks		,    depths=self.depths		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							lowerCAmelCase__       =						lowerCAmelCase__       =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							self.parent.assertGreater(result.loss		,    0.0       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						torch.randint(0		,    1		,    (self.batch_size, self.image_size, self.image_size)       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertGreater(result.loss		,    0.0       )


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						config_and_inputs
							lowerCAmelCase__       =						{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": SegformerModel,
	        """image-classification""": SegformerForImageClassification,
	        """image-segmentation""": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a_         =True
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModelTester(self       )
							lowerCAmelCase__       =						SegformerConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase       )

	@unittest.skip("SegFormer does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						False
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													lowerCAmelCase__       =						sum(self.model_tester.depths       )
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

													# verify the last attentions (last block, last layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 32) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]       )		,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,    )
													lowerCAmelCase__       =						len(__UpperCAmelCase       )

													# Check attention is always last and order is fine
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													self.assertEqual(out_len + 1		,    len(__UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )
													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





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

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

													lowerCAmelCase__       =						outputs.hidden_states

													lowerCAmelCase__       =						self.model_tester.num_encoder_blocks
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]       )		,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]		,    )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							if not self.model_tester.is_training:
													return

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													if model_class in get_values(__UpperCAmelCase       ):
																			continue

													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.train()
													lowerCAmelCase__       =						self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase		,    return_labels=__UpperCAmelCase       )
													lowerCAmelCase__       =						model(**__UpperCAmelCase       ).loss
													loss.backward()

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						SegformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





def        _a  (     ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
							        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
							        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

	@slow
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained(
							    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
							        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
							        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-1       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						outputs.logits.detach().cpu()

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		,    target_sizes=[(500, 300)]       )
							lowerCAmelCase__       =						torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.Size((128, 128)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )






