






import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case      :      Tuple             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  SwinConfig.from_pretrained(
						    'microsoft/swin-tiny-patch4-window7-224'      ,    out_features=['stage1', 'stage2', 'stage3', 'stage4']						)
						lowerCAmelCase__        =  MaskFormerConfig(backbone_config=UpperCamelCase_						)

						lowerCAmelCase__        =  'huggingface/label-files'
						if "ade20k-full" in model_name:
												# this should be ok
												lowerCAmelCase__        =  847
												lowerCAmelCase__        =  'maskformer-ade20k-full-id2label.json'
						elif "ade" in model_name:
												# this should be ok
												lowerCAmelCase__        =  150
												lowerCAmelCase__        =  'ade20k-id2label.json'
						elif "coco-stuff" in model_name:
												# this should be ok
												lowerCAmelCase__        =  171
												lowerCAmelCase__        =  'maskformer-coco-stuff-id2label.json'
						elif "coco" in model_name:
												# TODO
												lowerCAmelCase__        =  133
												lowerCAmelCase__        =  'coco-panoptic-id2label.json'
						elif "cityscapes" in model_name:
												# this should be ok
												lowerCAmelCase__        =  19
												lowerCAmelCase__        =  'cityscapes-id2label.json'
						elif "vistas" in model_name:
												# this should be ok
												lowerCAmelCase__        =  65
												lowerCAmelCase__        =  'mapillary-vistas-id2label.json'

						lowerCAmelCase__        =  json.load(open(hf_hub_download(UpperCamelCase_      ,    UpperCamelCase_      ,    repo_type='dataset'						)      ,    'r'						)						)
						lowerCAmelCase__        =  {int(UpperCamelCase_						): v for k, v in idalabel.items()}

						return config







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						# stem
						# fmt: off
						rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')						)
						rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')						)
						rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')						)
						rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')						)
						# stages
						for i in range(len(config.backbone_config.depths						)						):
												for j in range(config.backbone_config.depths[i]						):
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")						)

												if i < 3:
																		rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")						)
																		rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")						)
												rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")						)
												rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")						)

						# FPN
						rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')						)
						rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')						)
						rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')						)
						for source_index, target_index in zip(range(3      ,    0      ,    -1						)      ,    range(0      ,    3						)						):
												rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")						)
												rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")						)
												rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")						)
												rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")						)
												rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")						)
												rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")						)
						rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')						)
						rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')						)

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers						):
												# self-attention out projection
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")						)
												# cross-attention out projection
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")						)
												# MLP 1
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")						)
												# MLP 2
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")						)
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")						)
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")						)
												# layernorm 3 (final layernorm)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")						)

						rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')						)
						rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')						)

						# heads on top
						rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')						)

						rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')						)
						rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')						)

						rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')						)
						rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')						)

						for i in range(3						):
												rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")						)
												rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")						)
						# fmt: on

						return rename_keys







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
						lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Dict						)					->				Dict:

						"""simple docstring"""



						lowerCAmelCase__        =  [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
						for i in range(len(backbone_config.depths						)						):
												lowerCAmelCase__        =  num_features[i]
												for j in range(backbone_config.depths[i]						):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		lowerCAmelCase__        =  state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"						)
																		lowerCAmelCase__        =  state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"						)
																		# next, add query, keys and values (in that order) to the state dict
																		lowerCAmelCase__        =  in_proj_weight[:dim, :]
																		lowerCAmelCase__        =  in_proj_bias[: dim]
																		lowerCAmelCase__        =  in_proj_weight[
																		    dim : dim * 2, :
																		]
																		lowerCAmelCase__        =  in_proj_bias[
																		    dim : dim * 2
																		]
																		lowerCAmelCase__        =  in_proj_weight[
																		    -dim :, :
																		]
																		lowerCAmelCase__        =  in_proj_bias[-dim :]
																		# fmt: on







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers						):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"						)
												lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"						)
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__        =  in_proj_weight[: hidden_size, :]
												lowerCAmelCase__        =  in_proj_bias[:config.hidden_size]
												lowerCAmelCase__        =  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCAmelCase__        =  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCAmelCase__        =  in_proj_weight[-hidden_size :, :]
												lowerCAmelCase__        =  in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"						)
												lowerCAmelCase__        =  state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"						)
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__        =  in_proj_weight[: hidden_size, :]
												lowerCAmelCase__        =  in_proj_bias[:config.hidden_size]
												lowerCAmelCase__        =  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCAmelCase__        =  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCAmelCase__        =  in_proj_weight[-hidden_size :, :]
												lowerCAmelCase__        =  in_proj_bias[-hidden_size :]
						# fmt: on







def 		_UpperCamelCase				(      )					->				torch.Tensor:

						"""simple docstring"""



						lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
						return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   bool = False						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  get_maskformer_config(UpperCamelCase_						)

						# load original state_dict
						with open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  pickle.load(UpperCamelCase_						)
						lowerCAmelCase__        =  data['model']

						# for name, param in state_dict.items():
						#     print(name, param.shape)

						# rename keys
						lowerCAmelCase__        =  create_rename_keys(UpperCamelCase_						)
						for src, dest in rename_keys:
												rename_key(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						read_in_swin_q_k_v(UpperCamelCase_      ,    config.backbone_config						)
						read_in_decoder_q_k_v(UpperCamelCase_      ,    UpperCamelCase_						)

						# update to torch tensors
						for key, value in state_dict.items():
												lowerCAmelCase__        =  torch.from_numpy(UpperCamelCase_						)

						# load 🤗 model
						lowerCAmelCase__        =  MaskFormerForInstanceSegmentation(UpperCamelCase_						)
						model.eval()

						for name, param in model.named_parameters():
												print(UpperCamelCase_      ,    param.shape						)

						lowerCAmelCase__  ,    lowerCAmelCase__        =  model.load_state_dict(UpperCamelCase_      ,    strict=UpperCamelCase_						)
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(UpperCamelCase_						) == 0, F"Unexpected keys: {unexpected_keys}"

						# verify results
						lowerCAmelCase__        =  prepare_img()
						if "vistas" in model_name:
												lowerCAmelCase__        =  65
						elif "cityscapes" in model_name:
												lowerCAmelCase__        =  6_5535
						else:
												lowerCAmelCase__        =  255
						lowerCAmelCase__        =  True if 'ade' in model_name else False
						lowerCAmelCase__        =  MaskFormerImageProcessor(ignore_index=UpperCamelCase_      ,    reduce_labels=UpperCamelCase_						)

						lowerCAmelCase__        =  image_processor(UpperCamelCase_      ,    return_tensors='pt'						)

						lowerCAmelCase__        =  model(**UpperCamelCase_						)

						print('Logits:'      ,    outputs.class_queries_logits[0, :3, :3]						)

						if model_name == "maskformer-swin-tiny-ade":
												lowerCAmelCase__        =  torch.tensor(
												    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]						)
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,    UpperCamelCase_      ,    atol=1e-4						)
						print('Looks ok!'						)

						if pytorch_dump_folder_path is not None:
												print(F"Saving model and image processor to {pytorch_dump_folder_path}"						)
												Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
												model.save_pretrained(UpperCamelCase_						)
												image_processor.save_pretrained(UpperCamelCase_						)

						if push_to_hub:
												print('Pushing model and image processor to the hub...'						)
												model.push_to_hub(F"nielsr/{model_name}"						)
												image_processor.push_to_hub(F"nielsr/{model_name}"						)


if __name__ == "__main__":
					__snake_case      :      Optional[int]             =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""maskformer-swin-tiny-ade""",
					    type=str,
					    help=("""Name of the MaskFormer model you'd like to convert""",),
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
					    type=str,
					    help="""Path to the original state dict (.pth file).""",
					)
					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."""
					)

					__snake_case      :      Union[str, Any]             =       parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)













from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=3       ,  _UpperCamelCase=32       ,  _UpperCamelCase=3       ,  _UpperCamelCase=10       ,  _UpperCamelCase=[10, 20, 30, 40]       ,  _UpperCamelCase=[1, 1, 2, 1]       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase="relu"       ,  _UpperCamelCase=3       ,  _UpperCamelCase=None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  image_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  embeddings_size
												lowerCAmelCase__        =  hidden_sizes
												lowerCAmelCase__        =  depths
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  hidden_act
												lowerCAmelCase__        =  num_labels
												lowerCAmelCase__        =  scope
												lowerCAmelCase__        =  len(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""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.num_labels				)

												lowerCAmelCase__        =  self.get_config()

												return config, pixel_values, labels






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return ResNetConfig(
												    num_channels=self.num_channels       ,  embeddings_size=self.embeddings_size       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  hidden_act=self.hidden_act       ,  num_labels=self.num_labels       ,  image_size=self.image_size       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetModel(config=_UpperCamelCase				)
												lowerCAmelCase__        =  model(_UpperCamelCase				)
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.num_labels
												lowerCAmelCase__        =  TFResNetForImageClassification(_UpperCamelCase				)
												lowerCAmelCase__        =  model(_UpperCamelCase       ,  labels=_UpperCamelCase				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)







						def  UpperCamelCase__ (		self				):






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




@require_tf
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Any						=   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    List[str]						=   (
						    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   False
						_SCREAMING_SNAKE_CASE		:    int						=   False
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   False
						_SCREAMING_SNAKE_CASE		:    List[str]						=   False
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase       ,  has_text_modality=_UpperCamelCase				)






						def  UpperCamelCase__ (		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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return






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






												"""simple docstring"""
												pass






						@unittest.skip(reason='ResNet does not support input and output embeddings'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""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.call				)
																		# 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				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												def check_hidden_states_output(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  model_class(_UpperCamelCase				)
																		lowerCAmelCase__        =  model(**self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase				)				)

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

																		lowerCAmelCase__        =  self.model_tester.num_stages
																		self.assertEqual(len(_UpperCamelCase				)       ,  expected_num_stages + 1				)

																		# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]				)       ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,  )

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()
												lowerCAmelCase__        =  ['basic', 'bottleneck']
												for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																								lowerCAmelCase__        =  layer_type
																								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				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__        =  TFResNetModel.from_pretrained(_UpperCamelCase				)
																		self.assertIsNotNone(_UpperCamelCase				)







def 		_UpperCamelCase				(      )					->				List[str]:

						"""simple docstring"""



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


@require_tf
@require_vision
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return (
												    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
												    if is_vision_available()
												    else None
												)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

												lowerCAmelCase__        =  self.default_image_processor
												lowerCAmelCase__        =  prepare_img()
												lowerCAmelCase__        =  image_processor(images=_UpperCamelCase       ,  return_tensors='tf'				)

												# forward pass
												lowerCAmelCase__        =  model(**_UpperCamelCase				)

												# verify the logits
												lowerCAmelCase__        =  tf.TensorShape((1, 10_00)				)
												self.assertEqual(outputs.logits.shape       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  tf.constant([-11.10_69, -9.78_77, -8.37_77]				)

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





