"""simple docstring"""





import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     Tuple:
							_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     384
							if "tiny" in model_name:
														_SCREAMING_SNAKE_CASE		:    Dict      	=     [3, 3, 9, 3]
														_SCREAMING_SNAKE_CASE		:    int      	=     [96, 192, 384, 768]
							if "small" in model_name:
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     [3, 3, 27, 3]
														_SCREAMING_SNAKE_CASE		:    Any      	=     [96, 192, 384, 768]
							if "base" in model_name:
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     [3, 3, 27, 3]
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     [128, 256, 512, 1_024]
														_SCREAMING_SNAKE_CASE		:    str      	=     512
							if "large" in model_name:
														_SCREAMING_SNAKE_CASE		:    Any      	=     [3, 3, 27, 3]
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     [192, 384, 768, 1_536]
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     768
							if "xlarge" in model_name:
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     [3, 3, 27, 3]
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     [256, 512, 1_024, 2_048]
														_SCREAMING_SNAKE_CASE		:    int      	=     1_024

							# set label information
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     150
							_SCREAMING_SNAKE_CASE		:    Any      	=     """huggingface/label-files"""
							_SCREAMING_SNAKE_CASE		:    str      	=     """ade20k-id2label.json"""
							_SCREAMING_SNAKE_CASE		:    int      	=     json.load(open(hf_hub_download(UpperCamelCase__  ,						UpperCamelCase__  ,						repo_type="""dataset"""	)  ,						"""r"""	)	)
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     {int(UpperCamelCase__	): v for k, v in idalabel.items()}
							_SCREAMING_SNAKE_CASE		:    int      	=     {v: k for k, v in idalabel.items()}

							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     ConvNextConfig(
							    depths=UpperCamelCase__  ,						hidden_sizes=UpperCamelCase__  ,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
							_SCREAMING_SNAKE_CASE		:    int      	=     UperNetConfig(
							    backbone_config=UpperCamelCase__  ,						auxiliary_in_channels=UpperCamelCase__  ,						num_labels=UpperCamelCase__  ,						idalabel=UpperCamelCase__  ,						labelaid=UpperCamelCase__  ,						)

							return config



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     Optional[Any]:
							_SCREAMING_SNAKE_CASE		:    int      	=     []

							# fmt: off
							# stem
							rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")	)
							rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")	)
							rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")	)
							rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma""", F"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.norm.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.norm.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")	)
																					rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")	)
														if i > 0:
																					rename_keys.append((F"""backbone.downsample_layers.{i}.0.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")	)
																					rename_keys.append((F"""backbone.downsample_layers.{i}.0.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")	)
																					rename_keys.append((F"""backbone.downsample_layers.{i}.1.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")	)
																					rename_keys.append((F"""backbone.downsample_layers.{i}.1.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""")	)

														rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")	)
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")	)

							# decode head
							rename_keys.extend(
							    [
							        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
							        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
							        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
							        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
							    ]	)
							# fmt: on

							return rename_keys



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)->     Tuple:
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     dct.pop(UpperCamelCase__	)
							_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     val



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)->     Tuple:
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     {
							    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
							    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
							    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
							    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
							    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
							}
							_SCREAMING_SNAKE_CASE		:    Any      	=     model_name_to_url[model_name]
							_SCREAMING_SNAKE_CASE		:    Tuple      	=     torch.hub.load_state_dict_from_url(UpperCamelCase__  ,						map_location="""cpu"""	)["""state_dict"""]

							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     get_upernet_config(UpperCamelCase__	)
							_SCREAMING_SNAKE_CASE		:    Any      	=     UperNetForSemanticSegmentation(UpperCamelCase__	)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     state_dict.pop(UpperCamelCase__	)
														if "bn" in key:
																					_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     key.replace("""bn"""  ,						"""batch_norm"""	)
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     val

							# rename keys
							_SCREAMING_SNAKE_CASE		:    Dict      	=     create_rename_keys(UpperCamelCase__	)
							for src, dest in rename_keys:
														rename_key(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__	)

							model.load_state_dict(UpperCamelCase__	)

							# verify on image
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     Image.open(requests.get(UpperCamelCase__  ,						stream=UpperCamelCase__	).raw	).convert("""RGB"""	)

							_SCREAMING_SNAKE_CASE		:    Tuple      	=     SegformerImageProcessor()
							_SCREAMING_SNAKE_CASE		:    Any      	=     processor(UpperCamelCase__  ,						return_tensors="""pt"""	).pixel_values

							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:    str      	=     model(UpperCamelCase__	)

							if model_name == "upernet-convnext-tiny":
														_SCREAMING_SNAKE_CASE		:    int      	=     torch.tensor(
														    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]	)
							elif model_name == "upernet-convnext-small":
														_SCREAMING_SNAKE_CASE		:    str      	=     torch.tensor(
														    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]	)
							elif model_name == "upernet-convnext-base":
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     torch.tensor(
														    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]	)
							elif model_name == "upernet-convnext-large":
														_SCREAMING_SNAKE_CASE		:    Dict      	=     torch.tensor(
														    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]	)
							elif model_name == "upernet-convnext-xlarge":
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     torch.tensor(
														    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]	)
							print("""Logits:"""  ,						outputs.logits[0, 0, :3, :3]	)
							assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,						UpperCamelCase__  ,						atol=1e-4	)
							print("""Looks ok!"""	)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
														model.save_pretrained(UpperCamelCase__	)
														print(F"""Saving processor to {pytorch_dump_folder_path}"""	)
														processor.save_pretrained(UpperCamelCase__	)

							if push_to_hub:
														print(F"""Pushing model and processor for {model_name} to hub"""	)
														model.push_to_hub(F"""openmmlab/{model_name}"""	)
														processor.push_to_hub(F"""openmmlab/{model_name}"""	)


if __name__ == "__main__":
			lowerCAmelCase_     			=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''upernet-convnext-tiny''',
			    type=str,
			    choices=[F"upernet-convnext-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
			    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
			)
			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.'''
			)

			lowerCAmelCase_     			=						parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                   ],)



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 							UpperCamelCase     (				self					):
        pass





