






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						(						__lowerCamelCase       ) ->			Any:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			384
     if "tiny" in model_name:
          UpperCAmelCase__      :  Dict				      =			[3, 3, 9, 3]
          UpperCAmelCase__      :  int				      =			[96, 192, 384, 768]
     if "small" in model_name:
          UpperCAmelCase__      :  Optional[int]				      =			[3, 3, 27, 3]
          UpperCAmelCase__      :  Dict				      =			[96, 192, 384, 768]
     if "base" in model_name:
          UpperCAmelCase__      :  str				      =			[3, 3, 27, 3]
          UpperCAmelCase__      :  Optional[Any]				      =			[128, 256, 512, 1024]
          UpperCAmelCase__      :  int				      =			512
     if "large" in model_name:
          UpperCAmelCase__      :  List[str]				      =			[3, 3, 27, 3]
          UpperCAmelCase__      :  Tuple				      =			[192, 384, 768, 1536]
          UpperCAmelCase__      :  Any				      =			768
     if "xlarge" in model_name:
          UpperCAmelCase__      :  int				      =			[3, 3, 27, 3]
          UpperCAmelCase__      :  int				      =			[256, 512, 1024, 2048]
          UpperCAmelCase__      :  int				      =			1024

     # set label information
     UpperCAmelCase__      :  Tuple				      =			150
     UpperCAmelCase__      :  int				      =			"""huggingface/label-files"""
     UpperCAmelCase__      :  Tuple				      =			"""ade20k-id2label.json"""
     UpperCAmelCase__      :  int				      =			json.load(open(hf_hub_download(__lowerCamelCase ,							__lowerCamelCase ,							repo_type="""dataset"""       ) ,							"""r"""       )       )
     UpperCAmelCase__      :  Any				      =			{int(__lowerCamelCase       ): v for k, v in idalabel.items()}
     UpperCAmelCase__      :  Dict				      =			{v: k for k, v in idalabel.items()}

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

     return config





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[str]:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			[]

     # 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						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  str				      =			dct.pop(__lowerCamelCase       )
     UpperCAmelCase__      :  Union[str, Any]				      =			val





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  Tuple				      =			{
         """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""",
     }
     UpperCAmelCase__      :  List[Any]				      =			model_name_to_url[model_name]
     UpperCAmelCase__      :  List[Any]				      =			torch.hub.load_state_dict_from_url(__lowerCamelCase ,							map_location="""cpu"""       )["""state_dict"""]

     UpperCAmelCase__      :  Dict				      =			get_upernet_config(__lowerCamelCase       )
     UpperCAmelCase__      :  int				      =			UperNetForSemanticSegmentation(__lowerCamelCase       )
     model.eval()

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

     # rename keys
     UpperCAmelCase__      :  Optional[Any]				      =			create_rename_keys(__lowerCamelCase       )
     for src, dest in rename_keys:
          rename_key(__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )

     model.load_state_dict(__lowerCamelCase       )

     # verify on image
     UpperCAmelCase__      :  Tuple				      =			"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
     UpperCAmelCase__      :  Tuple				      =			Image.open(requests.get(__lowerCamelCase ,							stream=__lowerCamelCase       ).raw       ).convert("""RGB"""       )

     UpperCAmelCase__      :  Dict				      =			SegformerImageProcessor()
     UpperCAmelCase__      :  Union[str, Any]				      =			processor(__lowerCamelCase ,							return_tensors="""pt"""       ).pixel_values

     with torch.no_grad():
          UpperCAmelCase__      :  Any				      =			model(__lowerCamelCase       )

     if model_name == "upernet-convnext-tiny":
          UpperCAmelCase__      :  Optional[Any]				      =			torch.tensor(
              [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
     elif model_name == "upernet-convnext-small":
          UpperCAmelCase__      :  Optional[Any]				      =			torch.tensor(
              [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
     elif model_name == "upernet-convnext-base":
          UpperCAmelCase__      :  int				      =			torch.tensor(
              [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
     elif model_name == "upernet-convnext-large":
          UpperCAmelCase__      :  List[Any]				      =			torch.tensor(
              [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
     elif model_name == "upernet-convnext-xlarge":
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.tensor(
              [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
     print("""Logits:""" ,							outputs.logits[0, 0, :3, :3]       )
     assert torch.allclose(outputs.logits[0, 0, :3, :3] ,							__lowerCamelCase ,							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(__lowerCamelCase       )
          print(F"Saving processor to {pytorch_dump_folder_path}"       )
          processor.save_pretrained(__lowerCamelCase       )

     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__":
      SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				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."""
      )

      SCREAMING_SNAKE_CASE__		:							List[Any] 				=				parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=2     ,				help='Column of the dataset csv file with example ids.'   )
         train_parser.add_argument(
             '--skip_first_row'     ,				action='store_true'     ,				help='Skip the first row of the csv file (headers).'   )

         train_parser.add_argument('--validation_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0.1     ,				help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'     ,				)

         train_parser.add_argument('--output'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

         a__		=			'tf' if is_tf_available() else 'torch'

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         a__		=			args.output

         a__		=			args.column_label
         a__		=			args.column_text
         a__		=			args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'   )
         if args.task == "text_classification":
             a__		=			TextClassificationPipeline.from_pretrained(args.model   )
         elif args.task == "token_classification":
             raise NotImplementedError
         elif args.task == "question_answering":
             raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'   )
         a__		=			Processor.create_from_csv(
             args.train_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)
         a__		=			None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'   )
             a__		=			Processor.create_from_csv(
                 args.validation_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)

         a__		=			args.validation_split
         a__		=			args.train_batch_size
         a__		=			args.valid_batch_size
         a__		=			args.learning_rate
         a__		=			args.adam_epsilon



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         self.pipeline.fit(
             self.train_dataset     ,				validation_data=self.valid_dataset     ,				validation_split=self.validation_split     ,				learning_rate=self.learning_rate     ,				adam_epsilon=self.adam_epsilon     ,				train_batch_size=self.train_batch_size     ,				valid_batch_size=self.valid_batch_size     ,				)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output   )


