


"""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__				:							Tuple						=     False
a__				:							Optional[int]						=     False




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return TrainCommand(lowerCAmelCase_   )




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				ArgumentParser   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				parser.add_parser("train"			,			help="CLI tool to train a model on a task."   )

						train_parser.add_argument(
						    "--train_data"			,			type=UpperCAmelCase__			,			required=UpperCAmelCase__			,			help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."			,			)
						train_parser.add_argument(
						    "--column_label"			,			type=UpperCAmelCase__			,			default=0			,			help="Column of the dataset csv file with example labels."   )
						train_parser.add_argument(
						    "--column_text"			,			type=UpperCAmelCase__			,			default=1			,			help="Column of the dataset csv file with example texts."   )
						train_parser.add_argument(
						    "--column_id"			,			type=UpperCAmelCase__			,			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=UpperCAmelCase__			,			default=""			,			help="path to validation dataset."   )
						train_parser.add_argument(
						    "--validation_split"			,			type=UpperCAmelCase__			,			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=UpperCAmelCase__			,			default="./"			,			help="path to saved the trained model."   )

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




	def __init__( self			:				List[str]			,			UpperCAmelCase__			:				Namespace   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				logging.get_logger("transformers-cli/training"   )

						__SCREAMING_SNAKE_CASE						   =				"tf" if is_tf_available() else "torch"

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

						__SCREAMING_SNAKE_CASE						   =				args.column_label
						__SCREAMING_SNAKE_CASE						   =				args.column_text
						__SCREAMING_SNAKE_CASE						   =				args.column_id

						self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""   )
						if args.task == "text_classification":
											__SCREAMING_SNAKE_CASE						   =				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}"""   )
						__SCREAMING_SNAKE_CASE						   =				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			,			)
						__SCREAMING_SNAKE_CASE						   =				None
						if args.validation_data:
											self.logger.info(F"""Loading validation dataset from {args.validation_data}"""   )
											__SCREAMING_SNAKE_CASE						   =				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			,			)

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




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[Any]:
						if self.framework == "tf":
											return self.run_tf()
						return self.run_torch()




	def   UpperCAmelCase_      ( self			:				int   )       ->	str:
						raise NotImplementedError




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







"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       OrderedDict()
      for key, value in state_dict.items():
            if key.startswith("""module.encoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.encoder"""							,						"""glpn.encoder"""			)
            if key.startswith("""module.decoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.decoder"""							,						"""decoder.stages"""			)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase  						=       key[key.find("""patch_embed"""			) + len("""patch_embed"""			)]
                  lowerCAmelCase  						=       key.replace(F'patch_embed{idx}'							,						F'patch_embeddings.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "norm" in key:
                  lowerCAmelCase  						=       key.replace("""norm"""							,						"""layer_norm"""			)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase  						=       key[key.find("""glpn.encoder.layer_norm"""			) + len("""glpn.encoder.layer_norm"""			)]
                  lowerCAmelCase  						=       key.replace(F'layer_norm{idx}'							,						F'layer_norm.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "layer_norm1" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm1"""							,						"""layer_norm_1"""			)
            if "layer_norm2" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm2"""							,						"""layer_norm_2"""			)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase  						=       key[key.find("""block"""			) + len("""block"""			)]
                  lowerCAmelCase  						=       key.replace(F'block{idx}'							,						F'block.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "attn.q" in key:
                  lowerCAmelCase  						=       key.replace("""attn.q"""							,						"""attention.self.query"""			)
            if "attn.proj" in key:
                  lowerCAmelCase  						=       key.replace("""attn.proj"""							,						"""attention.output.dense"""			)
            if "attn" in key:
                  lowerCAmelCase  						=       key.replace("""attn"""							,						"""attention.self"""			)
            if "fc1" in key:
                  lowerCAmelCase  						=       key.replace("""fc1"""							,						"""dense1"""			)
            if "fc2" in key:
                  lowerCAmelCase  						=       key.replace("""fc2"""							,						"""dense2"""			)
            if "linear_pred" in key:
                  lowerCAmelCase  						=       key.replace("""linear_pred"""							,						"""classifier"""			)
            if "linear_fuse" in key:
                  lowerCAmelCase  						=       key.replace("""linear_fuse.conv"""							,						"""linear_fuse"""			)
                  lowerCAmelCase  						=       key.replace("""linear_fuse.bn"""							,						"""batch_norm"""			)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase  						=       key[key.find("""linear_c"""			) + len("""linear_c"""			)]
                  lowerCAmelCase  						=       key.replace(F'linear_c{idx}'							,						F'linear_c.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "bot_conv" in key:
                  lowerCAmelCase  						=       key.replace("""bot_conv"""							,						"""0.convolution"""			)
            if "skip_conv1" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv1"""							,						"""1.convolution"""			)
            if "skip_conv2" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv2"""							,						"""2.convolution"""			)
            if "fusion1" in key:
                  lowerCAmelCase  						=       key.replace("""fusion1"""							,						"""1.fusion"""			)
            if "fusion2" in key:
                  lowerCAmelCase  						=       key.replace("""fusion2"""							,						"""2.fusion"""			)
            if "fusion3" in key:
                  lowerCAmelCase  						=       key.replace("""fusion3"""							,						"""3.fusion"""			)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase  						=       key.replace("""conv"""							,						"""convolutional_layer"""			)
            if key.startswith("""module.last_layer_depth"""			):
                  lowerCAmelCase  						=       key.replace("""module.last_layer_depth"""							,						"""head.head"""			)
            lowerCAmelCase  						=       value

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      for i in range(config.num_encoder_blocks			):
            for j in range(config.depths[i]			):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'			)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]							,						decoder_hidden_size=64							,						depths=[3, 8, 27, 3]			)

      # load image processor (only resize + rescale)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

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

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       rename_keys(SCREAMING_SNAKE_CASE			)

      # key and value matrices need special treatment
      read_in_k_v(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # create HuggingFace model and load state dict
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

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

      # finally, push to hub if required
      if push_to_hub:
            logger.info("""Pushing model and image processor to the hub..."""			)
            model.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add model"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)
            image_processor.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add image processor"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)


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

       parser.add_argument(
           "--checkpoint_path",
           default=None,
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       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 to upload the model to the HuggingFace hub."
       )
       parser.add_argument(
           "--model_name",
           default="glpn-kitti",
           type=str,
           help="Name of the model in case you're pushing to the hub.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






