"""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()
lowercase__ 	=     logging.get_logger(__name__)







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

  return new_state_dict







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[str]:
  # for each of the encoder blocks:
  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)
      a__:					Optional[int]      							=     state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'       )
      a__:					Tuple      							=     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
      a__:					Union[str, Any]      							=     kv_weight[
          : config.hidden_sizes[i], :
      ]
      a__:					Optional[Any]      							=     kv_bias[: config.hidden_sizes[i]]
      a__:					Tuple      							=     kv_weight[
          config.hidden_sizes[i] :, :
      ]
      a__:					Optional[Any]      							=     kv_bias[config.hidden_sizes[i] :]







def 						__a       (     )     ->str:
  a__:					Optional[int]      							=     'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:					int      							=     Image.open(requests.get(_SCREAMING_SNAKE_CASE		,					stream=_SCREAMING_SNAKE_CASE       ).raw       )

  return image







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Any      							=     GLPNConfig(hidden_sizes=[64, 128, 320, 512]		,					decoder_hidden_size=64		,					depths=[3, 8, 27, 3]       )

  # load image processor (only resize + rescale)
  a__:					Union[str, Any]      							=     GLPNImageProcessor()

  # prepare image
  a__:					str      							=     prepare_img()
  a__:					Any      							=     image_processor(images=_SCREAMING_SNAKE_CASE		,					return_tensors='pt'       ).pixel_values

  logger.info('Converting model...'       )

  # load original state dict
  a__:					Dict      							=     torch.load(_SCREAMING_SNAKE_CASE		,					map_location=torch.device('cpu'       )       )

  # rename keys
  a__:					Optional[int]      							=     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
  a__:					int      							=     GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE       )
  model.load_state_dict(_SCREAMING_SNAKE_CASE       )
  model.eval()

  # forward pass
  a__:					Optional[Any]      							=     model(_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     outputs.predicted_depth

  # verify output
  if model_name is not None:
    if "nyu" in model_name:
      a__:					Optional[int]      							=     torch.tensor(
          [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]       )
    elif "kitti" in model_name:
      a__:					int      							=     torch.tensor(
          [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]       )
    else:
      raise ValueError(F'Unknown model name: {model_name}'       )

    a__:					Dict      							=     torch.Size([1, 480, 640]       )

    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__":
 lowercase__ 	=     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.',
 )
 lowercase__ 	=     parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase__ 	=     [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase = None)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     None
        a__:					Optional[Any]      							=     os.path.abspath(os.path.join('examples'   ,      'by_feature'))
        a__:					Optional[Any]      							=     os.path.abspath('examples')
        for item in os.listdir(lowercase):
          if item not in EXCLUDE_EXAMPLES:
            a__:					Dict      							=     os.path.join(lowercase   ,      lowercase)
            if os.path.isfile(lowercase) and ".py" in item_path:
              with self.subTest(
                  tested_script=lowercase   ,      feature_script=lowercase   ,      tested_section='main()' if parser_only else 'training_function()'   ,      ):
                a__:					List[Any]      							=     compare_against_test(
                    os.path.join(lowercase   ,      lowercase)   ,      lowercase   ,      lowercase   ,      lowercase)
                a__:					Dict      							=     '\n'.join(lowercase)
                if special_strings is not None:
                  for string in special_strings:
                    a__:					Union[str, Any]      							=     diff.replace(lowercase   ,      '')
                self.assertEqual(lowercase   ,      '')



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        self.one_complete_example('complete_nlp_example.py'   ,      lowercase)
        self.one_complete_example('complete_nlp_example.py'   ,      lowercase)



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





        '''simple docstring'''





        a__:					Dict      							=     os.path.abspath(os.path.join('examples'   ,      'cv_example.py'))
        a__:					Tuple      							=     [
            ' ' * 16 + '{\n\n',
            ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
            ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
            ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            ' ' * 20 + '"epoch": epoch,\n\n',
            ' ' * 16 + '},\n\n',
            ' ' * 16 + 'step=epoch,\n',
            ' ' * 12,
            ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
        ]
        self.one_complete_example('complete_cv_example.py'   ,      lowercase   ,      lowercase   ,      lowercase)
        self.one_complete_example('complete_cv_example.py'   ,      lowercase   ,      lowercase   ,      lowercase)







@mock.patch.dict(os.environ	,				{"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		False



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().setUpClass()
        a__:					Dict      							=     tempfile.mkdtemp()
        a__:					Optional[int]      							=     os.path.join(cls._tmpdir   ,      'default_config.yml')

        write_basic_config(save_location=cls.configPath)
        a__:					List[str]      							=     ['accelerate', 'launch', '--config_file', cls.configPath]



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().tearDownClass()
        shutil.rmtree(cls._tmpdir)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
        run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir   ,      'epoch_0')))



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





        '''simple docstring'''





        a__:					Any      							=     f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
        a__:					Tuple      							=     run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir   ,      'step_2')))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[Any]      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "epoch_0")}\n        '.split()
        a__:					Optional[int]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        self.assertNotIn('epoch 0:'   ,      lowercase)
        self.assertIn('epoch 1:'   ,      lowercase)



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





        '''simple docstring'''





        a__:					int      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "step_2")}\n        '.split()
        a__:					List[str]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        if torch.cuda.is_available():
          a__:					Union[str, Any]      							=     torch.cuda.device_count()
        else:
          a__:					Optional[int]      							=     1
        if num_processes > 1:
          self.assertNotIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)
        else:
          self.assertIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Dict      							=     '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ   ,      {'TESTING_MOCKED_DATALOADERS': '0'}):
          a__:					Union[str, Any]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
          a__:					Union[str, Any]      							=     re.findall('({.+})'   ,      lowercase)
          a__:					Dict      							=     [r for r in results if 'accuracy' in r][-1]
          a__:					Optional[Any]      							=     ast.literal_eval(lowercase)
          self.assertGreaterEqual(results['accuracy']   ,      0.75)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					str      							=     ['examples/by_feature/multi_process_metrics.py']
        run_command(self._launch_args + testargs)



      @require_trackers
      @mock.patch.dict(os.environ   ,      {'WANDB_MODE': 'offline'})
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        with tempfile.TemporaryDirectory() as tmpdir:
          a__:					List[str]      							=     f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
          run_command(self._launch_args + testargs)
          self.assertTrue(os.path.exists(os.path.join(lowercase   ,      'tracking')))



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					int      							=     ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs)



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





        '''simple docstring'''





        a__:					Union[str, Any]      							=     ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs)


