import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
   import torch


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					ViTImageProcessor if is_vision_available() else None







 @property
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Dict:


   """simple docstring"""







   return self.image_processor_tester.prepare_image_processor_dict()







 def 		__UpperCAmelCase				(		self				:   int)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    (3, 32, 128)
   _UpperCamelCase           =    tempfile.mkdtemp()

   # fmt: off
   _UpperCamelCase           =    ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
   # fmt: on
   _UpperCamelCase           =    dict(zip(SCREAMING_SNAKE_CASE__							, range(len(SCREAMING_SNAKE_CASE__))))

   _UpperCamelCase           =    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["vocab_file"])
   with open(self.vocab_file							, "w"							, encoding="utf-8") as fp:
     fp.write(json.dumps(SCREAMING_SNAKE_CASE__) + "\n")

   _UpperCamelCase           =    {
       "do_normalize": False,
       "do_resize": True,
       "image_processor_type": "ViTImageProcessor",
       "resample": 3,
       "size": {"height": 32, "width": 128},
   }
   _UpperCamelCase           =    os.path.join(self.tmpdirname							, SCREAMING_SNAKE_CASE__)
   with open(self.image_processor_file							, "w"							, encoding="utf-8") as fp:
     json.dump(SCREAMING_SNAKE_CASE__							, SCREAMING_SNAKE_CASE__)







 def 		__UpperCAmelCase				(		self				:   int							, **lowercase_				:   int)						->	List[str]:


   """simple docstring"""







   return MgpstrTokenizer.from_pretrained(self.tmpdirname							, **SCREAMING_SNAKE_CASE__)







 def 		__UpperCAmelCase				(		self				:   str							, **lowercase_				:   str)						->	Dict:


   """simple docstring"""







   return ViTImageProcessor.from_pretrained(self.tmpdirname							, **SCREAMING_SNAKE_CASE__)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	List[Any]:


   """simple docstring"""







   shutil.rmtree(self.tmpdirname)







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


   """simple docstring"""







   _UpperCamelCase           =    np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta)

   _UpperCamelCase           =    Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__							, 0							, -1))

   return image_input







 def 		__UpperCAmelCase				(		self				:   str)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    self.get_tokenizer()
   _UpperCamelCase           =    self.get_image_processor()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)
   processor.save_pretrained(self.tmpdirname)
   _UpperCamelCase           =    MgpstrProcessor.from_pretrained(self.tmpdirname							, use_fast=SCREAMING_SNAKE_CASE__)

   self.assertEqual(processor.char_tokenizer.get_vocab()							, tokenizer.get_vocab())
   self.assertIsInstance(processor.char_tokenizer							, SCREAMING_SNAKE_CASE__)

   self.assertEqual(processor.image_processor.to_json_string()							, image_processor.to_json_string())
   self.assertIsInstance(processor.image_processor							, SCREAMING_SNAKE_CASE__)







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


   """simple docstring"""







   _UpperCamelCase           =    self.get_tokenizer()
   _UpperCamelCase           =    self.get_image_processor()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)
   processor.save_pretrained(self.tmpdirname)

   _UpperCamelCase           =    self.get_tokenizer(bos_token="(BOS)"							, eos_token="(EOS)")
   _UpperCamelCase           =    self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__							, padding_value=1.0)

   _UpperCamelCase           =    MgpstrProcessor.from_pretrained(
       self.tmpdirname							, bos_token="(BOS)"							, eos_token="(EOS)"							, do_normalize=SCREAMING_SNAKE_CASE__							, padding_value=1.0)

   self.assertEqual(processor.char_tokenizer.get_vocab()							, tokenizer_add_kwargs.get_vocab())
   self.assertIsInstance(processor.char_tokenizer							, SCREAMING_SNAKE_CASE__)

   self.assertEqual(processor.image_processor.to_json_string()							, image_processor_add_kwargs.to_json_string())
   self.assertIsInstance(processor.image_processor							, SCREAMING_SNAKE_CASE__)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    self.prepare_image_inputs()

   _UpperCamelCase           =    image_processor(SCREAMING_SNAKE_CASE__							, return_tensors="np")
   _UpperCamelCase           =    processor(images=SCREAMING_SNAKE_CASE__							, return_tensors="np")

   for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum()							, input_processor[key].sum()							, delta=1e-2)







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


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    "test"

   _UpperCamelCase           =    processor(text=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    tokenizer(SCREAMING_SNAKE_CASE__)
   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]							, encoded_processor[key])







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    "test"
   _UpperCamelCase           =    self.prepare_image_inputs()

   _UpperCamelCase           =    processor(text=SCREAMING_SNAKE_CASE__							, images=SCREAMING_SNAKE_CASE__)

   self.assertListEqual(list(inputs.keys())							, ["pixel_values", "labels"])

   # test if it raises when no input is passed
   with pytest.raises(SCREAMING_SNAKE_CASE__):
     processor()







 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

   _UpperCamelCase           =    processor.char_decode(SCREAMING_SNAKE_CASE__)
   _UpperCamelCase           =    tokenizer.batch_decode(SCREAMING_SNAKE_CASE__)
   _UpperCamelCase           =    [seq.replace(" "							, "") for seq in decoded_tok]

   self.assertListEqual(SCREAMING_SNAKE_CASE__							, SCREAMING_SNAKE_CASE__)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    None
   _UpperCamelCase           =    self.prepare_image_inputs()

   _UpperCamelCase           =    processor(text=SCREAMING_SNAKE_CASE__							, images=SCREAMING_SNAKE_CASE__)

   self.assertListEqual(list(inputs.keys())							, processor.model_input_names)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.get_image_processor()
   _UpperCamelCase           =    self.get_tokenizer()

   _UpperCamelCase           =    MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__							, image_processor=SCREAMING_SNAKE_CASE__)

   _UpperCamelCase           =    torch.randn(1							, 27							, 38)
   _UpperCamelCase           =    torch.randn(1							, 27							, 50257)
   _UpperCamelCase           =    torch.randn(1							, 27							, 30522)

   _UpperCamelCase           =    processor.batch_decode([char_input, bpe_input, wp_input])

   self.assertListEqual(list(results.keys())							, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"])






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()
_snake_case      = logging.get_logger(__name__)

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

     return new_state_dict

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     # 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)
               UpperCamelCase						    =					state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
               UpperCamelCase						    =					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
               UpperCamelCase						    =					kv_weight[
                   : config.hidden_sizes[i], :
               ]
               UpperCamelCase						    =					kv_bias[: config.hidden_sizes[i]]
               UpperCamelCase						    =					kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               UpperCamelCase						    =					kv_bias[config.hidden_sizes[i] :]

def  __lowerCamelCase					( )       ->      Any:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=None			)       ->      Tuple:
     UpperCamelCase						    =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,     decoder_hidden_size=64						,     depths=[3, 8, 27, 3]			)

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

     # prepare image
     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					image_processor(images=_lowercase						,     return_tensors='pt'			).pixel_values

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

     # load original state dict
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location=torch.device('cpu'			)			)

     # rename keys
     UpperCamelCase						    =					rename_keys(_lowercase			)

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

     # create HuggingFace model and load state dict
     UpperCamelCase						    =					GLPNForDepthEstimation(_lowercase			)
     model.load_state_dict(_lowercase			)
     model.eval()

     # forward pass
     UpperCamelCase						    =					model(_lowercase			)
     UpperCamelCase						    =					outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               UpperCamelCase						    =					torch.tensor(
                   [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]			)
          elif "kitti" in model_name:
               UpperCamelCase						    =					torch.tensor(
                   [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]			)
          else:
               raise ValueError(F'Unknown model name: {model_name}'			)

          UpperCamelCase						    =					torch.Size([1, 480, 640]			)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]						,     _lowercase						,     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(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add model'						,     use_temp_dir=_lowercase						,     )
          image_processor.push_to_hub(
              repo_path_or_name=Path(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add image processor'						,     use_temp_dir=_lowercase						,     )


if __name__ == "__main__":
 _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.''',
 )
 _snake_case      = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


