


'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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





def lowerCamelCase__     (		__lowerCamelCase			:				Dict				):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =DPTConfig(embedding_type='hybrid'				)

   if "large" in checkpoint_url:
      _UpperCAmelCase  :  List[str]				       =1_0_2_4
      _UpperCAmelCase  :  Any				       =4_0_9_6
      _UpperCAmelCase  :  List[str]				       =2_4
      _UpperCAmelCase  :  Optional[int]				       =1_6
      _UpperCAmelCase  :  List[str]				       =[5, 1_1, 1_7, 2_3]
      _UpperCAmelCase  :  str				       =[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
      _UpperCAmelCase  :  Dict				       =(1, 3_8_4, 3_8_4)

   if "nyu" or "midas" in checkpoint_url:
      _UpperCAmelCase  :  Optional[int]				       =7_6_8
      _UpperCAmelCase  :  Dict				       =[1, 1, 1, 0.5]
      _UpperCAmelCase  :  Optional[int]				       =[2_5_6, 5_1_2, 7_6_8, 7_6_8]
      _UpperCAmelCase  :  Dict				       =1_5_0
      _UpperCAmelCase  :  Tuple				       =1_6
      _UpperCAmelCase  :  Optional[Any]				       =(1, 3_8_4, 3_8_4)
      _UpperCAmelCase  :  Tuple				       =False
      _UpperCAmelCase  :  Any				       ='project'

   if "ade" in checkpoint_url:
      _UpperCAmelCase  :  str				       =True
      _UpperCAmelCase  :  str				       =7_6_8
      _UpperCAmelCase  :  Union[str, Any]				       =[1, 1, 1, 0.5]
      _UpperCAmelCase  :  Optional[Any]				       =1_5_0
      _UpperCAmelCase  :  Dict				       =1_6
      _UpperCAmelCase  :  Dict				       ='huggingface/label-files'
      _UpperCAmelCase  :  List[Any]				       ='ade20k-id2label.json'
      _UpperCAmelCase  :  str				       =json.load(open(cached_download(hf_hub_url(__lowerCamelCase			,       __lowerCamelCase			,       repo_type='dataset'				)				)			,       'r'				)				)
      _UpperCAmelCase  :  List[Any]				       ={int(__lowerCamelCase				): v for k, v in idalabel.items()}
      _UpperCAmelCase  :  Optional[Any]				       =idalabel
      _UpperCAmelCase  :  str				       ={v: k for k, v in idalabel.items()}
      _UpperCAmelCase  :  Tuple				       =[1, 1_5_0, 4_8_0, 4_8_0]

   return config, expected_shape





def lowerCamelCase__     (		__lowerCamelCase			:				Tuple				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[Any]				       =['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(__lowerCamelCase			,       __lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				int				):







   '''simple docstring'''




   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      _UpperCAmelCase  :  Optional[int]				       =name.replace('pretrained.model'			,       'dpt.encoder'				)
   if "pretrained.model" in name:
      _UpperCAmelCase  :  int				       =name.replace('pretrained.model'			,       'dpt.embeddings'				)
   if "patch_embed" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('patch_embed'			,       ''				)
   if "pos_embed" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('pos_embed'			,       'position_embeddings'				)
   if "attn.proj" in name:
      _UpperCAmelCase  :  Optional[int]				       =name.replace('attn.proj'			,       'attention.output.dense'				)
   if "proj" in name and "project" not in name:
      _UpperCAmelCase  :  Optional[int]				       =name.replace('proj'			,       'projection'				)
   if "blocks" in name:
      _UpperCAmelCase  :  Union[str, Any]				       =name.replace('blocks'			,       'layer'				)
   if "mlp.fc1" in name:
      _UpperCAmelCase  :  Tuple				       =name.replace('mlp.fc1'			,       'intermediate.dense'				)
   if "mlp.fc2" in name:
      _UpperCAmelCase  :  Any				       =name.replace('mlp.fc2'			,       'output.dense'				)
   if "norm1" in name and "backbone" not in name:
      _UpperCAmelCase  :  int				       =name.replace('norm1'			,       'layernorm_before'				)
   if "norm2" in name and "backbone" not in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('norm2'			,       'layernorm_after'				)
   if "scratch.output_conv" in name:
      _UpperCAmelCase  :  int				       =name.replace('scratch.output_conv'			,       'head'				)
   if "scratch" in name:
      _UpperCAmelCase  :  Tuple				       =name.replace('scratch'			,       'neck'				)
   if "layer1_rn" in name:
      _UpperCAmelCase  :  Any				       =name.replace('layer1_rn'			,       'convs.0'				)
   if "layer2_rn" in name:
      _UpperCAmelCase  :  str				       =name.replace('layer2_rn'			,       'convs.1'				)
   if "layer3_rn" in name:
      _UpperCAmelCase  :  Any				       =name.replace('layer3_rn'			,       'convs.2'				)
   if "layer4_rn" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('layer4_rn'			,       'convs.3'				)
   if "refinenet" in name:
      _UpperCAmelCase  :  str				       =int(name[len('neck.refinenet'				) : len('neck.refinenet'				) + 1]				)
      # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
      _UpperCAmelCase  :  Optional[Any]				       =name.replace(f"refinenet{layer_idx}"			,       f"fusion_stage.layers.{abs(layer_idx-4				)}"				)
   if "out_conv" in name:
      _UpperCAmelCase  :  str				       =name.replace('out_conv'			,       'projection'				)
   if "resConfUnit1" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('resConfUnit1'			,       'residual_layer1'				)
   if "resConfUnit2" in name:
      _UpperCAmelCase  :  Optional[Any]				       =name.replace('resConfUnit2'			,       'residual_layer2'				)
   if "conv1" in name:
      _UpperCAmelCase  :  Tuple				       =name.replace('conv1'			,       'convolution1'				)
   if "conv2" in name:
      _UpperCAmelCase  :  Dict				       =name.replace('conv2'			,       'convolution2'				)
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('pretrained.act_postprocess1.0.project.0'			,       'neck.reassemble_stage.readout_projects.0.0'				)
   if "pretrained.act_postprocess2.0.project.0" in name:
      _UpperCAmelCase  :  Dict				       =name.replace('pretrained.act_postprocess2.0.project.0'			,       'neck.reassemble_stage.readout_projects.1.0'				)
   if "pretrained.act_postprocess3.0.project.0" in name:
      _UpperCAmelCase  :  Optional[int]				       =name.replace('pretrained.act_postprocess3.0.project.0'			,       'neck.reassemble_stage.readout_projects.2.0'				)
   if "pretrained.act_postprocess4.0.project.0" in name:
      _UpperCAmelCase  :  Any				       =name.replace('pretrained.act_postprocess4.0.project.0'			,       'neck.reassemble_stage.readout_projects.3.0'				)

   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      _UpperCAmelCase  :  str				       =name.replace('pretrained.act_postprocess1.3'			,       'neck.reassemble_stage.layers.0.projection'				)
   if "pretrained.act_postprocess1.4" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('pretrained.act_postprocess1.4'			,       'neck.reassemble_stage.layers.0.resize'				)
   if "pretrained.act_postprocess2.3" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('pretrained.act_postprocess2.3'			,       'neck.reassemble_stage.layers.1.projection'				)
   if "pretrained.act_postprocess2.4" in name:
      _UpperCAmelCase  :  Union[str, Any]				       =name.replace('pretrained.act_postprocess2.4'			,       'neck.reassemble_stage.layers.1.resize'				)
   if "pretrained.act_postprocess3.3" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('pretrained.act_postprocess3.3'			,       'neck.reassemble_stage.layers.2.projection'				)
   if "pretrained.act_postprocess4.3" in name:
      _UpperCAmelCase  :  int				       =name.replace('pretrained.act_postprocess4.3'			,       'neck.reassemble_stage.layers.3.projection'				)
   if "pretrained.act_postprocess4.4" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('pretrained.act_postprocess4.4'			,       'neck.reassemble_stage.layers.3.resize'				)
   if "pretrained" in name:
      _UpperCAmelCase  :  Any				       =name.replace('pretrained'			,       'dpt'				)
   if "bn" in name:
      _UpperCAmelCase  :  int				       =name.replace('bn'			,       'batch_norm'				)
   if "head" in name:
      _UpperCAmelCase  :  int				       =name.replace('head'			,       'head.head'				)
   if "encoder.norm" in name:
      _UpperCAmelCase  :  Optional[int]				       =name.replace('encoder.norm'			,       'layernorm'				)
   if "auxlayer" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('auxlayer'			,       'auxiliary_head.head'				)
   if "backbone" in name:
      _UpperCAmelCase  :  Optional[Any]				       =name.replace('backbone'			,       'backbone.bit.encoder'				)

   if ".." in name:
      _UpperCAmelCase  :  Tuple				       =name.replace('..'			,       '.'				)

   if "stem.conv" in name:
      _UpperCAmelCase  :  str				       =name.replace('stem.conv'			,       'bit.embedder.convolution'				)
   if "blocks" in name:
      _UpperCAmelCase  :  Optional[int]				       =name.replace('blocks'			,       'layers'				)
   if "convolution" in name and "backbone" in name:
      _UpperCAmelCase  :  Tuple				       =name.replace('convolution'			,       'conv'				)
   if "layer" in name and "backbone" in name:
      _UpperCAmelCase  :  int				       =name.replace('layer'			,       'layers'				)
   if "backbone.bit.encoder.bit" in name:
      _UpperCAmelCase  :  Optional[Any]				       =name.replace('backbone.bit.encoder.bit'			,       'backbone.bit'				)
   if "embedder.conv" in name:
      _UpperCAmelCase  :  List[Any]				       =name.replace('embedder.conv'			,       'embedder.convolution'				)
   if "backbone.bit.encoder.stem.norm" in name:
      _UpperCAmelCase  :  List[str]				       =name.replace('backbone.bit.encoder.stem.norm'			,       'backbone.bit.embedder.norm'				)
   return name





def lowerCamelCase__     (		__lowerCamelCase			:				Dict			,       __lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   for i in range(config.num_hidden_layers				):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      _UpperCAmelCase  :  Any				       =state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"				)
      _UpperCAmelCase  :  Optional[int]				       =state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"				)
      # next, add query, keys and values (in that order) to the state dict
      _UpperCAmelCase  :  Optional[int]				       =in_proj_weight[: config.hidden_size, :]
      _UpperCAmelCase  :  List[str]				       =in_proj_bias[: config.hidden_size]
      _UpperCAmelCase  :  List[Any]				       =in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _UpperCAmelCase  :  List[str]				       =in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      _UpperCAmelCase  :  Optional[int]				       =in_proj_weight[
          -config.hidden_size :, :
      ]
      _UpperCAmelCase  :  Optional[int]				       =in_proj_bias[-config.hidden_size :]





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _UpperCAmelCase  :  str				       =Image.open(requests.get(__lowerCamelCase			,       stream=__lowerCamelCase				).raw				)
   return im





@torch.no_grad()
def lowerCamelCase__     (		__lowerCamelCase			:				Dict			,       __lowerCamelCase			:				Any			,       __lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				List[str]			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =get_dpt_config(__lowerCamelCase				)
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   _UpperCAmelCase  :  Optional[int]				       =torch.load(__lowerCamelCase			,       map_location='cpu'				)
   # remove certain keys
   remove_ignore_keys_(__lowerCamelCase				)
   # rename keys
   for key in state_dict.copy().keys():
      _UpperCAmelCase  :  int				       =state_dict.pop(__lowerCamelCase				)
      _UpperCAmelCase  :  Optional[Any]				       =val
   # read in qkv matrices
   read_in_q_k_v(__lowerCamelCase			,       __lowerCamelCase				)

   # load HuggingFace model
   _UpperCAmelCase  :  Optional[Any]				       =DPTForSemanticSegmentation(__lowerCamelCase				) if 'ade' in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase				)
   model.load_state_dict(__lowerCamelCase				)
   model.eval()

   # Check outputs on an image
   _UpperCAmelCase  :  Optional[int]				       =4_8_0 if 'ade' in checkpoint_url else 3_8_4
   _UpperCAmelCase  :  Optional[int]				       =DPTImageProcessor(size=__lowerCamelCase				)

   _UpperCAmelCase  :  int				       =prepare_img()
   _UpperCAmelCase  :  List[str]				       =image_processor(__lowerCamelCase			,       return_tensors='pt'				)

   # forward pass
   _UpperCAmelCase  :  Any				       =model(**__lowerCamelCase				).logits if 'ade' in checkpoint_url else model(**__lowerCamelCase				).predicted_depth

   if show_prediction:
      _UpperCAmelCase  :  int				       =(
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1				)			,       size=(image.size[1], image.size[0])			,       mode='bicubic'			,       align_corners=__lowerCamelCase			,       )
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 2_5_5				).show()

   if pytorch_dump_folder_path is not None:
      Path(__lowerCamelCase				).mkdir(exist_ok=__lowerCamelCase				)
      print(f"Saving model to {pytorch_dump_folder_path}"				)
      model.save_pretrained(__lowerCamelCase				)
      print(f"Saving image processor to {pytorch_dump_folder_path}"				)
      image_processor.save_pretrained(__lowerCamelCase				)

   if push_to_hub:
      model.push_to_hub('ybelkada/dpt-hybrid-midas'				)
      image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'				)


if __name__ == "__main__":
 lowercase				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
     type=str,
     help='URL of the original DPT checkpoint you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     required=False,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 parser.add_argument(
     '--model_name',
     default='dpt-large',
     type=str,
     help='Name of the model, in case you\'re pushing to the hub.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 lowercase				=parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )





'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =CodeGenTokenizer
       UpperCAmelCase		 =CodeGenTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 ={"add_prefix_space": True}
       UpperCAmelCase		 =False






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




          '''simple docstring'''
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _UpperCAmelCase  :  Optional[Any]				       =[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          _UpperCAmelCase  :  Dict				       =dict(zip(snake_case    ,				range(len(snake_case))))
          _UpperCAmelCase  :  List[Any]				       =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          _UpperCAmelCase  :  List[Any]				       ={'unk_token': '<unk>'}

          _UpperCAmelCase  :  List[Any]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file'])
          _UpperCAmelCase  :  List[str]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write(json.dumps(snake_case) + '\n')
          with open(self.merges_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write('\n'.join(snake_case))






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    str:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'
          _UpperCAmelCase  :  int				       ='lower newer'
          return input_text, output_text






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =CodeGenTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map)
          _UpperCAmelCase  :  str				       ='lower newer'
          _UpperCAmelCase  :  Union[str, Any]				       =['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          _UpperCAmelCase  :  Any				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Any				       =tokens + [tokenizer.unk_token]
          _UpperCAmelCase  :  List[str]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






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




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  Tuple				       =self.get_tokenizer()
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'

          # Testing tokenization
          _UpperCAmelCase  :  List[str]				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids without special tokens
          _UpperCAmelCase  :  str				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids with special tokens
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  int				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing the unknown token
          _UpperCAmelCase  :  List[str]				       =tokens + [rust_tokenizer.unk_token]
          _UpperCAmelCase  :  Union[str, Any]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Any:




          '''simple docstring'''
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Optional[int]:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  Optional[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  List[str]				       ='This is a simple input'
                _UpperCAmelCase  :  Optional[Any]				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  int				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  Union[str, Any]				       =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






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




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='<pad>')

          # Simple input
          _UpperCAmelCase  :  List[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input looooooooong', 'This is a simple input']
          _UpperCAmelCase  :  List[str]				       =('This is a simple input', 'This is a pair')
          _UpperCAmelCase  :  Optional[int]				       =[
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          _UpperCAmelCase  :  List[Any]				       =tokenizer.pad_token_id

          _UpperCAmelCase  :  Union[str, Any]				       =tokenizer(snake_case    ,				padding='max_length'    ,				max_length=3_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(*snake_case    ,				padding='max_length'    ,				max_length=6_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]    ,				3_0)
          self.assertTrue(pad_token_id in out_s['input_ids'])
          self.assertTrue(0 in out_s['attention_mask'])

          # s2
          # test automatic padding
          self.assertEqual(out_sa['input_ids'].shape[-1]    ,				3_3)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['input_ids'][0])
          self.assertFalse(0 in out_sa['attention_mask'][0])
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['input_ids'][1])
          self.assertTrue(0 in out_sa['attention_mask'][1])

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['input_ids'].shape[-1]    ,				6_0)
          self.assertTrue(pad_token_id in out_p['input_ids'])
          self.assertTrue(0 in out_p['attention_mask'])

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['input_ids'].shape[-1]    ,				5_2)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['input_ids'][0])
          self.assertFalse(0 in out_pa['attention_mask'][0])
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['input_ids'][1])
          self.assertTrue(0 in out_pa['attention_mask'][1])






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




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ='$$$'
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				bos_token=snake_case    ,				add_bos_token=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']

          _UpperCAmelCase  :  int				       =tokenizer.bos_token_id

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer(snake_case)

          self.assertEqual(out_s.input_ids[0]    ,				snake_case)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.decode(out_s.input_ids)
          _UpperCAmelCase  :  Any				       =tokenizer.batch_decode(out_sa.input_ids)

          self.assertEqual(decode_s.split()[0]    ,				snake_case)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

          _UpperCAmelCase  :  Optional[Any]				       ='\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
          _UpperCAmelCase  :  Tuple				       ='\nif len_a > len_b:      result = a\nelse:      result = b'

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  List[str]				       =['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
          _UpperCAmelCase  :  Dict				       =tokenizer.decode(snake_case    ,				truncate_before_pattern=snake_case)
          self.assertEqual(snake_case    ,				snake_case)






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




          '''simple docstring'''
          pass

