






'''simple docstring'''
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 			_a(	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Optional[int]						='https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=Image.open(requests.get(lowerCAmelCase__,						stream=lowerCAmelCase__				).raw				).convert('''RGB'''				)

      return image





def 			_a(	UpperCamelCase__					:    str				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Tuple						=[]
      # fmt: off

      # vision encoder
      rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')				)
      rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')				)
      rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')				)
      rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')				)
      rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')				)
      rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')				)

      for i in range(config.vision_config.num_hidden_layers				):
            rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",)				)
            rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")				)
            rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")				)

      # QFormer
      rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')				)
      rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')				)

      # fmt: on
      return rename_keys





def 			_a(	UpperCamelCase__					:    List[Any],						UpperCamelCase__					:    Optional[int],						UpperCamelCase__					:    Tuple				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Optional[int]						=dct.pop(lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					List[Any]						=val





def 			_a(	UpperCamelCase__					:    Optional[Any],						UpperCamelCase__					:    Optional[int]				):




      '''simple docstring'''






      for i in range(config.vision_config.num_hidden_layers				):
            # read in original q and v biases
            SCREAMING_SNAKE_CASE__  :					int						=state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"				)
            SCREAMING_SNAKE_CASE__  :					List[str]						=state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"				)

            # next, set bias in the state dict
            SCREAMING_SNAKE_CASE__  :					List[Any]						=torch.cat((q_bias, torch.zeros_like(lowerCAmelCase__,						requires_grad=lowerCAmelCase__				), v_bias)				)
            SCREAMING_SNAKE_CASE__  :					Optional[int]						=qkv_bias





def 			_a(	UpperCamelCase__					:    str,						UpperCamelCase__					:    Tuple				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					int						=3_6_4 if 'coco' in model_name else 2_2_4
      SCREAMING_SNAKE_CASE__  :					Any						=BlipaVisionConfig(image_size=lowerCAmelCase__				).to_dict()

      # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
      # seems like flan-T5 models don't have bos_token_id properly set?
      if "opt-2.7b" in model_name:
            SCREAMING_SNAKE_CASE__  :					Optional[int]						=OPTConfig.from_pretrained('''facebook/opt-2.7b''',						eos_token_id=lowerCAmelCase__				).to_dict()
      elif "opt-6.7b" in model_name:
            SCREAMING_SNAKE_CASE__  :					str						=OPTConfig.from_pretrained('''facebook/opt-6.7b''',						eos_token_id=lowerCAmelCase__				).to_dict()
      elif "t5-xl" in model_name:
            SCREAMING_SNAKE_CASE__  :					Dict						=TaConfig.from_pretrained('''google/flan-t5-xl''',						dense_act_fn='''gelu''',						bos_token_id=1				).to_dict()
      elif "t5-xxl" in model_name:
            SCREAMING_SNAKE_CASE__  :					Any						=TaConfig.from_pretrained('''google/flan-t5-xxl''',						dense_act_fn='''gelu''',						bos_token_id=1				).to_dict()

      SCREAMING_SNAKE_CASE__  :					Any						=BlipaConfig(vision_config=lowerCAmelCase__,						text_config=lowerCAmelCase__				)

      return config, image_size





@torch.no_grad()
def 			_a(	UpperCamelCase__					:    Dict,						UpperCamelCase__					:    Any=None,						UpperCamelCase__					:    Union[str, Any]=False				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					List[str]						=(
          AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''				)
          if 'opt' in model_name
          else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''				)
      )
      SCREAMING_SNAKE_CASE__  :					str						=tokenizer('''\n''',						add_special_tokens=lowerCAmelCase__				).input_ids[0]
      SCREAMING_SNAKE_CASE__  :					Optional[int]						=get_blipa_config(lowerCAmelCase__,						eos_token_id=lowerCAmelCase__				)

      SCREAMING_SNAKE_CASE__  :					Optional[int]						=BlipaForConditionalGeneration(lowerCAmelCase__				).eval()

      SCREAMING_SNAKE_CASE__  :					Optional[Any]						={
          'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
          'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
          'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
          'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
          'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
          'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
          'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
      }

      SCREAMING_SNAKE_CASE__  :					Optional[int]						=model_name_to_original[model_name]

      # load original model
      print('''Loading original model...'''				)
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						='cuda' if torch.cuda.is_available() else 'cpu'
      SCREAMING_SNAKE_CASE__  :					List[Any]						=load_model_and_preprocess(
          name=lowerCAmelCase__,						model_type=lowerCAmelCase__,						is_eval=lowerCAmelCase__,						device=lowerCAmelCase__				)
      original_model.eval()
      print('''Done!'''				)

      # update state dict keys
      SCREAMING_SNAKE_CASE__  :					int						=original_model.state_dict()
      SCREAMING_SNAKE_CASE__  :					Dict						=create_rename_keys(lowerCAmelCase__				)
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__,						lowerCAmelCase__,						lowerCAmelCase__				)

      # some keys can be renamed efficiently
      for key, val in state_dict.copy().items():
            SCREAMING_SNAKE_CASE__  :					int						=state_dict.pop(lowerCAmelCase__				)
            if key.startswith('''Qformer.bert'''				):
                  SCREAMING_SNAKE_CASE__  :					int						=key.replace('''Qformer.bert''',						'''qformer'''				)
            if "attention.self" in key:
                  SCREAMING_SNAKE_CASE__  :					Dict						=key.replace('''self''',						'''attention'''				)
            if "opt_proj" in key:
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=key.replace('''opt_proj''',						'''language_projection'''				)
            if "t5_proj" in key:
                  SCREAMING_SNAKE_CASE__  :					List[str]						=key.replace('''t5_proj''',						'''language_projection'''				)
            if key.startswith('''opt'''				):
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=key.replace('''opt''',						'''language'''				)
            if key.startswith('''t5'''				):
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=key.replace('''t5''',						'''language'''				)
            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=val

      # read in qv biases
      read_in_q_v_bias(lowerCAmelCase__,						lowerCAmelCase__				)

      SCREAMING_SNAKE_CASE__  :					Dict						=hf_model.load_state_dict(lowerCAmelCase__,						strict=lowerCAmelCase__				)
      assert len(lowerCAmelCase__				) == 0
      assert unexpected_keys == ["qformer.embeddings.position_ids"]

      SCREAMING_SNAKE_CASE__  :					Tuple						=load_demo_image()
      SCREAMING_SNAKE_CASE__  :					List[Any]						=vis_processors['eval'](lowerCAmelCase__				).unsqueeze(0				).to(lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=tokenizer(['''\n'''],						return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)

      # create processor
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=BlipImageProcessor(
          size={'''height''': image_size, '''width''': image_size},						image_mean=lowerCAmelCase__,						image_std=lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					Tuple						=BlipaProcessor(image_processor=lowerCAmelCase__,						tokenizer=lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					Optional[int]						=processor(images=lowerCAmelCase__,						return_tensors='''pt'''				).pixel_values.to(lowerCAmelCase__				)

      # make sure processor creates exact same pixel values
      assert torch.allclose(lowerCAmelCase__,						lowerCAmelCase__				)

      original_model.to(lowerCAmelCase__				)
      hf_model.to(lowerCAmelCase__				)
      with torch.no_grad():
            if "opt" in model_name:
                  SCREAMING_SNAKE_CASE__  :					List[Any]						=original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}				).logits
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=hf_model(lowerCAmelCase__,						lowerCAmelCase__				).logits
            else:
                  SCREAMING_SNAKE_CASE__  :					Optional[Any]						=original_model(
                      {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}				).logits
                  SCREAMING_SNAKE_CASE__  :					Dict						=input_ids.masked_fill(input_ids == tokenizer.pad_token_id,						-1_0_0				)
                  SCREAMING_SNAKE_CASE__  :					Tuple						=hf_model(lowerCAmelCase__,						lowerCAmelCase__,						labels=lowerCAmelCase__				).logits

      assert original_logits.shape == logits.shape
      print('''First values of original logits:''',						original_logits[0, :3, :3]				)
      print('''First values of HF logits:''',						logits[0, :3, :3]				)

      # assert values
      if model_name == "blip2-flan-t5-xl":
            SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=torch.tensor(
                [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]],						device=lowerCAmelCase__				)
            assert torch.allclose(logits[0, :3, :3],						lowerCAmelCase__,						atol=1e-4				)
      elif model_name == "blip2-flan-t5-xl-coco":
            SCREAMING_SNAKE_CASE__  :					str						=torch.tensor(
                [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]],						device=lowerCAmelCase__				)
      else:
            # cast to same type
            SCREAMING_SNAKE_CASE__  :					str						=logits.dtype
            assert torch.allclose(original_logits.to(lowerCAmelCase__				),						lowerCAmelCase__,						atol=1e-2				)
      print('''Looks ok!'''				)

      print('''Generating a caption...'''				)
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=''
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=tokenizer(lowerCAmelCase__,						return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)

      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=original_model.generate({'''image''': original_pixel_values}				)
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=hf_model.generate(
          lowerCAmelCase__,						lowerCAmelCase__,						do_sample=lowerCAmelCase__,						num_beams=5,						max_length=3_0,						min_length=1,						top_p=0.9,						repetition_penalty=1.0,						length_penalty=1.0,						temperature=1,						)
      print('''Original generation:''',						lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					List[Any]						=input_ids.shape[1]
      SCREAMING_SNAKE_CASE__  :					Optional[int]						=processor.batch_decode(outputs[:, prompt_length:],						skip_special_tokens=lowerCAmelCase__				)
      SCREAMING_SNAKE_CASE__  :					Tuple						=[text.strip() for text in output_text]
      print('''HF generation:''',						lowerCAmelCase__				)

      if pytorch_dump_folder_path is not None:
            processor.save_pretrained(lowerCAmelCase__				)
            hf_model.save_pretrained(lowerCAmelCase__				)

      if push_to_hub:
            processor.push_to_hub(f"nielsr/{model_name}"				)
            hf_model.push_to_hub(f"nielsr/{model_name}"				)


if __name__ == "__main__":
  a_											=			argparse.ArgumentParser()
  a_											=			[
      """blip2-opt-2.7b""",
      """blip2-opt-6.7b""",
      """blip2-opt-2.7b-coco""",
      """blip2-opt-6.7b-coco""",
      """blip2-flan-t5-xl""",
      """blip2-flan-t5-xl-coco""",
      """blip2-flan-t5-xxl""",
  ]
  parser.add_argument(
      '--model_name',
      default='blip2-opt-2.7b',
      choices=choices,
      type=str,
      help='Path to hf config.json of model to convert',
  )
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
      help='Whether to push the model and processor to the hub after converting',
  )

  a_											=			parser.parse_args()

  convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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 						UpperCamelCase_  (  snake_case__  ,     unittest.TestCase							):
					_a      :						Optional[Any]						    =					CodeGenTokenizer
					_a      :						Optional[Any]						    =					CodeGenTokenizerFast
					_a      :						Union[str, Any]						    =					True
					_a      :						Dict						    =					{'add_prefix_space': True}
					_a      :						Dict						    =					False



					def   __a     (		self     :     Union[str, Any]						):
							super().setUp()

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

							lowerCamelCase_	:     Optional[Any] 						=      os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES['vocab_file']						)
							lowerCamelCase_	:     Tuple 						=      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(lowerCamelCase						) + '\n'						)
							with open(self.merges_file       ,      'w'       ,      encoding='utf-8'						) as fp:
									fp.write('\n'.join(lowerCamelCase						)						)



					def   __a     (		self     :     Optional[int]       ,      **lowerCamelCase     :     Dict						):
							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      **lowerCamelCase						)



					def   __a     (		self     :     List[Any]       ,      **lowerCamelCase     :     int						):
							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,      **lowerCamelCase						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     List[str] 						=      'lower newer'
							lowerCamelCase_	:     str 						=      'lower newer'
							return input_text, output_text



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      CodeGenTokenizer(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map						)
							lowerCamelCase_	:     Union[str, Any] 						=      'lower newer'
							lowerCamelCase_	:     List[str] 						=      ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
							lowerCamelCase_	:     Dict 						=      tokenizer.tokenize(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      tokens + [tokenizer.unk_token]
							lowerCamelCase_	:     Dict 						=      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase						)       ,      lowerCamelCase						)



					def   __a     (		self     :     str						):
							if not self.test_rust_tokenizer:
									return

							lowerCamelCase_	:     Dict 						=      self.get_tokenizer()
							lowerCamelCase_	:     Optional[Any] 						=      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      'lower newer'

							# Testing tokenization
							lowerCamelCase_	:     List[str] 						=      tokenizer.tokenize(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      rust_tokenizer.tokenize(lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing conversion to ids without special tokens
							lowerCamelCase_	:     List[str] 						=      tokenizer.encode(lowerCamelCase       ,      add_special_tokens=lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     str 						=      rust_tokenizer.encode(lowerCamelCase       ,      add_special_tokens=lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing conversion to ids with special tokens
							lowerCamelCase_	:     Dict 						=      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      tokenizer.encode(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      rust_tokenizer.encode(lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing the unknown token
							lowerCamelCase_	:     Optional[int] 						=      tokens + [rust_tokenizer.unk_token]
							lowerCamelCase_	:     int 						=      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase						)       ,      lowerCamelCase						)



					def   __a     (		self     :     Tuple       ,      *lowerCamelCase     :     Dict       ,      **lowerCamelCase     :     Tuple						):
							# 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   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Any=15						):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
											lowerCamelCase_	:     Optional[int] 						=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase       ,      **lowerCamelCase						)

											# Simple input
											lowerCamelCase_	:     Any 						=      'This is a simple input'
											lowerCamelCase_	:     Optional[int] 						=      ['This is a simple input 1', 'This is a simple input 2']
											lowerCamelCase_	:     Optional[Any] 						=      ('This is a simple input', 'This is a pair')
											lowerCamelCase_	:     str 						=      [
											    ('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(lowerCamelCase       ,      tokenizer_r.encode       ,      lowerCamelCase       ,      max_length=lowerCamelCase       ,      padding='max_length'						)

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

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

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

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

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



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Optional[int] 						=      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      pad_token='<pad>'						)

							# Simple input
							lowerCamelCase_	:     Tuple 						=      'This is a simple input'
							lowerCamelCase_	:     Tuple 						=      ['This is a simple input looooooooong', 'This is a simple input']
							lowerCamelCase_	:     Any 						=      ('This is a simple input', 'This is a pair')
							lowerCamelCase_	:     Tuple 						=      [
							    ('This is a simple input loooooong', 'This is a simple input'),
							    ('This is a simple pair loooooong', 'This is a simple pair'),
							]

							lowerCamelCase_	:     str 						=      tokenizer.pad_token_id

							lowerCamelCase_	:     int 						=      tokenizer(lowerCamelCase       ,      padding='max_length'       ,      max_length=30       ,      return_tensors='np'						)
							lowerCamelCase_	:     List[Any] 						=      tokenizer(lowerCamelCase       ,      padding=lowerCamelCase       ,      truncate=lowerCamelCase       ,      return_tensors='np'						)
							lowerCamelCase_	:     Tuple 						=      tokenizer(*lowerCamelCase       ,      padding='max_length'       ,      max_length=60       ,      return_tensors='np'						)
							lowerCamelCase_	:     Union[str, Any] 						=      tokenizer(lowerCamelCase       ,      padding=lowerCamelCase       ,      truncate=lowerCamelCase       ,      return_tensors='np'						)

							# s
							# test single string max_length padding
							self.assertEqual(out_s['input_ids'].shape[-1]       ,      30						)
							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]       ,      33						)
							# 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]       ,      60						)
							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]       ,      52						)
							# 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   __a     (		self     :     str						):
							lowerCamelCase_	:     List[str] 						=      '$$$'
							lowerCamelCase_	:     Dict 						=      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      bos_token=lowerCamelCase       ,      add_bos_token=lowerCamelCase						)

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

							lowerCamelCase_	:     List[str] 						=      tokenizer.bos_token_id

							lowerCamelCase_	:     Optional[Any] 						=      tokenizer(lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      tokenizer(lowerCamelCase						)

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

							lowerCamelCase_	:     str 						=      tokenizer.decode(out_s.input_ids						)
							lowerCamelCase_	:     Any 						=      tokenizer.batch_decode(out_sa.input_ids						)

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



					@slow
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Optional[int] 						=      CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'						)

							lowerCamelCase_	:     Tuple 						=      '\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
							lowerCamelCase_	:     Any 						=      '\nif len_a > len_b:      result = a\nelse:      result = b'

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





					def   __a     (		self     :     Optional[Any]						):
							pass
