




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase       :	Optional[int]									=     2







class 						A:



 '''simple docstring'''


 def __init__(       self      :						Tuple		,							*,  # begin keyword-only arguments
     A_      :						Optional[Any]="<s>"		,							A_      :						Tuple="<pad>"		,							A_      :						Any="</s>"		,							A_      :						Optional[int]="<unk>"		,							A_      :						int=None		,							)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					bos, unk, pad, eos
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					{}
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       lowerCamelCase_	  =					self.add_symbol(A_						)
       if extra_special_symbols:
             for s in extra_special_symbols:
                   self.add_symbol(A_						)
       lowerCamelCase_	  =					len(self.symbols						)

 def __eq__(       self      :						str		,							A_      :						Dict						)     ->			str:

       """simple docstring"""






       return self.indices == other.indices

 def __getitem__(       self      :						Tuple		,							A_      :						List[Any]						)     ->			Any:

       """simple docstring"""






       if idx < len(self.symbols						):
             return self.symbols[idx]
       return self.unk_word

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

       """simple docstring"""






       return len(self.symbols						)

 def __contains__(       self      :						Any		,							A_      :						Dict						)     ->			Union[str, Any]:

       """simple docstring"""






       return sym in self.indices

 @classmethod
 def        a__				(       cls      :						Dict		,							A_      :						List[str]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					cls()
       d.add_from_file(A_						)
       return d

 def        a__				(       self      :						Any		,							A_      :						Tuple		,							A_      :						Tuple=1		,							A_      :						Tuple=False						)     ->			Dict:

       """simple docstring"""






       if word in self.indices and not overwrite:
             lowerCamelCase_	  =					self.indices[word]
             lowerCamelCase_	  =					self.count[idx] + n
             return idx
       else:
             lowerCamelCase_	  =					len(self.symbols						)
             lowerCamelCase_	  =					idx
             self.symbols.append(A_						)
             self.count.append(A_						)
             return idx

 def        a__				(       self      :						int		,							A_      :						List[Any]						)     ->			Optional[int]:

       """simple docstring"""






       return 0





 def        a__				(       self      :						Dict		,							A_      :						Optional[Any]						)     ->			Optional[int]:

       """simple docstring"""






       if isinstance(A_		,							A_						):
             try:
                   with open(A_		,							'r'		,							encoding='utf-8'						) as fd:
                         self.add_from_file(A_						)
             except FileNotFoundError as fnfe:
                   raise fnfe
             except UnicodeError:
                   raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(A_						)						)
             return

       lowerCamelCase_	  =					f.readlines()
       lowerCamelCase_	  =					self._load_meta(A_						)

       for line in lines[indices_start_line:]:
             try:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					line.rstrip().rsplit(' '		,							1						)
                   if field == "#fairseq:overwrite":
                         lowerCamelCase_	  =					True
                         lowerCamelCase_  ,       lowerCamelCase_	  =					line.rsplit(' '		,							1						)
                   else:
                         lowerCamelCase_	  =					False
                   lowerCamelCase_	  =					int(A_						)
                   lowerCamelCase_	  =					line
                   if word in self and not overwrite:
                         raise RuntimeError(
                             'Duplicate word found when loading Dictionary: \'{}\'. '
                             'Duplicate words can overwrite earlier ones by adding the '
                             '#fairseq:overwrite flag at the end of the corresponding row '
                             'in the dictionary file. If using the Camembert model, please '
                             'download an updated copy of the model file.'.format(A_						)						)
                   self.add_symbol(A_		,							n=A_		,							overwrite=A_						)
             except ValueError:
                   raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					dict((re.sub(r'@@$'			,							''			,							lowercase  ), v) if k.endswith('@@'  ) else (re.sub(r'$'			,							'</w>'			,							lowercase  ), v) for k, v in d.items()  )
      lowerCamelCase_	  =					'<s> <pad> </s> <unk>'.split()
      # restore the special tokens
      for k in keep_keys:
            del da[f"""{k}</w>"""]
            lowerCamelCase_	  =					d[k]  # restore
      return da

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      if not os.path.exists(lowercase  ):
            raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""  )
      os.makedirs(lowercase			,							exist_ok=lowercase  )
      print(f"""Writing results to {pytorch_dump_folder_path}"""  )

      # handle various types of models

      lowerCamelCase_	  =					os.path.join(lowercase			,							'checkpoint.pt'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""  )
      lowerCamelCase_	  =					torch.load(lowercase			,							map_location='cpu'  )

      lowerCamelCase_	  =					chkpt['cfg']['model']

      # dicts
      lowerCamelCase_	  =					os.path.join(lowercase			,							'dict.txt'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {dict_file} does not exist!"""  )
      lowerCamelCase_	  =					Dictionary.load(lowercase  )
      lowerCamelCase_	  =					rewrite_dict_keys(src_dict.indices  )
      lowerCamelCase_	  =					len(lowercase  )
      lowerCamelCase_	  =					os.path.join(lowercase			,							VOCAB_FILES_NAMES['vocab_file']  )
      print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # merges_file (bpecodes)
      lowerCamelCase_	  =					os.path.join(lowercase			,							'bpecodes'  )
      if not os.path.isfile(lowercase  ):
            raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""  )

      lowerCamelCase_	  =					os.path.join(lowercase			,							VOCAB_FILES_NAMES['merges_file']  )
      shutil.copyfile(lowercase			,							lowercase  )

      # model config
      lowerCamelCase_	  =					os.path.join(lowercase			,							'config.json'  )

      lowerCamelCase_	  =					{
          'activation_dropout': args['activation_dropout'],
          'architectures': ['BioGptForCausalLM'],
          'attention_probs_dropout_prob': args['attention_dropout'],
          'bos_token_id': 0,
          'eos_token_id': 2,
          'hidden_act': args['activation_fn'],
          'hidden_dropout_prob': args['dropout'],
          'hidden_size': args['decoder_embed_dim'],
          'initializer_range': 0.02,
          'intermediate_size': args['decoder_ffn_embed_dim'],
          'layer_norm_eps': 1e-1_2,
          'layerdrop': args['decoder_layerdrop'],
          'max_position_embeddings': args['max_target_positions'],
          'model_type': 'biogpt',
          'num_attention_heads': args['decoder_attention_heads'],
          'num_hidden_layers': args['decoder_layers'],
          'pad_token_id': 1,
          'scale_embedding': not args['no_scale_embedding'],
          'tie_word_embeddings': args['share_decoder_input_output_embed'],
          'vocab_size': src_vocab_size,
      }

      # good hparam defaults to start with

      print(f"""Generating {biogpt_model_config_file}"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # tokenizer config
      lowerCamelCase_	  =					os.path.join(lowercase			,							lowercase  )

      lowerCamelCase_	  =					{
          'bos_token': '<s>',
          'eos_token': '</s>',
          'model_max_length': 10_24,
          'pad_token': '<pad>',
          'special_tokens_map_file': None,
          'tokenizer_class': 'BioGptTokenizer',
          'unk_token': '<unk>',
      }

      print(f"""Generating {biogpt_tokenizer_config_file}"""  )
      with open(lowercase			,							'w'			,							encoding='utf-8'  ) as f:
            f.write(json.dumps(lowercase			,							ensure_ascii=lowercase			,							indent=lowercase  )  )

      # model
      lowerCamelCase_	  =					chkpt['model']

      # remove unneeded keys
      lowerCamelCase_	  =					[
          'decoder.version',
      ]
      for k in ignore_keys:
            model_state_dict.pop(lowercase			,							lowercase  )

      lowerCamelCase_	  =					list(model_state_dict.keys()  )
      for layer_name in layer_names:
            if layer_name.endswith('output_projection.weight'  ):
                  lowerCamelCase_	  =					model_state_dict.pop(lowercase  )
            else:
                  lowerCamelCase_	  =					model_state_dict.pop(lowercase  )

      lowerCamelCase_	  =					BioGptConfig.from_pretrained(lowercase  )
      lowerCamelCase_	  =					BioGptForCausalLM(lowercase  )

      # check that it loads ok
      model_new.load_state_dict(lowercase  )

      # save
      lowerCamelCase_	  =					os.path.join(lowercase			,							lowercase  )
      print(f"""Generating {pytorch_weights_dump_path}"""  )
      torch.save(lowercase			,							lowercase  )

      print('Conversion is done!'  )


if __name__ == "__main__":
 lowerCamelCase       :	Tuple									=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--biogpt_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help=(
         "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
         " bpecodes, etc."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase       :	str									=     parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		MODEL_FOR_CAUSAL_LM_MAPPING
 UpperCamelCase		     =		TF_MODEL_FOR_CAUSAL_LM_MAPPING

 @require_torch
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model='sshleifer/tiny-ctrl'		,							framework='pt'						)
       # Using `do_sample=False` to force deterministic output
       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                       ' oscope. FiliFili@@'
                   )
               }
           ]		,							)

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       'generated_text': (
                           'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                           ' oscope. FiliFili@@'
                       )
                   }
               ],
               [
                   {
                       'generated_text': (
                           'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                           ' oscope. oscope. FiliFili@@'
                       )
                   }
               ],
           ]		,							)

       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_		,							num_return_sequences=2		,							return_tensors=A_						)
       self.assertEqual(
           A_		,							[
               {'generated_token_ids': ANY(A_						)},
               {'generated_token_ids': ANY(A_						)},
           ]		,							)
       lowerCamelCase_	  =					text_generator.model.config.eos_token_id
       lowerCamelCase_	  =					'<pad>'
       lowerCamelCase_	  =					text_generator(
           ['This is a test', 'This is a second test']		,							do_sample=A_		,							num_return_sequences=2		,							batch_size=2		,							return_tensors=A_		,							)
       self.assertEqual(
           A_		,							[
               [
                   {'generated_token_ids': ANY(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
               [
                   {'generated_token_ids': ANY(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
           ]		,							)

 @require_tf
 def        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model='sshleifer/tiny-ctrl'		,							framework='tf'						)

       # Using `do_sample=False` to force deterministic output
       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                       ' please,'
                   )
               }
           ]		,							)

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       'generated_text': (
                           'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                           ' please,'
                       )
                   }
               ],
               [
                   {
                       'generated_text': (
                           'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                           ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                       )
                   }
               ],
           ]		,							)

 def        a__				(       self      :						Optional[int]		,							A_      :						Dict		,							A_      :						int		,							A_      :						List[str]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					TextGenerationPipeline(model=A_		,							tokenizer=A_						)
       return text_generator, ["This is a test", "Another test"]

 def        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello I believe in'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       lowerCamelCase_	  =					text_generator(A_						)
       self.assertEqual(
           A_		,							[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]		,							)

       lowerCamelCase_	  =					text_generator(A_		,							stop_sequence=' fe'						)
       self.assertEqual(A_		,							[{'generated_text': 'Hello I believe in fe'}]						)

 def        a__				(       self      :						Any		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					text_generator.model
       lowerCamelCase_	  =					text_generator.tokenizer

       lowerCamelCase_	  =					text_generator('This is a test'						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'						)						)

       lowerCamelCase_	  =					text_generator('This is a test'		,							return_full_text=A_						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertNotIn('This is a test'		,							outputs[0]['generated_text']						)

       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model=A_		,							tokenizer=A_		,							return_full_text=A_						)
       lowerCamelCase_	  =					text_generator('This is a test'						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertNotIn('This is a test'		,							outputs[0]['generated_text']						)

       lowerCamelCase_	  =					text_generator('This is a test'		,							return_full_text=A_						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'						)						)

       lowerCamelCase_	  =					text_generator(['This is great !', 'Something else']		,							num_return_sequences=2		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
               [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
           ]		,							)

       if text_generator.tokenizer.pad_token is not None:
             lowerCamelCase_	  =					text_generator(
                 ['This is great !', 'Something else']		,							num_return_sequences=2		,							batch_size=2		,							do_sample=A_						)
             self.assertEqual(
                 A_		,							[
                     [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
                     [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
                 ]		,							)

       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_text=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_tensors=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_text=A_		,							return_tensors=A_						)

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
             lowerCamelCase_	  =					text_generator(''						)
             self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       else:
             with self.assertRaises((ValueError, AssertionError)						):
                   lowerCamelCase_	  =					text_generator(''						)

       if text_generator.framework == "tf":
             # TF generation does not support max_new_tokens, and it's impossible
             # to control long generation with only max_length without
             # fancy calculation, dismissing tests for now.
             return
       # We don't care about infinite range models.
       # They already work.
       # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
       lowerCamelCase_	  =					['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
       if (
           tokenizer.model_max_length < 10000
           and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
       ):
             # Handling of large generations
             with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)						):
                   text_generator('This is a test' * 500		,							max_new_tokens=20						)

             lowerCamelCase_	  =					text_generator('This is a test' * 500		,							handle_long_generation='hole'		,							max_new_tokens=20						)
             # Hole strategy cannot work
             with self.assertRaises(A_						):
                   text_generator(
                       'This is a test' * 500		,							handle_long_generation='hole'		,							max_new_tokens=tokenizer.model_max_length + 10		,							)

 @require_torch
 @require_accelerate
 @require_torch_gpu
 def        a__				(       self      :						Union[str, Any]						)     ->			Any:

       """simple docstring"""






       import torch

       # Classic `model_kwargs`
       lowerCamelCase_	  =					pipeline(
           model='hf-internal-testing/tiny-random-bloom'		,							model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}		,							)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.bfloataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'		,							torch_dtype=torch.bfloataa						)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.bfloataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'						)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.floataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

 @require_torch
 @require_torch_gpu
 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       import torch

       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device=0		,							torch_dtype=torch.floataa						)
       pipe('This is a test'						)

 @require_torch
 @require_accelerate
 @require_torch_gpu
 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       import torch

       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'		,							torch_dtype=torch.floataa						)
       pipe('This is a test'		,							do_sample=A_		,							top_p=0.5						)





 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello world'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       if text_generator.model.framework == "tf":
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.tf_utils'						)
       else:
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.utils'						)
       lowerCamelCase_	  =					'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_length=10		,							max_new_tokens=1						)
       self.assertIn(A_		,							cl.out						)

       # The user only sets one -> no warning
       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_new_tokens=1						)
       self.assertNotIn(A_		,							cl.out						)

       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_length=10						)
       self.assertNotIn(A_		,							cl.out						)



