






"""simple docstring"""





import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

UpperCamelCase_						:					str      				=			logging.get_logger(__name__)

UpperCamelCase_						:					Any      				=			{'vocab_file': 'tokenizer.model'}

UpperCamelCase_						:					List[str]      				=			{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
UpperCamelCase_						:					int      				=			{
    'hf-internal-testing/llama-tokenizer': 2048,
}
UpperCamelCase_						:					Optional[int]      				=			'▁'

UpperCamelCase_						:					int      				=			'[INST]', '[/INST]'
UpperCamelCase_						:					Tuple      				=			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
UpperCamelCase_						:					Any      				=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class   _lowercase			(  lowerCAmelCase    ):
      _a :  Tuple										= VOCAB_FILES_NAMES
      _a :  List[str]										= PRETRAINED_VOCAB_FILES_MAP
      _a :  str										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a :  str										= ["input_ids", "attention_mask"]


      def __init__(     self					:						Dict				, a					:						Union[str, Any]				, a					:						Any="<unk>"				, a					:						str="<s>"				, a					:						Tuple="</s>"				, a					:						Optional[Any]=None				, a					:						Optional[Dict[str, Any]] = None				, a					:						str=True				, a					:						Optional[int]=False				, a					:						str=False				, a					:						Optional[int]=True				, **a					:						Dict				, ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      ={} if sp_model_kwargs is None else sp_model_kwargs
            __snake_case   :     List[Any]	      =AddedToken(UpperCamelCase_				, lstrip=UpperCamelCase_				, rstrip=UpperCamelCase_    ) if isinstance(UpperCamelCase_				, UpperCamelCase_    ) else bos_token
            __snake_case   :     str	      =AddedToken(UpperCamelCase_				, lstrip=UpperCamelCase_				, rstrip=UpperCamelCase_    ) if isinstance(UpperCamelCase_				, UpperCamelCase_    ) else eos_token
            __snake_case   :     Any	      =AddedToken(UpperCamelCase_				, lstrip=UpperCamelCase_				, rstrip=UpperCamelCase_    ) if isinstance(UpperCamelCase_				, UpperCamelCase_    ) else unk_token
            __snake_case   :     str	      =AddedToken(UpperCamelCase_				, lstrip=UpperCamelCase_				, rstrip=UpperCamelCase_    ) if isinstance(UpperCamelCase_				, UpperCamelCase_    ) else pad_token
            super().__init__(
                bos_token=UpperCamelCase_				, eos_token=UpperCamelCase_				, unk_token=UpperCamelCase_				, pad_token=UpperCamelCase_				, add_bos_token=UpperCamelCase_				, add_eos_token=UpperCamelCase_				, sp_model_kwargs=self.sp_model_kwargs				, clean_up_tokenization_spaces=UpperCamelCase_				, legacy=UpperCamelCase_				, **UpperCamelCase_				, )
            if legacy:
                  logger.warning_once(
                      f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                      ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''    )
            __snake_case   :     Dict	      =legacy
            __snake_case   :     Optional[int]	      =vocab_file
            __snake_case   :     Optional[int]	      =add_bos_token
            __snake_case   :     List[str]	      =add_eos_token
            __snake_case   :     Union[str, Any]	      =spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.Load(UpperCamelCase_    )


      def __getstate__(     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     int	      =self.__dict__.copy()
            __snake_case   :     Optional[Any]	      =None
            __snake_case   :     List[Any]	      =self.sp_model.serialized_model_proto()
            return state


      def __setstate__(     self					:						List[Any]				, a					:						Dict    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =d
            __snake_case   :     int	      =spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )


      @property
      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            return self.sp_model.get_piece_size()


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     Dict	      ={self.convert_ids_to_tokens(UpperCamelCase_    ): i for i in range(self.vocab_size    )}
            vocab.update(self.added_tokens_encoder    )
            return vocab


      def _UpperCamelCase    (     self					:						Any				, a					:						Optional[Any]				, **a					:						str    ):





            """simple docstring"""







            if not self.legacy:
                  __snake_case   :     Union[str, Any]	      =SPIECE_UNDERLINE + text.replace(UpperCamelCase_				, ''' '''    )
            return super().tokenize(UpperCamelCase_				, **UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						str				, a					:						List[str]    ):





            """simple docstring"""







            if not self.legacy:
                  __snake_case   :     Union[str, Any]	      =text.startswith(UpperCamelCase_    )
                  if is_first:
                        __snake_case   :     List[str]	      =text[1:]

            __snake_case   :     str	      =self.sp_model.encode(UpperCamelCase_				, out_type=UpperCamelCase_    )

            if not self.legacy and not is_first and not text.startswith(''' '''    ) and tokens[0].startswith(UpperCamelCase_    ):
                  __snake_case   :     str	      =([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
            return tokens


      def _UpperCamelCase    (     self					:						List[str]				, a					:						int    ):





            """simple docstring"""







            return self.sp_model.piece_to_id(UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						Tuple				, a					:						List[str]    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =self.sp_model.IdToPiece(UpperCamelCase_    )
            return token


      def _UpperCamelCase    (     self					:						str				, a					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =[]
            __snake_case   :     List[str]	      =''
            __snake_case   :     str	      =False
            for i, token in enumerate(UpperCamelCase_    ):
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special and i != 0:
                              out_string += " "
                        out_string += self.sp_model.decode(UpperCamelCase_    ) + token
                        __snake_case   :     Dict	      =True
                        __snake_case   :     str	      =[]
                  else:
                        current_sub_tokens.append(UpperCamelCase_    )
                        __snake_case   :     Optional[Any]	      =False
            out_string += self.sp_model.decode(UpperCamelCase_    )
            return out_string


      def _UpperCamelCase    (     self					:						List[str]				, a					:						str				, a					:						Optional[str] = None    ):





            """simple docstring"""







            if not os.path.isdir(UpperCamelCase_    ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
                  return
            __snake_case   :     Optional[int]	      =os.path.join(
                UpperCamelCase_				, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

            if os.path.abspath(self.vocab_file    ) != os.path.abspath(UpperCamelCase_    ) and os.path.isfile(self.vocab_file    ):
                  copyfile(self.vocab_file				, UpperCamelCase_    )
            elif not os.path.isfile(self.vocab_file    ):
                  with open(UpperCamelCase_				, '''wb'''    ) as fi:
                        __snake_case   :     Any	      =self.sp_model.serialized_model_proto()
                        fi.write(UpperCamelCase_    )

            return (out_vocab_file,)


      def _UpperCamelCase    (     self					:						str				, a					:						Union[str, Any]				, a					:						Optional[Any]=None    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =[self.bos_token_id] if self.add_bos_token else []
            __snake_case   :     Tuple	      =[self.eos_token_id] if self.add_eos_token else []

            __snake_case   :     Any	      =bos_token_id + token_ids_a + eos_token_id

            if token_ids_a is not None:
                  __snake_case   :     List[str]	      =output + bos_token_id + token_ids_a + eos_token_id

            return output


      def _UpperCamelCase    (     self					:						Dict				, a					:						List[int]				, a					:						Optional[List[int]] = None				, a					:						bool = False    ):





            """simple docstring"""







            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase_				, token_ids_a=UpperCamelCase_				, already_has_special_tokens=UpperCamelCase_    )

            __snake_case   :     List[str]	      =[1] if self.add_bos_token else []
            __snake_case   :     List[str]	      =[1] if self.add_eos_token else []

            if token_ids_a is None:
                  return bos_token_id + ([0] * len(UpperCamelCase_    )) + eos_token_id
            return (
                bos_token_id
                + ([0] * len(UpperCamelCase_    ))
                + eos_token_id
                + bos_token_id
                + ([0] * len(UpperCamelCase_    ))
                + eos_token_id
            )


      def _UpperCamelCase    (     self					:						Tuple				, a					:						List[int]				, a					:						Optional[List[int]] = None    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =[self.bos_token_id] if self.add_bos_token else []
            __snake_case   :     Any	      =[self.eos_token_id] if self.add_eos_token else []

            __snake_case   :     int	      =[0] * len(bos_token_id + token_ids_a + eos_token_id    )

            if token_ids_a is not None:
                  output += [1] * len(bos_token_id + token_ids_a + eos_token_id    )

            return output







      def _UpperCamelCase    (     self					:						Union[str, Any]				, a					:						"Conversation"    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =list(conversation.iter_texts()    )
            if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
                [not is_user for is_user, msg in dialogue[1::2]]    ):
                  raise ValueError(
                      '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''    )

            __snake_case   :     List[int]	      =[]
            if len(conversation.past_user_inputs    ) > 0:
                  if not conversation.past_user_inputs[0].startswith(UpperCamelCase_    ) or E_SYS not in conversation.past_user_inputs[0]:
                        __snake_case   :     Optional[Any]	      =(
                            B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                        )
            elif not dialogue[0][1].startswith(UpperCamelCase_    ) or E_SYS not in dialogue[0][1]:
                  __snake_case   :     List[Any]	      =(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

            dialog_tokens += sum(
                [
                    [self.bos_token_id]
                    + self.encode(
                        f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''				, add_special_tokens=UpperCamelCase_    )
                    + [self.eos_token_id]
                    for prompt, answer in zip(dialogue[::2]				, dialogue[1::2]    )
                ]				, []				, )
            if not (dialogue[-1][0]):
                  raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}'''    )
            dialog_tokens += [self.bos_token_id] + self.encode(
                f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''				, add_special_tokens=UpperCamelCase_    )
            return dialog_tokens








import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase__    :							Any       		=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm
   import run_image_classification
   import run_mlm
   import run_ner
   import run_qa as run_squad
   import run_summarization
   import run_swag
   import run_text_classification
   import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





  _lowercase:			str      =     {}
  _lowercase:			Union[str, Any]      =     os.path.join(UpperCamelCase       ,  'all_results.json'				)
  if os.path.exists(UpperCamelCase				):
    with open(UpperCamelCase       ,  'r'				) as f:
      _lowercase:			Dict      =     json.load(UpperCamelCase				)
  else:
    raise ValueError(F'can\'t find {path}'				)
  return results


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





  return bool(tf.config.list_physical_devices('GPU'				)				)


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






   @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
   def 			_a    (						self :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			List[str]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

     if is_cuda_available():
       testargs.append('--fp16'  )

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_text_classification.main()
       # Reset the mixed precision policy so we don't break other tests
       tf.keras.mixed_precision.set_global_policy('float32'  )
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['eval_accuracy']    ,       0.75  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

     if len(tf.config.list_physical_devices('GPU'  )  ) > 1:
       # Skipping because there are not enough batches to train the model + would need a drop_last to work.
       return

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_clm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






   def 			_a    (						self :							int  )->      Any:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     _lowercase:			List[Any]      =     7 if get_gpu_count() > 1 else 2

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_ner.main()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Any      =     f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['rouge1']    ,       10  )
       self.assertGreaterEqual(result['rouge2']    ,       2  )
       self.assertGreaterEqual(result['rougeL']    ,       7  )
       self.assertGreaterEqual(result['rougeLsum']    ,       7  )






   @slow
   def 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_translation.main()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Dict      =     f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_image_classification.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
