

import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @parameterized.expand([(None,), ('foo.json',)])
    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	GenerationConfig(
             do_sample=a						,       temperature=0.7						,       length_penalty=1.0						,       bad_words_ids=[[1, 2, 3], [4, 5]]						,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(a						,       config_name=a)
              lowercase__   :       Tuple				=	GenerationConfig.from_pretrained(a						,       config_name=a)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample						,       a)
         self.assertEqual(loaded_config.temperature						,       0.7)
         self.assertEqual(loaded_config.length_penalty						,       1.0)
         self.assertEqual(loaded_config.bad_words_ids						,       [[1, 2, 3], [4, 5]])

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k						,       50)
         self.assertEqual(loaded_config.max_length						,       20)
         self.assertEqual(loaded_config.max_time						,       a)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	AutoConfig.from_pretrained('gpt2')
         lowercase__   :       List[Any]				=	GenerationConfig.from_model_config(a)
         lowercase__   :       Any				=	GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(a						,       a)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id						,       default_generation_config.eos_token_id)
         self.assertEqual(generation_config_from_model.eos_token_id						,       model_config.eos_token_id)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	GenerationConfig()
         lowercase__   :       Optional[Any]				=	{
             'max_new_tokens': 1024,
             'foo': 'bar',
         }
         lowercase__   :       Optional[Any]				=	copy.deepcopy(a)
         lowercase__   :       Optional[Any]				=	generation_config.update(**a)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(a						,       a)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens						,       1024)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(a						,       {'foo': 'bar'})





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	GenerationConfig()
         lowercase__   :       List[Any]				=	'bar'

         with tempfile.TemporaryDirectory('test-generation-config') as tmp_dir:
              generation_config.save_pretrained(a)

              lowercase__   :       List[str]				=	GenerationConfig.from_pretrained(a)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo						,       'bar')

         lowercase__   :       Optional[Any]				=	GenerationConfig.from_model_config(a)
         assert not hasattr(a						,       'foo')  # no new kwargs should be initialized if from config





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	GenerationConfig()
         self.assertEqual(default_config.temperature						,       1.0)
         self.assertEqual(default_config.do_sample						,       a)
         self.assertEqual(default_config.num_beams						,       1)

         lowercase__   :       str				=	GenerationConfig(
             do_sample=a						,       temperature=0.7						,       length_penalty=1.0						,       bad_words_ids=[[1, 2, 3], [4, 5]]						,       )
         self.assertEqual(config.temperature						,       0.7)
         self.assertEqual(config.do_sample						,       a)
         self.assertEqual(config.num_beams						,       1)

         with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(a)
              lowercase__   :       Optional[Any]				=	GenerationConfig.from_pretrained(a						,       temperature=1.0)

         self.assertEqual(loaded_config.temperature						,       1.0)
         self.assertEqual(loaded_config.do_sample						,       a)
         self.assertEqual(loaded_config.num_beams						,       1)  # default value



@is_staging_test
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @classmethod
    def 				snake_case_    (				cls):
         lowercase__   :       int				=	TOKEN
         HfFolder.save_token(a)





    @classmethod
    def 				snake_case_    (				cls):
         try:
              delete_repo(token=cls._token						,       repo_id='test-generation-config')
         except HTTPError:
              pass

         try:
              delete_repo(token=cls._token						,       repo_id='valid_org/test-generation-config-org')
         except HTTPError:
              pass





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	GenerationConfig(
             do_sample=a						,       temperature=0.7						,       length_penalty=1.0						,       )
         config.push_to_hub('test-generation-config'						,       use_auth_token=self._token)

         lowercase__   :       str				=	GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""")
         for k, v in config.to_dict().items():
              if k != "transformers_version":
                   self.assertEqual(a						,       getattr(a						,       a))

        # Reset repo
         delete_repo(token=self._token						,       repo_id='test-generation-config')

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  a						,       repo_id='test-generation-config'						,       push_to_hub=a						,       use_auth_token=self._token)

         lowercase__   :       Any				=	GenerationConfig.from_pretrained(f"""{USER}/test-generation-config""")
         for k, v in config.to_dict().items():
              if k != "transformers_version":
                   self.assertEqual(a						,       getattr(a						,       a))





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	GenerationConfig(
             do_sample=a						,       temperature=0.7						,       length_penalty=1.0						,       )
         config.push_to_hub('valid_org/test-generation-config-org'						,       use_auth_token=self._token)

         lowercase__   :       List[str]				=	GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
         for k, v in config.to_dict().items():
              if k != "transformers_version":
                   self.assertEqual(a						,       getattr(a						,       a))

        # Reset repo
         delete_repo(token=self._token						,       repo_id='valid_org/test-generation-config-org')

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  a						,       repo_id='valid_org/test-generation-config-org'						,       push_to_hub=a						,       use_auth_token=self._token)

         lowercase__   :       List[str]				=	GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
         for k, v in config.to_dict().items():
              if k != "transformers_version":
                   self.assertEqual(a						,       getattr(a						,       a))




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

