



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		_a			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			int =				None
    __lowerCamelCase      :			Union[str, Any] =				BloomTokenizerFast
    __lowerCamelCase      :			str =				BloomTokenizerFast
    __lowerCamelCase      :			List[str] =				True
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			List[str] =				"tokenizer_file"
    __lowerCamelCase      :			Optional[int] =				{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}






    def       _lowerCAmelCase (	self		):
           super().setUp()
           A			:	List[str]										=       BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""		)
           tokenizer.save_pretrained(self.tmpdirname		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
           kwargs.update(self.special_tokens_map		)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname,		**snake_case_		)






    def       _lowerCAmelCase (	self		):
           A			:	Union[str, Any]										=       self.get_rust_tokenizer()

           A			:	Union[str, Any]										=       ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           A			:	str										=       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

           A			:	int										=       tokenizer.batch_encode_plus(snake_case_		)["""input_ids"""]
           self.assertListEqual(snake_case_,		snake_case_		)

           A			:	str										=       tokenizer.batch_decode(snake_case_		)
           self.assertListEqual(snake_case_,		snake_case_		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__=6		):
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                         A			:	Any										=       self.rust_tokenizer_class.from_pretrained(snake_case_,		**snake_case_		)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         A			:	str										=       """This is a simple input"""
                         A			:	Optional[Any]										=       ["""This is a simple input 1""", """This is a simple input 2"""]
                         A			:	List[str]										=       ("""This is a simple input""", """This is a pair""")
                         A			:	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
                         try:
                                tokenizer_r.encode(snake_case_,		max_length=snake_case_		)
                                tokenizer_r.encode_plus(snake_case_,		max_length=snake_case_		)

                                tokenizer_r.batch_encode_plus(snake_case_,		max_length=snake_case_		)
                                tokenizer_r.encode(snake_case_,		max_length=snake_case_		)
                                tokenizer_r.batch_encode_plus(snake_case_,		max_length=snake_case_		)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""		)

                         A			:	Union[str, Any]										=       None  # Hotfixing padding = None
                         self.assertRaises(snake_case_,		tokenizer_r.encode,		snake_case_,		max_length=snake_case_,		padding="""max_length"""		)

                         # Simple input
                         self.assertRaises(snake_case_,		tokenizer_r.encode_plus,		snake_case_,		max_length=snake_case_,		padding="""max_length"""		)

                         # Simple input
                         self.assertRaises(
                             snake_case_,		tokenizer_r.batch_encode_plus,		snake_case_,		max_length=snake_case_,		padding="""max_length""",		)

                         # Pair input
                         self.assertRaises(snake_case_,		tokenizer_r.encode,		snake_case_,		max_length=snake_case_,		padding="""max_length"""		)

                         # Pair input
                         self.assertRaises(snake_case_,		tokenizer_r.encode_plus,		snake_case_,		max_length=snake_case_,		padding="""max_length"""		)

                         # Pair input
                         self.assertRaises(
                             snake_case_,		tokenizer_r.batch_encode_plus,		snake_case_,		max_length=snake_case_,		padding="""max_length""",		)






    def       _lowerCAmelCase (	self		):
           A			:	Tuple										=       self.get_rust_tokenizer()
           A			:	Any										=       load_dataset("""xnli""",		"""all_languages""",		split="""test""",		streaming=snake_case_		)

           A			:	Dict										=       next(iter(snake_case_		)		)["""premise"""]  # pick up one data
           A			:	Tuple										=       list(sample_data.values()		)

           A			:	Union[str, Any]										=       list(map(tokenizer.encode,		snake_case_		)		)
           A			:	Optional[Any]										=       [tokenizer.decode(snake_case_,		clean_up_tokenization_spaces=snake_case_		) for x in output_tokens]
           self.assertListEqual(snake_case_,		snake_case_		)






    def       _lowerCAmelCase (	self		):
           # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
           # any sequence length constraints. This test of the parent class will fail since it relies on the
           # maximum sequence length of the positoonal embeddings.
           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map		),		1		)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()		)[0]		),		1		)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A			:	List[str]										=       vocab_size
        A			:	List[Any]										=       hidden_size
        A			:	List[Any]										=       num_hidden_layers
        A			:	Optional[Any]										=       num_attention_heads
        A			:	Union[str, Any]										=       hidden_act
        A			:	Union[str, Any]										=       intermediate_size
        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	int										=       max_position_embeddings
        A			:	List[str]										=       type_vocab_size
        A			:	List[str]										=       initializer_range
        A			:	Dict										=       layer_norm_eps
        A			:	Optional[int]										=       position_embedding_type
        A			:	Any										=       use_cache
        A			:	Optional[int]										=       classifier_dropout






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


