






"""simple docstring"""



import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							CLIPTokenizer
							snake_case_     =							CLIPTokenizerFast
							snake_case_     =							True
							snake_case_     =							{}
							snake_case_     =							False


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													super().setUp()

													# fmt: off
													A__						:  Optional[Any]		  =  ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
													# fmt: on
													A__						:  List[str]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Union[str, Any]		  =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
													A__						:  Optional[int]		  =  {"""unk_token""": """<unk>"""}

													A__						:  List[str]		  =  os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  Optional[int]		  =  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(snake_case							) + """\n"""							)
													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""							) as fp:
																			fp.write("""\n""".join(snake_case							)							)


							def 						_UpperCamelCase       (			self      :						int			,    **snake_case      :						str							):
													'''simple docstring'''





													kwargs.update(self.special_tokens_map							)
													return CLIPTokenizer.from_pretrained(self.tmpdirname			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    **snake_case      :						List[str]							):
													'''simple docstring'''





													kwargs.update(self.special_tokens_map							)
													return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  int		  =  """lower newer"""
													A__						:  List[str]		  =  """lower newer"""
													return input_text, output_text


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Any		  =  CLIPTokenizer(self.vocab_file			,    self.merges_file			,    **self.special_tokens_map							)
													A__						:  Union[str, Any]		  =  """lower newer"""
													A__						:  Optional[Any]		  =  ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
													A__						:  Optional[Any]		  =  tokenizer.tokenize(snake_case							)
													self.assertListEqual(snake_case			,    snake_case							)

													A__						:  str		  =  tokens + [tokenizer.unk_token]
													A__						:  List[Any]		  =  [10, 2, 16, 9, 3, 2, 16, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case							)			,    snake_case							)


							@require_ftfy
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																									A__						:  Any		  =  self.tokenizer_class.from_pretrained(snake_case			,    **snake_case							)
																									A__						:  Optional[int]		  =  self.rust_tokenizer_class.from_pretrained(snake_case			,    **snake_case							)

																									A__						:  Dict		  =  """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
																									A__						:  Union[str, Any]		  =  tokenizer_s.tokenize(snake_case							)
																									A__						:  Tuple		  =  tokenizer_r.tokenize(snake_case							)

																									self.assertListEqual(snake_case			,    snake_case							)

																									# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																									# with Tilde) encoded in 2 different ways
																									A__						:  Dict		  =  """xa\u0303y""" + """ """ + """x\xe3y"""
																									A__						:  Tuple		  =  tokenizer_s.tokenize(snake_case							)
																									A__						:  str		  =  tokenizer_r.tokenize(snake_case							)

																									self.assertListEqual(snake_case			,    snake_case							)

																									# Test that the tokenization is identical on unicode of space type
																									A__						:  Any		  =  [
																									    """\u0009""",  # (horizontal tab, '\t')
																									    """\u000B""",  # (vertical tab)
																									    """\u000C""",  # (form feed)
																									    """\u0020""",  # (space, ' ')
																									    """\u200E""",  # (left-to-right mark):w
																									    """\u200F""",  # (right-to-left mark)
																									]
																									for unicode_seq in spaces_unicodes:
																															A__						:  List[Any]		  =  tokenizer_s.tokenize(snake_case							)
																															A__						:  Union[str, Any]		  =  tokenizer_r.tokenize(snake_case							)

																															self.assertListEqual(snake_case			,    snake_case							)

																									# Test that the tokenization is identical on unicode of line break type
																									A__						:  Optional[int]		  =  [
																									    """\u000A""",  # (line feed, '\n')
																									    """\r\n""",  # (carriage return and line feed, '\r\n')
																									    """\u000D""",  # (carriage return, '\r')
																									    """\r""",  # (carriage return, '\r')
																									    """\u000D""",  # (carriage return, '\r')
																									    """\u2028""",  # (line separator)
																									    """\u2029""",  # (paragraph separator)
																									    # "\u0085", # (next line)
																									]

																									# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																									# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																									# space (and thus into an empty list).

																									for unicode_seq in line_break_unicodes:
																															A__						:  Tuple		  =  tokenizer_s.tokenize(snake_case							)
																															A__						:  Dict		  =  tokenizer_r.tokenize(snake_case							)

																															self.assertListEqual(snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																									A__						:  Union[str, Any]		  =  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																									A__						:  str		  =  F'{text_of_1_token} {text_of_1_token}'

																									A__						:  Dict		  =  self.rust_tokenizer_class.from_pretrained(
																									    snake_case			,    use_fast=snake_case			,    )
																									A__						:  List[str]		  =  tokenizer_r(snake_case			,    return_offsets_mapping=snake_case			,    add_special_tokens=snake_case							)
																									self.assertEqual(encoding.offset_mapping[0]			,    (0, len(snake_case							))							)
																									self.assertEqual(
																									    encoding.offset_mapping[1]			,    (len(snake_case							) + 1, len(snake_case							) + 1 + len(snake_case							))			,    )

																									A__						:  Optional[int]		  =  F' {text}'

																									A__						:  Tuple		  =  self.rust_tokenizer_class.from_pretrained(
																									    snake_case			,    use_fast=snake_case			,    )
																									A__						:  Optional[Any]		  =  tokenizer_r(snake_case			,    return_offsets_mapping=snake_case			,    add_special_tokens=snake_case							)
																									self.assertEqual(encoding.offset_mapping[0]			,    (1, 1 + len(snake_case							))							)
																									self.assertEqual(
																									    encoding.offset_mapping[1]			,    (1 + len(snake_case							) + 1, 1 + len(snake_case							) + 1 + len(snake_case							))			,    )


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													with self.assertRaises(snake_case							) as context:
																			self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""							)

													self.assertTrue(
													    context.exception.args[0].startswith(
													        """The `backend_tokenizer` provided does not match the expected format."""							)							)


							@require_ftfy
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													super().test_tokenization_python_rust_equals()


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													pass














"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    =							logging.get_logger(__name__)

A_    =							{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'roc_bert'


							def __init__(			self      :						Tuple			,    snake_case      :						Union[str, Any]=3_0522			,    snake_case      :						Optional[Any]=768			,    snake_case      :						Any=12			,    snake_case      :						Tuple=12			,    snake_case      :						Any=3072			,    snake_case      :						Tuple="gelu"			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						List[str]=512			,    snake_case      :						int=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						str=1e-12			,    snake_case      :						int=True			,    snake_case      :						Optional[Any]=0			,    snake_case      :						str="absolute"			,    snake_case      :						int=None			,    snake_case      :						int=True			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=768			,    snake_case      :						Dict=910			,    snake_case      :						Tuple=512			,    snake_case      :						Tuple=2_4858			,    snake_case      :						Optional[Any]=True			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  hidden_size
													A__						:  Optional[int]		  =  num_hidden_layers
													A__						:  Optional[Any]		  =  num_attention_heads
													A__						:  int		  =  intermediate_size
													A__						:  Optional[Any]		  =  hidden_act
													A__						:  Any		  =  hidden_dropout_prob
													A__						:  Tuple		  =  attention_probs_dropout_prob
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  str		  =  type_vocab_size
													A__						:  Any		  =  layer_norm_eps
													A__						:  List[str]		  =  use_cache
													A__						:  List[str]		  =  enable_pronunciation
													A__						:  int		  =  enable_shape
													A__						:  Tuple		  =  pronunciation_embed_dim
													A__						:  str		  =  pronunciation_vocab_size
													A__						:  Optional[Any]		  =  shape_embed_dim
													A__						:  str		  =  shape_vocab_size
													A__						:  List[str]		  =  concat_input
													A__						:  str		  =  position_embedding_type
													A__						:  str		  =  classifier_dropout
													super().__init__(pad_token_id=snake_case			,    **snake_case							)






