



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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					CLIPTokenizer
			UpperCamelCase        =					CLIPTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					{}
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  List[str])       ->   List[str]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase			=       ['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
						_UpperCAmelCase			=       dict(zip(A			,		range(len(A))))
						_UpperCAmelCase			=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
						_UpperCAmelCase			=       {'unk_token': '<unk>'}

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       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(A) + '\n')
						with open(self.merges_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write('\n'.join(A))



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		**A       :  str)       ->   Optional[int]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Any			,		**A       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       'lower newer'
						return input_text, output_text



			def    _lowerCamelCase	(						self       :  Dict)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map)
						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
						_UpperCAmelCase			=       tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokens + [tokenizer.unk_token]
						_UpperCAmelCase			=       [10, 2, 16, 9, 3, 2, 16, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)			,		A)



			@require_ftfy
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.tokenizer_class.from_pretrained(A			,		**A)
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(A			,		**A)

												_UpperCAmelCase			=       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												_UpperCAmelCase			=       tokenizer_s.tokenize(A)
												_UpperCAmelCase			=       tokenizer_r.tokenize(A)

												self.assertListEqual(A			,		A)

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

												self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of space type
												_UpperCAmelCase			=       [
												    '\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:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of line break type
												_UpperCAmelCase			=       [
												    '\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:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)



			def    _lowerCamelCase	(						self       :  str)       ->   Optional[Any]:



						"""simple docstring"""






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

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(0, len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(len(A) + 1, len(A) + 1 + len(A))			,		)

												_UpperCAmelCase			=       F" {text}"

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(1, 1 + len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(1 + len(A) + 1, 1 + len(A) + 1 + len(A))			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						with self.assertRaises(A) 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    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						super().test_tokenization_python_rust_equals()



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



						"""simple docstring"""






						pass




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["LxmertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "LxmertEncoder",
							    "LxmertForPreTraining",
							    "LxmertForQuestionAnswering",
							    "LxmertModel",
							    "LxmertPreTrainedModel",
							    "LxmertVisualFeatureEncoder",
							    "LxmertXLayer",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFLxmertForPreTraining",
							    "TFLxmertMainLayer",
							    "TFLxmertModel",
							    "TFLxmertPreTrainedModel",
							    "TFLxmertVisualFeatureEncoder",
							]


if TYPE_CHECKING:
							from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
							from .tokenization_lxmert import LxmertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_lxmert_fast import LxmertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lxmert import (
														    LxmertEncoder,
														    LxmertForPreTraining,
														    LxmertForQuestionAnswering,
														    LxmertModel,
														    LxmertPreTrainedModel,
														    LxmertVisualFeatureEncoder,
														    LxmertXLayer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_lxmert import (
														    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLxmertForPreTraining,
														    TFLxmertMainLayer,
														    TFLxmertModel,
														    TFLxmertPreTrainedModel,
														    TFLxmertVisualFeatureEncoder,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
