'''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 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						CLIPTokenizer
      __UpperCamelCase:  List[Any]						=						CLIPTokenizerFast
      __UpperCamelCase:  List[str]						=						True
      __UpperCamelCase:  Any						=						{}
      __UpperCamelCase:  Any						=						False




      def      _A	(       self						:				Any       ):
             super().setUp()

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

             _UpperCAmelCase     :  List[Any]							      =    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES["vocab_file"]       )
             _UpperCAmelCase     :  Optional[Any]							      =    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      _A	(       self						:				Optional[int]						,					**A						:				Optional[Any]       ):
             kwargs.update(self.special_tokens_map       )
             return CLIPTokenizer.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Tuple						,					**A						:				int       ):
             kwargs.update(self.special_tokens_map       )
             return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Tuple						,					A						:				Any       ):
             _UpperCAmelCase     :  int							      =    "lower newer"
             _UpperCAmelCase     :  Union[str, Any]							      =    "lower newer"
             return input_text, output_text




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[Any]							      =    CLIPTokenizer(self.vocab_file						,					self.merges_file						,					**self.special_tokens_map       )
             _UpperCAmelCase     :  Dict							      =    "lower newer"
             _UpperCAmelCase     :  str							      =    ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
             _UpperCAmelCase     :  List[str]							      =    tokenizer.tokenize(A       )
             self.assertListEqual(A						,					A       )

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




      @require_ftfy
      def      _A	(       self						:				Any       ):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Union[str, Any]							      =    self.tokenizer_class.from_pretrained(A						,					**A       )
                           _UpperCAmelCase     :  Optional[int]							      =    self.rust_tokenizer_class.from_pretrained(A						,					**A       )

                           _UpperCAmelCase     :  Optional[Any]							      =    "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
                           _UpperCAmelCase     :  Any							      =    tokenizer_s.tokenize(A       )
                           _UpperCAmelCase     :  List[str]							      =    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     :  List[Any]							      =    "xa\u0303y" + " " + "x\xe3y"
                           _UpperCAmelCase     :  List[str]							      =    tokenizer_s.tokenize(A       )
                           _UpperCAmelCase     :  Optional[Any]							      =    tokenizer_r.tokenize(A       )

                           self.assertListEqual(A						,					A       )

                           # Test that the tokenization is identical on unicode of space type
                           _UpperCAmelCase     :  List[str]							      =    [
                               "\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     :  Any							      =    tokenizer_s.tokenize(A       )
                                  _UpperCAmelCase     :  int							      =    tokenizer_r.tokenize(A       )

                                  self.assertListEqual(A						,					A       )

                           # Test that the tokenization is identical on unicode of line break type
                           _UpperCAmelCase     :  Dict							      =    [
                               "\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     :  Optional[int]							      =    tokenizer_s.tokenize(A       )
                                  _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.tokenize(A       )

                                  self.assertListEqual(A						,					A       )




      def      _A	(       self						:				List[Any]       ):
             # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Optional[int]							      =    "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                           _UpperCAmelCase     :  Optional[Any]							      =    F"""{text_of_1_token} {text_of_1_token}"""

                           _UpperCAmelCase     :  Optional[Any]							      =    self.rust_tokenizer_class.from_pretrained(
                               A						,					use_fast=A						,					)
                           _UpperCAmelCase     :  int							      =    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     :  str							      =    F""" {text}"""

                           _UpperCAmelCase     :  List[Any]							      =    self.rust_tokenizer_class.from_pretrained(
                               A						,					use_fast=A						,					)
                           _UpperCAmelCase     :  Optional[Any]							      =    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      _A	(       self						:				Dict       ):
             # Test related to the breaking change introduced in transformers v4.17.0
             # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
             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      _A	(       self						:				Dict       ):
             super().test_tokenization_python_rust_equals()





      def      _A	(       self						:				str       ):
             # CLIP always lower cases letters
             pass






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__snake_case							:   Optional[int]      =    logging.get_logger(__name__)



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'maskformer-swin'

	SCREAMING_SNAKE_CASE          =	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int=224       ,			_SCREAMING_SNAKE_CASE:		Tuple=4       ,			_SCREAMING_SNAKE_CASE:		int=3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=96       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=[2, 2, 6, 2]       ,			_SCREAMING_SNAKE_CASE:		Any=[3, 6, 12, 24]       ,			_SCREAMING_SNAKE_CASE:		List[str]=7       ,			_SCREAMING_SNAKE_CASE:		List[str]=4.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.1       ,			_SCREAMING_SNAKE_CASE:		str="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		str=1e-5       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       List[str]:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       image_size
				__lowerCAmelCase      :  Any												=       patch_size
				__lowerCAmelCase      :  Tuple												=       num_channels
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  Any												=       depths
				__lowerCAmelCase      :  Dict												=       len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       window_size
				__lowerCAmelCase      :  Dict												=       mlp_ratio
				__lowerCAmelCase      :  Any												=       qkv_bias
				__lowerCAmelCase      :  Union[str, Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  int												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Tuple												=       drop_path_rate
				__lowerCAmelCase      :  int												=       hidden_act
				__lowerCAmelCase      :  Optional[int]												=       use_absolute_embeddings
				__lowerCAmelCase      :  List[str]												=       layer_norm_eps
				__lowerCAmelCase      :  Any												=       initializer_range
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				__lowerCAmelCase      :  Optional[Any]												=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE) - 1))
				__lowerCAmelCase      :  Any												=       ["stem"] + [F"""stage{idx}""" for idx in range(1       ,			len(_SCREAMING_SNAKE_CASE) + 1)]
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       get_aligned_output_features_output_indices(
				    out_features=_SCREAMING_SNAKE_CASE       ,			out_indices=_SCREAMING_SNAKE_CASE       ,			stage_names=self.stage_names)