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 							__UpperCamelCase			( lowercase_     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Any										=CLIPTokenizer
				__a							: str										=CLIPTokenizerFast
				__a							: int										=True
				__a							: Tuple										={}
				__a							: Any										=False



				def       __snake_case		( self						):
											super().setUp()

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

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



				def       __snake_case		( self       , **UpperCAmelCase_						):
											kwargs.update(self.special_tokens_map						)
											return CLIPTokenizer.from_pretrained(self.tmpdirname       , **lowerCamelCase_						)



				def       __snake_case		( self       , **UpperCAmelCase_						):
											kwargs.update(self.special_tokens_map						)
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       , **lowerCamelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		"""lower newer"""
											lowerCAmelCase   =		"""lower newer"""
											return input_text, output_text



				def       __snake_case		( self						):
											lowerCAmelCase   =		CLIPTokenizer(self.vocab_file       , self.merges_file       , **self.special_tokens_map						)
											lowerCAmelCase   =		"""lower newer"""
											lowerCAmelCase   =		["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
											lowerCAmelCase   =		tokenizer.tokenize(lowerCamelCase_						)
											self.assertListEqual(lowerCamelCase_       , lowerCamelCase_						)

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



				@require_ftfy
				def       __snake_case		( self						):



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

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

																									self.assertListEqual(lowerCamelCase_       , lowerCamelCase_						)

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

																									self.assertListEqual(lowerCamelCase_       , lowerCamelCase_						)

																									# Test that the tokenization is identical on unicode of space type
																									lowerCAmelCase   =		[
																									    """\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:
																																lowerCAmelCase   =		tokenizer_s.tokenize(lowerCamelCase_						)
																																lowerCAmelCase   =		tokenizer_r.tokenize(lowerCamelCase_						)

																																self.assertListEqual(lowerCamelCase_       , lowerCamelCase_						)

																									# Test that the tokenization is identical on unicode of line break type
																									lowerCAmelCase   =		[
																									    """\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:
																																lowerCAmelCase   =		tokenizer_s.tokenize(lowerCamelCase_						)
																																lowerCAmelCase   =		tokenizer_r.tokenize(lowerCamelCase_						)

																																self.assertListEqual(lowerCamelCase_       , lowerCamelCase_						)



				def       __snake_case		( self						):



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

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

																									lowerCAmelCase   =		F""" {text}"""

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



				def       __snake_case		( self						):



											with self.assertRaises(lowerCamelCase_						) 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       __snake_case		( self						):
											super().test_tokenization_python_rust_equals()






				def       __snake_case		( self						):
											pass









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


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		window_size
											lowerCAmelCase   =		mlp_ratio
											lowerCAmelCase   =		qkv_bias
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		drop_path_rate
											lowerCAmelCase   =		hidden_act
											lowerCAmelCase   =		use_absolute_embeddings
											lowerCAmelCase   =		layer_norm_eps
											lowerCAmelCase   =		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   =		int(embed_dim * 2 ** (len(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



