





"""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       __a  (    lowerCAmelCase__      ,		unittest.TestCase						):
     SCREAMING_SNAKE_CASE__					: List[Any]									=     CLIPTokenizer
     SCREAMING_SNAKE_CASE__					: List[Any]									=     CLIPTokenizerFast
     SCREAMING_SNAKE_CASE__					: Union[str, Any]									=     True
     SCREAMING_SNAKE_CASE__					: Any									=     {}
     SCREAMING_SNAKE_CASE__					: Union[str, 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(snake_case_   ,	range(len(snake_case_  )  )  )  )
            _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(snake_case_  ) + '\n'  )
            with open(self.merges_file   ,	'w'   ,	encoding='utf-8'  ) as fp:
                   fp.write('\n'.join(snake_case_  )  )



     def 		snake_case_     (			self   ,	**a__  ):
            kwargs.update(self.special_tokens_map  )
            return CLIPTokenizer.from_pretrained(self.tmpdirname   ,	**snake_case_  )



     def 		snake_case_     (			self   ,	**a__  ):
            kwargs.update(self.special_tokens_map  )
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,	**snake_case_  )



     def 		snake_case_     (			self   ,	a__  ):
            _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(snake_case_  )
            self.assertListEqual(snake_case_   ,	snake_case_  )

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



     @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(snake_case_   ,	**snake_case_  )
                          _lowerCamelCase             =   self.rust_tokenizer_class.from_pretrained(snake_case_   ,	**snake_case_  )

                          _lowerCamelCase             =   'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                          _lowerCamelCase             =   tokenizer_s.tokenize(snake_case_  )
                          _lowerCamelCase             =   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
                          _lowerCamelCase             =   'xa\u0303y' + ' ' + 'x\xe3y'
                          _lowerCamelCase             =   tokenizer_s.tokenize(snake_case_  )
                          _lowerCamelCase             =   tokenizer_r.tokenize(snake_case_  )

                          self.assertListEqual(snake_case_   ,	snake_case_  )

                          # 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(snake_case_  )
                                 _lowerCamelCase             =   tokenizer_r.tokenize(snake_case_  )

                                 self.assertListEqual(snake_case_   ,	snake_case_  )

                          # 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(snake_case_  )
                                 _lowerCamelCase             =   tokenizer_r.tokenize(snake_case_  )

                                 self.assertListEqual(snake_case_   ,	snake_case_  )



     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(
                              snake_case_   ,	use_fast=snake_case_   ,	)
                          _lowerCamelCase             =   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_  ))   ,	)

                          _lowerCamelCase             =   F' {text}'

                          _lowerCamelCase             =   self.rust_tokenizer_class.from_pretrained(
                              snake_case_   ,	use_fast=snake_case_   ,	)
                          _lowerCamelCase             =   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 		snake_case_     (			self  ):

            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 		snake_case_     (			self  ):
            super().test_tokenization_python_rust_equals()



     def 		snake_case_     (			self  ):
            pass










# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

a			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
a			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					build_dir / VOCAB_FILES_NAMES['''merges_file''']
	with open(src_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, '''w''') as fp:
		fp.write('''\n'''.join(merges))

	a			:			Optional[int]												=					FSMTTokenizer(
	    langs=['''en''', '''ru'''],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

a			:			List[str]												=					FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

a			:			Union[str, Any]												=					FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
a			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru


