"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     _a      :		int           =   'pt'
elif is_tf_available():
     _a      :		Union[str, Any]           =   'tf'
else:
     _a      :		Union[str, Any]           =   'jax'
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Tuple							=		ByTaTokenizer
     _UpperCamelCase							:			Union[str, Any]							=		False






     def     __A  (		self  ):
       super().setUp()
       _lowerCAmelCase						:       Tuple  			=     ByTaTokenizer()
       tokenizer.save_pretrained(self.tmpdirname  )






     @cached_property
     def     __A  (		self  ):
       return ByTaTokenizer.from_pretrained("""google/byt5-small"""  )






     def     __A  (		self				,     **a__  ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **a__  )






     def     __A  (		self				,     a__				,     a__=False				,     a__=20				,     a__=5  ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for ByT5 because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       _lowerCAmelCase						:       Optional[int]  			=     []
       for i in range(len(a__  )  ):
         try:
           _lowerCAmelCase						:       int  			=     tokenizer.decode([i]				,     clean_up_tokenization_spaces=a__  )
         except UnicodeDecodeError:
           pass
         toks.append((i, tok)  )

       _lowerCAmelCase						:       Tuple  			=     list(filter(lambda a__  : re.match(r"""^[ a-zA-Z]+$"""				,     t[1]  )				,     a__  )  )
       _lowerCAmelCase						:       str  			=     list(filter(lambda a__  : [t[0]] == tokenizer.encode(t[1]				,     add_special_tokens=a__  )				,     a__  )  )
       if max_length is not None and len(a__  ) > max_length:
         _lowerCAmelCase						:       Optional[Any]  			=     toks[:max_length]
       if min_length is not None and len(a__  ) < min_length and len(a__  ) > 0:
         while len(a__  ) < min_length:
           _lowerCAmelCase						:       str  			=     toks + toks
        # toks_str = [t[1] for t in toks]
       _lowerCAmelCase						:       Any  			=     [t[0] for t in toks]

       # Ensure consistency
       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.decode(a__				,     clean_up_tokenization_spaces=a__  )
       if " " not in output_txt and len(a__  ) > 1:
         _lowerCAmelCase						:       List[str]  			=     (
             tokenizer.decode([toks_ids[0]]				,     clean_up_tokenization_spaces=a__  )
             + """ """
             + tokenizer.decode(toks_ids[1:]				,     clean_up_tokenization_spaces=a__  )
         )
       if with_prefix_space:
         _lowerCAmelCase						:       int  			=     """ """ + output_txt
       _lowerCAmelCase						:       Dict  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       return output_txt, output_ids






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]  )
       _lowerCAmelCase						:       Dict  			=     tokenizer(["""hi""", """I went to the gym""", """"""]  )
       self.assertListEqual(batch_with_eos_added["""input_ids"""]				,     batch_without_eos_added["""input_ids"""]  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       List[str]  			=     """Unicode €."""
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
       self.assertEqual(encoded["""input_ids"""]				,     a__  )

       # decoding
       _lowerCAmelCase						:       Optional[int]  			=     tokenizer.decode(a__  )
       self.assertEqual(a__				,     """Unicode €.</s>"""  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer("""e è é ê ë"""  )
       _lowerCAmelCase						:       Any  			=     [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
       self.assertEqual(encoded["""input_ids"""]				,     a__  )
       # decoding
       _lowerCAmelCase						:       Any  			=     tokenizer.decode(a__  )
       self.assertEqual(a__				,     """e è é ê ë</s>"""  )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""  )  )				,     """e è é ê ë</s>"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       Optional[int]  			=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       _lowerCAmelCase						:       str  			=     [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
       # fmt: on
       _lowerCAmelCase						:       List[str]  			=     tokenizer(a__				,     padding=a__				,     return_tensors=a__  )
       self.assertIsInstance(a__				,     a__  )

       if FRAMEWORK != "jax":
         _lowerCAmelCase						:       List[str]  			=     list(batch.input_ids.numpy()[0]  )
       else:
         _lowerCAmelCase						:       Optional[Any]  			=     list(batch.input_ids.tolist()[0]  )

       self.assertListEqual(a__				,     a__  )

       self.assertEqual((2, 37)				,     batch.input_ids.shape  )
       self.assertEqual((2, 37)				,     batch.attention_mask.shape  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       Union[str, Any]  			=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       _lowerCAmelCase						:       List[str]  			=     tokenizer(a__				,     padding=a__				,     return_tensors=a__  )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""				,     a__  )
       self.assertIn("""attention_mask"""				,     a__  )
       self.assertNotIn("""decoder_input_ids"""				,     a__  )
       self.assertNotIn("""decoder_attention_mask"""				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       Any  			=     [
           """Summary of the text.""",
           """Another summary.""",
       ]
       _lowerCAmelCase						:       Any  			=     tokenizer(
           text_target=a__				,     max_length=32				,     padding="""max_length"""				,     truncation=a__				,     return_tensors=a__  )
       self.assertEqual(32				,     targets["""input_ids"""].shape[1]  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.ta_base_tokenizer
       _lowerCAmelCase						:       List[str]  			=     ["""A long paragraph for summarization. </s>"""]
       _lowerCAmelCase						:       List[str]  			=     ["""Summary of the text. </s>"""]
       # fmt: off
       _lowerCAmelCase						:       Union[str, Any]  			=     [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
       _lowerCAmelCase						:       List[str]  			=     [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
       # fmt: on

       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(a__				,     text_target=a__  )

       self.assertEqual(a__				,     batch["""input_ids"""][0]  )
       self.assertEqual(a__				,     batch["""labels"""][0]  )






     def     __A  (		self  ):
       # safety check on max_len default value so we are sure the test works
       _lowerCAmelCase						:       Optional[int]  			=     self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           self.assertNotEqual(tokenizer.model_max_length				,     42  )

        # Now let's start the test
       _lowerCAmelCase						:       Optional[int]  			=     self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           # Isolate this from the other tests because we save additional tokens/etc
           _lowerCAmelCase						:       Union[str, Any]  			=     tempfile.mkdtemp()

           _lowerCAmelCase						:       Tuple  			=     """ He is very happy, UNwant\u00E9d,running"""
           _lowerCAmelCase						:       Optional[int]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
           tokenizer.save_pretrained(a__  )

           _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.__class__.from_pretrained(a__  )
           _lowerCAmelCase						:       Any  			=     after_tokenizer.encode(a__				,     add_special_tokens=a__  )
           self.assertListEqual(a__				,     a__  )

           shutil.rmtree(a__  )

       _lowerCAmelCase						:       Dict  			=     self.get_tokenizers(model_max_length=42  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           # Isolate this from the other tests because we save additional tokens/etc
           _lowerCAmelCase						:       List[str]  			=     tempfile.mkdtemp()

           _lowerCAmelCase						:       Dict  			=     """ He is very happy, UNwant\u00E9d,running"""
           tokenizer.add_tokens(["""bim""", """bambam"""]  )
           _lowerCAmelCase						:       Any  			=     tokenizer.additional_special_tokens
           additional_special_tokens.append("""new_additional_special_token"""  )
           tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
           _lowerCAmelCase						:       List[str]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
           tokenizer.save_pretrained(a__  )

           _lowerCAmelCase						:       str  			=     tokenizer.__class__.from_pretrained(a__  )
           _lowerCAmelCase						:       List[str]  			=     after_tokenizer.encode(a__				,     add_special_tokens=a__  )
           self.assertListEqual(a__				,     a__  )
           self.assertIn("""new_additional_special_token"""				,     after_tokenizer.additional_special_tokens  )
           self.assertEqual(after_tokenizer.model_max_length				,     42  )

           _lowerCAmelCase						:       Any  			=     tokenizer.__class__.from_pretrained(a__				,     model_max_length=43  )
           self.assertEqual(tokenizer.model_max_length				,     43  )

           shutil.rmtree(a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     []
       if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

       if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(a__  )

           with open(os.path.join(a__				,     """special_tokens_map.json"""  )				,     encoding="""utf-8"""  ) as json_file:
             _lowerCAmelCase						:       List[Any]  			=     json.load(a__  )

           with open(os.path.join(a__				,     """tokenizer_config.json"""  )				,     encoding="""utf-8"""  ) as json_file:
             _lowerCAmelCase						:       Tuple  			=     json.load(a__  )

           _lowerCAmelCase						:       int  			=     [F"<extra_id_{i}>" for i in range(125  )]

           _lowerCAmelCase						:       List[str]  			=     added_tokens_extra_ids + [
               """an_additional_special_token"""
           ]
           _lowerCAmelCase						:       Tuple  			=     added_tokens_extra_ids + [
               """an_additional_special_token"""
           ]

           with open(os.path.join(a__				,     """special_tokens_map.json"""  )				,     """w"""				,     encoding="""utf-8"""  ) as outfile:
             json.dump(a__				,     a__  )
           with open(os.path.join(a__				,     """tokenizer_config.json"""  )				,     """w"""				,     encoding="""utf-8"""  ) as outfile:
             json.dump(a__				,     a__  )

           # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
           # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
           # "special_tokens_map.json" files
           _lowerCAmelCase						:       str  			=     tokenizer_class.from_pretrained(
               a__				,     )
           self.assertIn(
               """an_additional_special_token"""				,     tokenizer_without_change_in_init.additional_special_tokens  )
           # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
           self.assertEqual(
               ["""an_additional_special_token"""]				,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                   tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]  )  )				,     )

           # Now we test that we can change the value of additional_special_tokens in the from_pretrained
           _lowerCAmelCase						:       Optional[Any]  			=     added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,     lstrip=a__  )]
           _lowerCAmelCase						:       Dict  			=     tokenizer_class.from_pretrained(
               a__				,     additional_special_tokens=a__				,     )

           self.assertIn("""a_new_additional_special_token"""				,     tokenizer.additional_special_tokens  )
           self.assertEqual(
               ["""a_new_additional_special_token"""]				,     tokenizer.convert_ids_to_tokens(
                   tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]  )  )				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     []
       if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

       if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(a__  )

           _lowerCAmelCase						:       Optional[Any]  			=     tokenizer_class.from_pretrained(a__  )

           self.assertTrue(tokenizer.decode([255]  ) == """"""  )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
       # and special added tokens as tokens
       _lowerCAmelCase						:       str  			=     self.get_tokenizers(fast=a__				,     do_lower_case=a__  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           _lowerCAmelCase						:       Dict  			=     ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.convert_tokens_to_string(a__  )

           self.assertIsInstance(a__				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           _lowerCAmelCase						:       Dict  			=     [
               """bos_token""",
               """eos_token""",
               """unk_token""",
               """sep_token""",
               """pad_token""",
               """cls_token""",
               """mask_token""",
           ]

           _lowerCAmelCase						:       Dict  			=     0
           _lowerCAmelCase						:       Dict  			=     tokenizer.convert_ids_to_tokens(
               a__				,     skip_special_tokens=a__  )

           for attr in attributes_list:
             setattr(a__				,     attr + """_id"""				,     a__  )
             self.assertEqual(getattr(a__				,     a__  )				,     a__  )
             self.assertEqual(getattr(a__				,     attr + """_id"""  )				,     a__  )

             setattr(a__				,     attr + """_id"""				,     a__  )
             self.assertEqual(getattr(a__				,     a__  )				,     a__  )
             self.assertEqual(getattr(a__				,     attr + """_id"""  )				,     a__  )

           setattr(a__				,     """additional_special_tokens_ids"""				,     []  )
           self.assertListEqual(getattr(a__				,     """additional_special_tokens"""  )				,     []  )
           self.assertListEqual(getattr(a__				,     """additional_special_tokens_ids"""  )				,     []  )

           setattr(a__				,     """additional_special_tokens_ids"""				,     [token_id_to_test_setters]  )
           self.assertListEqual(getattr(a__				,     """additional_special_tokens"""  )				,     [token_to_test_setters]  )
           self.assertListEqual(getattr(a__				,     """additional_special_tokens_ids"""  )				,     [token_id_to_test_setters]  )





"""simple docstring"""




from math import ceil





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     list(range(0			,_lowerCamelCase	)	)

  _lowerCAmelCase						:       Tuple  			=     [item for sublist in list(device_map.values()	) for item in sublist]

  # Duplicate check
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  for i in device_map_blocks:
    if device_map_blocks.count(_lowerCamelCase	) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_lowerCamelCase	)
    # Missing blocks
  _lowerCAmelCase						:       int  			=     [i for i in blocks if i not in device_map_blocks]
  _lowerCAmelCase						:       List[Any]  			=     [i for i in device_map_blocks if i not in blocks]

  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
        """ These attention blocks were specified more than once: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """There are attention blocks for this model that are not specified in the device_map. Add these attention """
        """blocks to a device on the device_map: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
        + str(_lowerCamelCase	)	)






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(range(_lowerCamelCase	)	)
  _lowerCAmelCase						:       Optional[Any]  			=     int(ceil(n_layers / len(_lowerCamelCase	)	)	)
  _lowerCAmelCase						:       Optional[int]  			=     [layers[i : i + n_blocks] for i in range(0			,_lowerCamelCase			,_lowerCamelCase	)]

  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)




