





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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    snake_case_				=			"""pt"""
elif is_tf_available():
    snake_case_				=			"""tf"""
else:
    snake_case_				=			"""jax"""

class 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    PerceiverTokenizer
      A_						:				Optional[Any]							=    False


      def    a (self       :      Any      ):





         """simple docstring"""



         super().setUp()
         __snake_case							       =   PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname      )


      @cached_property
      def    a (self       :      int      ):





         """simple docstring"""



         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''      )


      def    a (self       :      str				,			**a__       :      Optional[Any]      ):





         """simple docstring"""



         return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__lowercase      )


      def    a (self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      List[str]=False				,			a__       :      List[Any]=20				,			a__       :      Dict=5      ):





         """simple docstring"""



         __snake_case							       =   []
         for i in range(len(__lowercase      )      ):
            try:
               __snake_case							       =   tokenizer.decode([i]				,			clean_up_tokenization_spaces=__lowercase      )
            except UnicodeDecodeError:
               pass
            toks.append((i, tok)      )

         __snake_case							       =   list(filter(lambda a__      : re.match(R'''^[ a-zA-Z]+$'''				,			t[1]      )				,			__lowercase      )      )
         __snake_case							       =   list(filter(lambda a__      : [t[0]] == tokenizer.encode(t[1]				,			add_special_tokens=__lowercase      )				,			__lowercase      )      )
         if max_length is not None and len(__lowercase      ) > max_length:
            __snake_case							       =   toks[:max_length]
         if min_length is not None and len(__lowercase      ) < min_length and len(__lowercase      ) > 0:
            while len(__lowercase      ) < min_length:
               __snake_case							       =   toks + toks
        # toks_str = [t[1] for t in toks]
         __snake_case							       =   [t[0] for t in toks]

         # Ensure consistency
         __snake_case							       =   tokenizer.decode(__lowercase				,			clean_up_tokenization_spaces=__lowercase      )
         if " " not in output_txt and len(__lowercase      ) > 1:
            __snake_case							       =   (
                tokenizer.decode([toks_ids[0]]				,			clean_up_tokenization_spaces=__lowercase      )
                + ''' '''
                + tokenizer.decode(toks_ids[1:]				,			clean_up_tokenization_spaces=__lowercase      )
            )
         if with_prefix_space:
            __snake_case							       =   ''' ''' + output_txt
         __snake_case							       =   tokenizer.encode(__lowercase				,			add_special_tokens=__lowercase      )
         return output_txt, output_ids


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.perceiver_tokenizer
         __snake_case							       =   '''Unicode €.'''
         __snake_case							       =   tokenizer(__lowercase      )
         __snake_case							       =   [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
         self.assertEqual(encoded['''input_ids''']				,			__lowercase      )

         # decoding
         __snake_case							       =   tokenizer.decode(__lowercase      )
         self.assertEqual(__lowercase				,			'''[CLS]Unicode €.[SEP]'''      )

         __snake_case							       =   tokenizer('''e è é ê ë'''      )
         __snake_case							       =   [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
         self.assertEqual(encoded['''input_ids''']				,			__lowercase      )
         # decoding
         __snake_case							       =   tokenizer.decode(__lowercase      )
         self.assertEqual(__lowercase				,			'''[CLS]e è é ê ë[SEP]'''      )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''      )      )				,			'''[CLS]e è é ê ë[SEP]'''      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.perceiver_tokenizer
         __snake_case							       =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         __snake_case							       =   [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
         # fmt: on
         __snake_case							       =   tokenizer(__lowercase				,			padding=__lowercase				,			return_tensors=__lowercase      )
         self.assertIsInstance(__lowercase				,			__lowercase      )

         if FRAMEWORK != "jax":
            __snake_case							       =   list(batch.input_ids.numpy()[0]      )
         else:
            __snake_case							       =   list(batch.input_ids.tolist()[0]      )

         self.assertListEqual(__lowercase				,			__lowercase      )

         self.assertEqual((2, 38)				,			batch.input_ids.shape      )
         self.assertEqual((2, 38)				,			batch.attention_mask.shape      )


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.perceiver_tokenizer
         __snake_case							       =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         __snake_case							       =   tokenizer(__lowercase				,			padding=__lowercase				,			return_tensors=__lowercase      )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('''input_ids'''				,			__lowercase      )
         self.assertIn('''attention_mask'''				,			__lowercase      )
         self.assertNotIn('''decoder_input_ids'''				,			__lowercase      )
         self.assertNotIn('''decoder_attention_mask'''				,			__lowercase      )


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.perceiver_tokenizer
         __snake_case							       =   [
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         __snake_case							       =   tokenizer(
             text_target=__lowercase				,			max_length=32				,			padding='''max_length'''				,			truncation=__lowercase				,			return_tensors=__lowercase      )
         self.assertEqual(32				,			targets['''input_ids'''].shape[1]      )


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   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
         __snake_case							       =   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
               __snake_case							       =   tempfile.mkdtemp()

               __snake_case							       =   ''' He is very happy, UNwant\u00E9d,running'''
               __snake_case							       =   tokenizer.encode(__lowercase				,			add_special_tokens=__lowercase      )
               tokenizer.save_pretrained(__lowercase      )

               __snake_case							       =   tokenizer.__class__.from_pretrained(__lowercase      )
               __snake_case							       =   after_tokenizer.encode(__lowercase				,			add_special_tokens=__lowercase      )
               self.assertListEqual(__lowercase				,			__lowercase      )

               shutil.rmtree(__lowercase      )

         __snake_case							       =   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
               __snake_case							       =   tempfile.mkdtemp()

               __snake_case							       =   ''' He is very happy, UNwant\u00E9d,running'''
               tokenizer.add_tokens(['''bim''', '''bambam''']      )
               __snake_case							       =   tokenizer.additional_special_tokens
               additional_special_tokens.append('''new_additional_special_token'''      )
               tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}      )
               __snake_case							       =   tokenizer.encode(__lowercase				,			add_special_tokens=__lowercase      )
               tokenizer.save_pretrained(__lowercase      )

               __snake_case							       =   tokenizer.__class__.from_pretrained(__lowercase      )
               __snake_case							       =   after_tokenizer.encode(__lowercase				,			add_special_tokens=__lowercase      )
               self.assertListEqual(__lowercase				,			__lowercase      )
               self.assertIn('''new_additional_special_token'''				,			after_tokenizer.additional_special_tokens      )
               self.assertEqual(after_tokenizer.model_max_length				,			42      )

               __snake_case							       =   tokenizer.__class__.from_pretrained(__lowercase				,			model_max_length=43      )
               self.assertEqual(tokenizer.model_max_length				,			43      )

               shutil.rmtree(__lowercase      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   []
         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(__lowercase      )

               with open(os.path.join(__lowercase				,			'''special_tokens_map.json'''      )				,			encoding='''utf-8'''      ) as json_file:
                  __snake_case							       =   json.load(__lowercase      )

               with open(os.path.join(__lowercase				,			'''tokenizer_config.json'''      )				,			encoding='''utf-8'''      ) as json_file:
                  __snake_case							       =   json.load(__lowercase      )

               __snake_case							       =   [f"""<extra_id_{i}>""" for i in range(125      )]

               __snake_case							       =   added_tokens_extra_ids + [
                   '''an_additional_special_token'''
               ]
               __snake_case							       =   added_tokens_extra_ids + [
                   '''an_additional_special_token'''
               ]

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

               # 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
               __snake_case							       =   tokenizer_class.from_pretrained(
                   __lowercase				,			)
               self.assertIn(
                   '''an_additional_special_token'''				,			tokenizer_without_change_in_init.additional_special_tokens      )
               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
               __snake_case							       =   added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''				,			lstrip=__lowercase      )]
               __snake_case							       =   tokenizer_class.from_pretrained(
                   __lowercase				,			additional_special_tokens=__lowercase				,			)

               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       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([178]      )				,			'''�'''      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         pass


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      int      ):





         """simple docstring"""



         pass


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.get_tokenizers(fast=__lowercase				,			do_lower_case=__lowercase      )
         for tokenizer in tokenizers:
            with self.subTest(f"""{tokenizer.__class__.__name__}"""      ):
               __snake_case							       =   ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
               __snake_case							       =   tokenizer.convert_tokens_to_string(__lowercase      )

               self.assertIsInstance(__lowercase				,			__lowercase      )





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCAmelCase	:     Optional[int]   ="""src/transformers"""
_UpperCAmelCase	:     str   ="""docs/source/en"""
_UpperCAmelCase	:     Optional[int]   ="""."""



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       List[str]:
				with open(lowerCAmelCase_       ,      '''r'''       ,      encoding='''utf-8'''       ,      newline='''\n'''       ) as f:
								lowerCAmelCase_       :					int			 = f.readlines()
				# Find the start prompt.
				lowerCAmelCase_       :					List[Any]			 = 0
				while not lines[start_index].startswith(lowerCAmelCase_       ):
								start_index += 1
				start_index += 1

				lowerCAmelCase_       :					List[str]			 = start_index
				while not lines[end_index].startswith(lowerCAmelCase_       ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]       ) <= 1:
								start_index += 1
				while len(lines[end_index]       ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase	:     Optional[Any]   ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase	:     Optional[int]   =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase	:     Dict   =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase	:     Optional[Any]   =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase	:     Optional[int]   =direct_transformers_import(TRANSFORMERS_PATH)



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					str			 = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,      lowerCAmelCase_       )
				return [m.group(0       ) for m in matches]



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Tuple			 = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_       )
				lowerCAmelCase_       :					int			 = (width - text_length) // 2
				lowerCAmelCase_       :					Union[str, Any]			 = width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def 	lowerCAmelCase       (						)->       str:
				lowerCAmelCase_       :					Any			 = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_       :					Dict			 = {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase_       :					List[Any]			 = {name: config.replace('''Config'''       ,      ''''''       ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase_       :					Tuple			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[Any]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[int]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(lowerCAmelCase_       ):
								lowerCAmelCase_       :					Optional[int]			 = None
								if attr_name.endswith('''Tokenizer'''       ):
												lowerCAmelCase_       :					Union[str, Any]			 = slow_tokenizers
												lowerCAmelCase_       :					List[str]			 = attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''       ):
												lowerCAmelCase_       :					int			 = fast_tokenizers
												lowerCAmelCase_       :					Union[str, Any]			 = attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Tuple			 = tf_models
												lowerCAmelCase_       :					str			 = _re_tf_models.match(lowerCAmelCase_       ).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Tuple			 = flax_models
												lowerCAmelCase_       :					Union[str, Any]			 = _re_flax_models.match(lowerCAmelCase_       ).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Any			 = pt_models
												lowerCAmelCase_       :					List[Any]			 = _re_pt_models.match(lowerCAmelCase_       ).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase_       ) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase_       :					Union[str, Any]			 = True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_       :					Any			 = ''''''.join(camel_case_split(lowerCAmelCase_       )[:-1]       )

    # Let's build that table!
				lowerCAmelCase_       :					int			 = list(model_name_to_config.keys()       )
				model_names.sort(key=str.lower       )
				lowerCAmelCase_       :					Tuple			 = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase_       :					Union[str, Any]			 = [len(lowerCAmelCase_       ) + 2 for c in columns]
				lowerCAmelCase_       :					Optional[Any]			 = max([len(lowerCAmelCase_       ) for name in model_names]       ) + 2

				# Build the table per se
				lowerCAmelCase_       :					Dict			 = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_       ,      lowerCAmelCase_       ) for c, w in zip(lowerCAmelCase_       ,      lowerCAmelCase_       )]       ) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]       ) + "|\n"

				lowerCAmelCase_       :					List[str]			 = {True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_       :					List[Any]			 = model_name_to_prefix[name]
								lowerCAmelCase_       :					Union[str, Any]			 = [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase_       ,      lowerCAmelCase_       ) for l, w in zip(lowerCAmelCase_       ,      lowerCAmelCase_       )]       ) + "|\n"
				return table



def 	lowerCAmelCase       (						lowerCAmelCase_=False       )->       Tuple:
				lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_       :					str			 = _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_       ,      '''index.md'''       )       ,      start_prompt='''<!--This table is updated automatically from the auto modules'''       ,      end_prompt='''<!-- End table-->'''       ,      )
				lowerCAmelCase_       :					Tuple			 = get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_       ,      '''index.md'''       )       ,      '''w'''       ,      encoding='''utf-8'''       ,      newline='''\n'''       ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
								else:
												raise ValueError(
												    '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''       )


if __name__ == "__main__":
						_UpperCAmelCase	:     List[Any]   =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase	:     Tuple   =parser.parse_args()

						check_model_table(args.fix_and_overwrite)