




"""simple docstring"""


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():
  _A													=      """pt"""
elif is_tf_available():
  _A													=      """tf"""
else:
  _A													=      """jax"""


class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Tuple     				=     PerceiverTokenizer
   _lowerCamelCase    :Tuple     				=     False






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Union[str, Any]:

         """simple docstring"""
         super().setUp()
         lowerCAmelCase__      :			List[str]   =	PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname   )






   @cached_property
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[Any]:

         """simple docstring"""
         return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     **UpperCamelCase       :		List[str]   )			->						PerceiverTokenizer:

         """simple docstring"""
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		str=False   ,     UpperCamelCase       :		str=20   ,     UpperCamelCase       :		int=5   )			->						Tuple[str, list]:

         """simple docstring"""
         # XXX The default common tokenizer tests assume that every ID is decodable on its own.
         # This assumption is invalid for Perceiver 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__      :			Any   =	[]
         for i in range(len(UpperCamelCase   )   ):
               try:
                     lowerCAmelCase__      :			List[str]   =	tokenizer.decode([i]   ,     clean_up_tokenization_spaces=UpperCamelCase   )
               except UnicodeDecodeError:
                     pass
               toks.append((i, tok)   )

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

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






   def 	_lowerCAmelCase   (		self       :		str   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.perceiver_tokenizer
         lowerCAmelCase__      :			int   =	"""Unicode €."""
         lowerCAmelCase__      :			Union[str, Any]   =	tokenizer(UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
         self.assertEqual(encoded["""input_ids"""]   ,     UpperCamelCase   )

         # decoding
         lowerCAmelCase__      :			int   =	tokenizer.decode(UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     """[CLS]Unicode €.[SEP]"""   )

         lowerCAmelCase__      :			Union[str, Any]   =	tokenizer("""e è é ê ë"""   )
         lowerCAmelCase__      :			Union[str, Any]   =	[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
         self.assertEqual(encoded["""input_ids"""]   ,     UpperCamelCase   )
         # decoding
         lowerCAmelCase__      :			List[str]   =	tokenizer.decode(UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     """[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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.perceiver_tokenizer
         lowerCAmelCase__      :			List[str]   =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         # fmt: off
         lowerCAmelCase__      :			Optional[int]   =	[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
         # fmt: on
         lowerCAmelCase__      :			Dict   =	tokenizer(UpperCamelCase   ,     padding=UpperCamelCase   ,     return_tensors=UpperCamelCase   )
         self.assertIsInstance(UpperCamelCase   ,     UpperCamelCase   )

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

         self.assertListEqual(UpperCamelCase   ,     UpperCamelCase   )

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






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.perceiver_tokenizer
         lowerCAmelCase__      :			Dict   =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         lowerCAmelCase__      :			Optional[int]   =	tokenizer(UpperCamelCase   ,     padding=UpperCamelCase   ,     return_tensors=UpperCamelCase   )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn("""input_ids"""   ,     UpperCamelCase   )
         self.assertIn("""attention_mask"""   ,     UpperCamelCase   )
         self.assertNotIn("""decoder_input_ids"""   ,     UpperCamelCase   )
         self.assertNotIn("""decoder_attention_mask"""   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.perceiver_tokenizer
         lowerCAmelCase__      :			Union[str, Any]   =	[
             """Summary of the text.""",
             """Another summary.""",
         ]
         lowerCAmelCase__      :			Optional[int]   =	tokenizer(
             text_target=UpperCamelCase   ,     max_length=32   ,     padding="""max_length"""   ,     truncation=UpperCamelCase   ,     return_tensors=UpperCamelCase   )
         self.assertEqual(32   ,     targets["""input_ids"""].shape[1]   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

         """simple docstring"""
         # safety check on max_len default value so we are sure the test works
         lowerCAmelCase__      :			str   =	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__      :			List[str]   =	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__      :			List[Any]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Optional[Any]   =	""" He is very happy, UNwant\u00E9d,running"""
                     lowerCAmelCase__      :			int   =	tokenizer.encode(UpperCamelCase   ,     add_special_tokens=UpperCamelCase   )
                     tokenizer.save_pretrained(UpperCamelCase   )

                     lowerCAmelCase__      :			List[Any]   =	tokenizer.__class__.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Tuple   =	after_tokenizer.encode(UpperCamelCase   ,     add_special_tokens=UpperCamelCase   )
                     self.assertListEqual(UpperCamelCase   ,     UpperCamelCase   )

                     shutil.rmtree(UpperCamelCase   )

         lowerCAmelCase__      :			Optional[Any]   =	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__      :			Optional[int]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Optional[int]   =	""" He is very happy, UNwant\u00E9d,running"""
                     tokenizer.add_tokens(["""bim""", """bambam"""]   )
                     lowerCAmelCase__      :			int   =	tokenizer.additional_special_tokens
                     additional_special_tokens.append("""new_additional_special_token"""   )
                     tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}   )
                     lowerCAmelCase__      :			Optional[int]   =	tokenizer.encode(UpperCamelCase   ,     add_special_tokens=UpperCamelCase   )
                     tokenizer.save_pretrained(UpperCamelCase   )

                     lowerCAmelCase__      :			int   =	tokenizer.__class__.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Dict   =	after_tokenizer.encode(UpperCamelCase   ,     add_special_tokens=UpperCamelCase   )
                     self.assertListEqual(UpperCamelCase   ,     UpperCamelCase   )
                     self.assertIn("""new_additional_special_token"""   ,     after_tokenizer.additional_special_tokens   )
                     self.assertEqual(after_tokenizer.model_max_length   ,     42   )

                     lowerCAmelCase__      :			List[str]   =	tokenizer.__class__.from_pretrained(UpperCamelCase   ,     model_max_length=43   )
                     self.assertEqual(tokenizer.model_max_length   ,     43   )

                     shutil.rmtree(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	[]
         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(UpperCamelCase   )

                     with open(os.path.join(UpperCamelCase   ,     """special_tokens_map.json"""   )   ,     encoding="""utf-8"""   ) as json_file:
                           lowerCAmelCase__      :			Dict   =	json.load(UpperCamelCase   )

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

                     lowerCAmelCase__      :			Dict   =	[f"""<extra_id_{i}>""" for i in range(1_25   )]

                     lowerCAmelCase__      :			Tuple   =	added_tokens_extra_ids + [
                         """an_additional_special_token"""
                     ]
                     lowerCAmelCase__      :			Optional[int]   =	added_tokens_extra_ids + [
                         """an_additional_special_token"""
                     ]

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

                     # 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__      :			Union[str, Any]   =	tokenizer_class.from_pretrained(
                         UpperCamelCase   ,     )
                     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
                     lowerCAmelCase__      :			Optional[int]   =	added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""   ,     lstrip=UpperCamelCase   )]
                     lowerCAmelCase__      :			Tuple   =	tokenizer_class.from_pretrained(
                         UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     )

                     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 	_lowerCAmelCase   (		self       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_78]   )   ,     """�"""   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Any   )			->						int:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		int   )			->						Union[str, Any]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
         # strings and special added tokens as tokens
         lowerCAmelCase__      :			Optional[Any]   =	self.get_tokenizers(fast=UpperCamelCase   ,     do_lower_case=UpperCamelCase   )
         for tokenizer in tokenizers:
               with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
                     lowerCAmelCase__      :			Tuple   =	["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                     lowerCAmelCase__      :			Any   =	tokenizer.convert_tokens_to_string(UpperCamelCase   )

                     self.assertIsInstance(UpperCamelCase   ,     UpperCamelCase   )











"""simple docstring"""


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
      model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





