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():
      lowercase_  : Tuple								=					'pt'
elif is_tf_available():
      lowercase_  : int								=					'tf'
else:
      lowercase_  : Optional[int]								=					'jax'






class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   PerceiverTokenizer
    __a						=   False
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     super().setUp()
     SCREAMING_SNAKE_CASE__: str=   PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname )
    @cached_property
    def     UpperCamelCase_					(	self )							->       int:
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase=False			,			lowerCAmelCase=20			,			lowerCAmelCase=5 )							->       Tuple[str, list]:
     # 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.

     SCREAMING_SNAKE_CASE__: Dict=   []
     for i in range(len(lowerCAmelCase ) ):
      try:
       SCREAMING_SNAKE_CASE__: str=   tokenizer.decode([i]			,			clean_up_tokenization_spaces=lowerCAmelCase )
      except UnicodeDecodeError:
       pass
      toks.append((i, tok) )

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

     # Ensure consistency
     SCREAMING_SNAKE_CASE__: List[Any]=   tokenizer.decode(lowerCAmelCase			,			clean_up_tokenization_spaces=lowerCAmelCase )
     if " " not in output_txt and len(lowerCAmelCase ) > 1:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   (
          tokenizer.decode([toks_ids[0]]			,			clean_up_tokenization_spaces=lowerCAmelCase )
          + ''' '''
          + tokenizer.decode(toks_ids[1:]			,			clean_up_tokenization_spaces=lowerCAmelCase )
      )
     if with_prefix_space:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   ''' ''' + output_txt
     SCREAMING_SNAKE_CASE__: str=   tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
     return output_txt, output_ids
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: int=   '''Unicode €.'''
     SCREAMING_SNAKE_CASE__: Dict=   tokenizer(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: int=   [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids''']			,			lowerCAmelCase )

     # decoding
     SCREAMING_SNAKE_CASE__: int=   tokenizer.decode(lowerCAmelCase )
     self.assertEqual(lowerCAmelCase			,			'''[CLS]Unicode €.[SEP]''' )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   tokenizer('''e è é ê ë''' )
     SCREAMING_SNAKE_CASE__: str=   [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids''']			,			lowerCAmelCase )
     # decoding
     SCREAMING_SNAKE_CASE__: str=   tokenizer.decode(lowerCAmelCase )
     self.assertEqual(lowerCAmelCase			,			'''[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     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: Optional[int]=   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     SCREAMING_SNAKE_CASE__: str=   [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
     SCREAMING_SNAKE_CASE__: Any=   tokenizer(lowerCAmelCase			,			padding=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     self.assertIsInstance(lowerCAmelCase			,			lowerCAmelCase )

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

     self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )

     self.assertEqual((2, 38)			,			batch.input_ids.shape )
     self.assertEqual((2, 38)			,			batch.attention_mask.shape )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: str=   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     SCREAMING_SNAKE_CASE__: Any=   tokenizer(lowerCAmelCase			,			padding=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('''input_ids'''			,			lowerCAmelCase )
     self.assertIn('''attention_mask'''			,			lowerCAmelCase )
     self.assertNotIn('''decoder_input_ids'''			,			lowerCAmelCase )
     self.assertNotIn('''decoder_attention_mask'''			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Any=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: Dict=   [
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     SCREAMING_SNAKE_CASE__: List[str]=   tokenizer(
         text_target=lowerCAmelCase			,			max_length=32			,			padding='''max_length'''			,			truncation=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     self.assertEqual(32			,			targets['''input_ids'''].shape[1] )
    def     UpperCamelCase_					(	self )							->       Tuple:
     # safety check on max_len default value so we are sure the test works
     SCREAMING_SNAKE_CASE__: Tuple=   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
     SCREAMING_SNAKE_CASE__: Any=   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
       SCREAMING_SNAKE_CASE__: Dict=   tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE__: int=   ''' He is very happy, UNwant\u00E9d,running'''
       SCREAMING_SNAKE_CASE__: str=   tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       tokenizer.save_pretrained(lowerCAmelCase )

       SCREAMING_SNAKE_CASE__: Union[str, Any]=   tokenizer.__class__.from_pretrained(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: Any=   after_tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )

       shutil.rmtree(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Optional[int]=   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
       SCREAMING_SNAKE_CASE__: List[str]=   tempfile.mkdtemp()

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

       SCREAMING_SNAKE_CASE__: Optional[Any]=   tokenizer.__class__.from_pretrained(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: Optional[int]=   after_tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )
       self.assertIn('''new_additional_special_token'''			,			after_tokenizer.additional_special_tokens )
       self.assertEqual(after_tokenizer.model_max_length			,			42 )

       SCREAMING_SNAKE_CASE__: Dict=   tokenizer.__class__.from_pretrained(lowerCAmelCase			,			model_max_length=43 )
       self.assertEqual(tokenizer.model_max_length			,			43 )

       shutil.rmtree(lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   []
     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(lowerCAmelCase )

       with open(os.path.join(lowerCAmelCase			,			'''special_tokens_map.json''' )			,			encoding='''utf-8''' ) as json_file:
        SCREAMING_SNAKE_CASE__: Optional[int]=   json.load(lowerCAmelCase )

       with open(os.path.join(lowerCAmelCase			,			'''tokenizer_config.json''' )			,			encoding='''utf-8''' ) as json_file:
        SCREAMING_SNAKE_CASE__: List[Any]=   json.load(lowerCAmelCase )

       SCREAMING_SNAKE_CASE__: str=   [f'<extra_id_{i}>' for i in range(125 )]

       SCREAMING_SNAKE_CASE__: Dict=   added_tokens_extra_ids + [
           '''an_additional_special_token'''
       ]
       SCREAMING_SNAKE_CASE__: List[str]=   added_tokens_extra_ids + [
           '''an_additional_special_token'''
       ]

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

       # 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
       SCREAMING_SNAKE_CASE__: Optional[Any]=   tokenizer_class.from_pretrained(
           lowerCAmelCase			,			)
       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
       SCREAMING_SNAKE_CASE__: Optional[int]=   added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,			lstrip=lowerCAmelCase )]
       SCREAMING_SNAKE_CASE__: int=   tokenizer_class.from_pretrained(
           lowerCAmelCase			,			additional_special_tokens=lowerCAmelCase			,			)

       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     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178] )			,			'''�''' )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     pass
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     pass
    def     UpperCamelCase_					(	self )							->       Any:
     pass
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass



    def     UpperCamelCase_					(	self )							->       str:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     SCREAMING_SNAKE_CASE__: List[str]=   self.get_tokenizers(fast=lowerCAmelCase			,			do_lower_case=lowerCAmelCase )
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}' ):
       SCREAMING_SNAKE_CASE__: List[Any]=   ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
       SCREAMING_SNAKE_CASE__: Dict=   tokenizer.convert_tokens_to_string(lowerCAmelCase )

       self.assertIsInstance(lowerCAmelCase			,			lowerCAmelCase )



"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






