


'''simple docstring'''







import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				def get_masked_lm_array(UpperCamelCase__		):
								__magic_name__					:       str    							=							F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
								__magic_name__					:       Dict    							=							tf.train.load_variable(UpperCamelCase__  , UpperCamelCase__		)

								if "kernel" in name:
												__magic_name__					:       List[str]    							=							array.transpose()

								return torch.from_numpy(UpperCamelCase__		)

				def get_encoder_array(UpperCamelCase__		):
								__magic_name__					:       int    							=							F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
								__magic_name__					:       List[Any]    							=							tf.train.load_variable(UpperCamelCase__  , UpperCamelCase__		)

								if "kernel" in name:
												__magic_name__					:       int    							=							array.transpose()

								return torch.from_numpy(UpperCamelCase__		)

				def get_encoder_layer_array(UpperCamelCase__  , UpperCamelCase__		):
								__magic_name__					:       Optional[Any]    							=							F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
								__magic_name__					:       Optional[int]    							=							tf.train.load_variable(UpperCamelCase__  , UpperCamelCase__		)

								if "kernel" in name:
												__magic_name__					:       str    							=							array.transpose()

								return torch.from_numpy(UpperCamelCase__		)

				def get_encoder_attention_layer_array(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):
								__magic_name__					:       Optional[int]    							=							F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
								__magic_name__					:       Dict    							=							tf.train.load_variable(UpperCamelCase__  , UpperCamelCase__		)
								__magic_name__					:       Optional[int]    							=							array.reshape(UpperCamelCase__		)

								if "kernel" in name:
												__magic_name__					:       List[Any]    							=							array.transpose()

								return torch.from_numpy(UpperCamelCase__		)

				print(F"""Loading model based on config from {config_path}..."""		)
				__magic_name__					:       Optional[int]    							=							BertConfig.from_json_file(UpperCamelCase__		)
				__magic_name__					:       str    							=							BertForMaskedLM(UpperCamelCase__		)

				# Layers
				for layer_index in range(0  , config.num_hidden_layers		):
								__magic_name__					:       Tuple    							=							model.bert.encoder.layer[layer_index]

								# Self-attention
								__magic_name__					:       List[Any]    							=							layer.attention.self

								__magic_name__					:       Union[str, Any]    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_query_dense/kernel"  , self_attn.query.weight.data.shape		)
								__magic_name__					:       Dict    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_query_dense/bias"  , self_attn.query.bias.data.shape		)
								__magic_name__					:       int    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_key_dense/kernel"  , self_attn.key.weight.data.shape		)
								__magic_name__					:       Any    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_key_dense/bias"  , self_attn.key.bias.data.shape		)
								__magic_name__					:       Any    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_value_dense/kernel"  , self_attn.value.weight.data.shape		)
								__magic_name__					:       Any    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_value_dense/bias"  , self_attn.value.bias.data.shape		)

								# Self-attention Output
								__magic_name__					:       Optional[Any]    							=							layer.attention.output

								__magic_name__					:       Dict    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_output_dense/kernel"  , self_output.dense.weight.data.shape		)
								__magic_name__					:       Union[str, Any]    							=							get_encoder_attention_layer_array(
								    UpperCamelCase__  , "_output_dense/bias"  , self_output.dense.bias.data.shape		)

								__magic_name__					:       List[Any]    							=							get_encoder_layer_array(UpperCamelCase__  , "_attention_layer_norm/gamma"		)
								__magic_name__					:       Dict    							=							get_encoder_layer_array(UpperCamelCase__  , "_attention_layer_norm/beta"		)

								# Intermediate
								__magic_name__					:       Tuple    							=							layer.intermediate

								__magic_name__					:       Tuple    							=							get_encoder_layer_array(UpperCamelCase__  , "_intermediate_dense/kernel"		)
								__magic_name__					:       Dict    							=							get_encoder_layer_array(UpperCamelCase__  , "_intermediate_dense/bias"		)

								# Output
								__magic_name__					:       Tuple    							=							layer.output

								__magic_name__					:       Optional[Any]    							=							get_encoder_layer_array(UpperCamelCase__  , "_output_dense/kernel"		)
								__magic_name__					:       Dict    							=							get_encoder_layer_array(UpperCamelCase__  , "_output_dense/bias"		)

								__magic_name__					:       Optional[Any]    							=							get_encoder_layer_array(UpperCamelCase__  , "_output_layer_norm/gamma"		)
								__magic_name__					:       int    							=							get_encoder_layer_array(UpperCamelCase__  , "_output_layer_norm/beta"		)

				# Embeddings
				__magic_name__					:       List[Any]    							=							get_encoder_array("_position_embedding_layer/embeddings"		)
				__magic_name__					:       Tuple    							=							get_encoder_array("_type_embedding_layer/embeddings"		)
				__magic_name__					:       List[Any]    							=							get_encoder_array("_embedding_norm_layer/gamma"		)
				__magic_name__					:       Dict    							=							get_encoder_array("_embedding_norm_layer/beta"		)

				# LM Head
				__magic_name__					:       Dict    							=							model.cls.predictions.transform

				__magic_name__					:       Tuple    							=							get_masked_lm_array("dense/kernel"		)
				__magic_name__					:       Dict    							=							get_masked_lm_array("dense/bias"		)

				__magic_name__					:       Tuple    							=							get_masked_lm_array("layer_norm/gamma"		)
				__magic_name__					:       List[Any]    							=							get_masked_lm_array("layer_norm/beta"		)

				__magic_name__					:       Union[str, Any]    							=							get_masked_lm_array("embedding_table"		)

				# Pooling
				__magic_name__					:       int    							=							BertPooler(config=UpperCamelCase__		)
				__magic_name__					:       Tuple    							=							get_encoder_array("_pooler_layer/kernel"		)
				__magic_name__					:       int    							=							get_encoder_array("_pooler_layer/bias"		)

				# Export final model
				model.save_pretrained(UpperCamelCase__		)

				# Integration test - should load without any errors ;)
				__magic_name__					:       List[str]    							=							BertForMaskedLM.from_pretrained(UpperCamelCase__		)
				print(new_model.eval()		)

				print("Model conversion was done sucessfully!"		)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							: Tuple										=			argparse.ArgumentParser()
		parser.add_argument(
		    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
		)
		parser.add_argument(
		    "--bert_config_file",
		    type=str,
		    required=True,
		    help="The config json file corresponding to the BERT model. This specifies the model architecture.",
		)
		parser.add_argument(
		    "--pytorch_dump_path",
		    type=str,
		    required=True,
		    help="Path to the output PyTorch model.",
		)
		_SCREAMING_SNAKE_CASE							: Any										=			parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


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

class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    PerceiverTokenizer
   _lowercase     :       Dict  					=    False

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Any:
         """simple docstring"""





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

   @cached_property
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]			,						**lowerCamelCase__	:    str       ) ->			PerceiverTokenizer:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    List[str]=2_0			,						lowerCamelCase__	:    Union[str, Any]=5       ) ->			Tuple[str, list]:
         """simple docstring"""





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

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

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

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		'''Unicode €.'''
         A_							       =		tokenizer(lowerCamelCase__       )
         A_							       =		[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )

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

         A_							       =		tokenizer('''e è é ê ë'''       )
         A_							       =		[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )
         # decoding
         A_							       =		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]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         A_							       =		[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )

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

         self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

         self.assertEqual((2, 3_8)			,						batch.input_ids.shape       )
         self.assertEqual((2, 3_8)			,						batch.attention_mask.shape       )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         A_							       =		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	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         A_							       =		tokenizer(
             text_target=lowerCamelCase__			,						max_length=3_2			,						padding='''max_length'''			,						truncation=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertEqual(3_2			,						targets['''input_ids'''].shape[1]       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     self.assertNotEqual(tokenizer.model_max_length			,						4_2       )

        # Now let's start the test
         A_							       =		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
                     A_							       =		tempfile.mkdtemp()

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

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

                     shutil.rmtree(lowerCamelCase__       )

         A_							       =		self.get_tokenizers(model_max_length=4_2       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

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

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		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			,						4_2       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__			,						model_max_length=4_3       )
                     self.assertEqual(tokenizer.model_max_length			,						4_3       )

                     shutil.rmtree(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		[]
         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:
                           A_							       =		json.load(lowerCamelCase__       )

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

                     A_							       =		[F"<extra_id_{i}>" for i in range(1_2_5       )]

                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]
                     A_							       =		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
                     A_							       =		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
                     A_							       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,						lstrip=lowerCamelCase__       )]
                     A_							       =		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	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]       )			,						'''�'''       )

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Dict:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





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

                     self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )





