




"""simple docstring"""





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  __lowercase    (     snake_case_  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =PreTrainedTokenizerFast
				_lowerCAmelCase		     =False
				_lowerCAmelCase		     =True
				_lowerCAmelCase		     ="tokenizer_file"

				def  _lowerCamelCase   (				self				:       Any ):
										snake_case__    :   int							     =   False  # because we don't have pretrained_vocab_files_map
										super().setUp()
										snake_case__    :   Optional[int]							     =   True

										snake_case__    :   Optional[Any]							     =   ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
										snake_case__    :   str							     =   '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

										# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
										snake_case__    :   Optional[Any]							     =   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

										snake_case__    :   Union[str, Any]							     =   PreTrainedTokenizerFast.from_pretrained(model_paths[0] )
										tokenizer.save_pretrained(self.tmpdirname )

				def  _lowerCamelCase   (				self				:       List[Any] ):
										# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
										# model
										pass

				def  _lowerCamelCase   (				self				:       Optional[int] ):
										# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
										# model
										pass

				def  _lowerCamelCase   (				self				:       Optional[int] ):
										# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
										# model
										pass

				def  _lowerCamelCase   (				self				:       Tuple ):
										# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
										pass

				def  _lowerCamelCase   (				self				:       List[str] ):
										snake_case__    :   Any							     =   self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																						try:
																												snake_case__    :   Optional[int]							     =   tempfile.mkdtemp()
																												snake_case__    :   Union[str, Any]							     =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )

																												tokenizer.save_pretrained(self.tmpdirname )
																												super().test_training_new_tokenizer()
																						finally:
																												# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																												# is restored
																												shutil.rmtree(self.tmpdirname )
																												snake_case__    :   List[str]							     =   tmpdirname_orig

				def  _lowerCamelCase   (				self				:       List[Any] ):
										snake_case__    :   List[Any]							     =   self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
																						try:
																												snake_case__    :   Optional[Any]							     =   tempfile.mkdtemp()
																												snake_case__    :   List[Any]							     =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )

																												tokenizer.save_pretrained(self.tmpdirname )
																												super().test_training_new_tokenizer_with_special_tokens_change()
																						finally:
																												# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																												# is restored
																												shutil.rmtree(self.tmpdirname )
																												snake_case__    :   List[Any]							     =   tmpdirname_orig





				def  _lowerCamelCase   (				self				:       List[str] ):
										snake_case__    :   List[Any]							     =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name )

										snake_case__    :   Dict							     =   ('''a''' for _ in range(1_0_0_0 ))
										snake_case__    :   Union[str, Any]							     =   tokenizer.train_new_from_iterator(text_iterator=SCREAMING_SNAKE_CASE_ ,length=1_0_0_0 ,vocab_size=5_0 )

										snake_case__    :   List[Any]							     =   new_tokenizer.encode('''a🤗''' )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5] )







@require_tokenizers
class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       Dict ):
										snake_case__    :   Optional[Any]							     =   AutoTokenizer.from_pretrained('''bert-base-cased''' )
										snake_case__    :   Tuple							     =   json.loads(tokenizer._tokenizer.to_str() )
										snake_case__    :   List[str]							     =   len(SCREAMING_SNAKE_CASE_ )

										with tempfile.TemporaryDirectory() as tmp_dir:
																# Hack to save this in the tokenizer_config.json
																snake_case__    :   int							     =   ['''tokenizer.4.0.0.json''']
																tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
																json.dump(SCREAMING_SNAKE_CASE_ ,open(os.path.join(SCREAMING_SNAKE_CASE_ ,'''tokenizer.4.0.0.json''' ) ,'''w''' ) )

																# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
																snake_case__    :   List[str]							     =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
																self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(SCREAMING_SNAKE_CASE_ ) + 1 )
																snake_case__    :   Union[str, Any]							     =   json.loads(new_tokenizer._tokenizer.to_str() )
																self.assertIn('''huggingface''' ,json_tokenizer['''model''']['''vocab'''] )

																# Will need to be adjusted if we reach v42 and this test is still here.
																# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
																shutil.move(os.path.join(SCREAMING_SNAKE_CASE_ ,'''tokenizer.4.0.0.json''' ) ,os.path.join(SCREAMING_SNAKE_CASE_ ,'''tokenizer.42.0.0.json''' ) )
																snake_case__    :   str							     =   ['''tokenizer.42.0.0.json''']
																tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
																self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,len(SCREAMING_SNAKE_CASE_ ) )
																snake_case__    :   Union[str, Any]							     =   json.loads(new_tokenizer._tokenizer.to_str() )
																self.assertNotIn('''huggingface''' ,json_tokenizer['''model''']['''vocab'''] )





				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
										snake_case__    :   str							     =   '''hf-internal-testing/test-two-tokenizers'''

										# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
										snake_case__    :   List[str]							     =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
										self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,2_8_9_9_7 )
										snake_case__    :   Tuple							     =   json.loads(tokenizer._tokenizer.to_str() )
										self.assertIn('''huggingface''' ,json_tokenizer['''model''']['''vocab'''] )

										# Testing an older version by monkey-patching the version in the module it's used.
										import transformers as old_transformers

										snake_case__    :   List[Any]							     =   '''3.0.0'''
										snake_case__    :   Any							     =   old_transformers.models.auto.AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
										self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,2_8_9_9_6 )
										snake_case__    :   Dict							     =   json.loads(old_tokenizer._tokenizer.to_str() )
										self.assertNotIn('''huggingface''' ,json_tokenizer['''model''']['''vocab'''] )







@require_tokenizers
class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       List[str] ):
										# See https://github.com/huggingface/transformers/pull/12550
										# and https://github.com/huggingface/tokenizers/issues/537
										snake_case__    :   int							     =   PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel''' )
										snake_case__    :   Tuple							     =   '''The Matrix is a 1999 science fiction action film.'''

										with concurrent.futures.ThreadPoolExecutor() as executor:
																snake_case__    :   List[Any]							     =   [executor.submit(self.fetch ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for i in range(1_0 )]
																snake_case__    :   List[str]							     =   [future.result() for future in futures]
																self.assertEqual(SCREAMING_SNAKE_CASE_ ,[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0 )] )





				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										return tokenizer.encode(SCREAMING_SNAKE_CASE_ ,truncation='''longest_first''' ,padding='''longest''' )





"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						if "model" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''model.''' ,     ''''''						)
						if "norm1" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''norm1''' ,     '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''norm2''' ,     '''output.LayerNorm'''						)
						if "norm" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''norm''' ,     '''LayerNorm'''						)
						if "transformer" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												snake_case__    :   Optional[Any]							     =   orig_key.replace(f'''transformer_{layer_num}''' ,     f'''encoder.layer.{layer_num}'''						)
						if "mha.attn" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''mha.attn''' ,     '''attention.self'''						)
						if "mha" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''mha''' ,     '''attention'''						)
						if "W_q" in orig_key:
												snake_case__    :   Optional[int]							     =   orig_key.replace('''W_q''' ,     '''self.query'''						)
						if "W_k" in orig_key:
												snake_case__    :   Tuple							     =   orig_key.replace('''W_k''' ,     '''self.key'''						)
						if "W_v" in orig_key:
												snake_case__    :   Dict							     =   orig_key.replace('''W_v''' ,     '''self.value'''						)
						if "ff.0" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''ff.0''' ,     '''intermediate.dense'''						)
						if "ff.2" in orig_key:
												snake_case__    :   int							     =   orig_key.replace('''ff.2''' ,     '''output.dense'''						)
						if "ff" in orig_key:
												snake_case__    :   Union[str, Any]							     =   orig_key.replace('''ff''' ,     '''output.dense'''						)
						if "mlm_class" in orig_key:
												snake_case__    :   str							     =   orig_key.replace('''mlm.mlm_class''' ,     '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												snake_case__    :   List[Any]							     =   orig_key.replace('''mlm''' ,     '''cls.predictions.transform'''						)
						if "backbone.backbone.encoders" in orig_key:
												snake_case__    :   Optional[Any]							     =   orig_key.replace('''backbone.backbone.encoders''' ,     '''encoder.layer'''						)
						if "cls" not in orig_key:
												snake_case__    :   Union[str, Any]							     =   '''mra.''' + orig_key

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

						snake_case__    :   Union[str, Any]							     =   orig_state_dict['''cls.predictions.decoder.bias''']
						snake_case__    :   Any							     =   torch.arange(lowerCamelCase						).expand((1, -1)						) + 2

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   convert_checkpoint_helper(config.max_position_embeddings ,     lowerCamelCase						)

						print(model.load_state_dict(lowerCamelCase						)						)
						model.eval()
						model.save_pretrained(lowerCamelCase						)

						print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)


if __name__ == "__main__":
							_lowercase		 =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for Mra model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							_lowercase		 =      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


