
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     __A(				a						,     unittest.TestCase    ):
				snake_case_		     =					PreTrainedTokenizerFast
				snake_case_		     =					False
				snake_case_		     =					True
				snake_case_		     =					'''tokenizer_file'''




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					False  # because we don't have pretrained_vocab_files_map
								super().setUp()
								__a    							=					True

								__a    							=					['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
								__a    							=					'''SaulLu/dummy-tokenizer-bytelevel-bpe'''

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

								__a    							=					PreTrainedTokenizerFast.from_pretrained(model_paths[0]       )
								tokenizer.save_pretrained(self.tmpdirname       )




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


								'''simple docstring'''




								pass




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								pass




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								pass




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								__a    							=					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:
																				__a    							=					tempfile.mkdtemp()
																				__a    							=					self.rust_tokenizer_class.from_pretrained(_snake_case	,  **_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       )
																				__a    							=					tmpdirname_orig




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					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:
																				__a    							=					tempfile.mkdtemp()
																				__a    							=					self.rust_tokenizer_class.from_pretrained(_snake_case	,  **_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       )
																				__a    							=					tmpdirname_orig




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


								'''simple docstring'''




								__a    							=					self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name       )

								__a    							=					('''a''' for _ in range(1_000       ))
								__a    							=					tokenizer.train_new_from_iterator(text_iterator=_snake_case	,  length=1_000	,  vocab_size=50       )

								__a    							=					new_tokenizer.encode('''a🤗'''       )
								self.assertEqual(_snake_case	,  [64, 172, 253, 97, 245]       )







@require_tokenizers
class     __A(				unittest.TestCase    ):




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


								'''simple docstring'''




								__a    							=					AutoTokenizer.from_pretrained('''bert-base-cased'''       )
								__a    							=					json.loads(tokenizer._tokenizer.to_str()       )
								__a    							=					len(_snake_case       )

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

												# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
												__a    							=					AutoTokenizer.from_pretrained(_snake_case       )
												self.assertEqual(len(_snake_case       )	,  len(_snake_case       ) + 1       )
												__a    							=					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(_snake_case	,  '''tokenizer.4.0.0.json'''       )	,  os.path.join(_snake_case	,  '''tokenizer.42.0.0.json'''       )       )
												__a    							=					['''tokenizer.42.0.0.json''']
												tokenizer.save_pretrained(_snake_case       )
												__a    							=					AutoTokenizer.from_pretrained(_snake_case       )
												self.assertEqual(len(_snake_case       )	,  len(_snake_case       )       )
												__a    							=					json.loads(new_tokenizer._tokenizer.to_str()       )
												self.assertNotIn('''huggingface'''	,  json_tokenizer['''model''']['''vocab''']       )




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


								'''simple docstring'''




								__a    							=					'''hf-internal-testing/test-two-tokenizers'''

								# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
								__a    							=					AutoTokenizer.from_pretrained(_snake_case       )
								self.assertEqual(len(_snake_case       )	,  28_997       )
								__a    							=					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

								__a    							=					'''3.0.0'''
								__a    							=					old_transformers.models.auto.AutoTokenizer.from_pretrained(_snake_case       )
								self.assertEqual(len(_snake_case       )	,  28_996       )
								__a    							=					json.loads(old_tokenizer._tokenizer.to_str()       )
								self.assertNotIn('''huggingface'''	,  json_tokenizer['''model''']['''vocab''']       )







@require_tokenizers
class     __A(				unittest.TestCase    ):




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


								'''simple docstring'''




								__a    							=					PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''       )
								__a    							=					'''The Matrix is a 1999 science fiction action film.'''

								with concurrent.futures.ThreadPoolExecutor() as executor:
												__a    							=					[executor.submit(self.fetch	,  _snake_case	,  _snake_case       ) for i in range(10       )]
												__a    							=					[future.result() for future in futures]
												self.assertEqual(_snake_case	,  [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10       )]       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   List[Any]:


								'''simple docstring'''




								return tokenizer.encode(_snake_case	,  truncation='''longest_first'''	,  padding='''longest'''       )

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					__lowerCAmelCase    (    a__						) ->						Any:
				if "model" in orig_key:
								__a    							=					orig_key.replace('''model.'''		,					''''''						)
				if "norm1" in orig_key:
								__a    							=					orig_key.replace('''norm1'''		,					'''attention.output.LayerNorm'''						)
				if "norm2" in orig_key:
								__a    							=					orig_key.replace('''norm2'''		,					'''output.LayerNorm'''						)
				if "norm" in orig_key:
								__a    							=					orig_key.replace('''norm'''		,					'''LayerNorm'''						)
				if "transformer" in orig_key:
								__a    							=					orig_key.split('''.'''						)[0].split('''_'''						)[-1]
								__a    							=					orig_key.replace(F"""transformer_{layer_num}"""		,					F"""encoder.layer.{layer_num}"""						)
				if "mha.attn" in orig_key:
								__a    							=					orig_key.replace('''mha.attn'''		,					'''attention.self'''						)
				if "mha" in orig_key:
								__a    							=					orig_key.replace('''mha'''		,					'''attention'''						)
				if "W_q" in orig_key:
								__a    							=					orig_key.replace('''W_q'''		,					'''self.query'''						)
				if "W_k" in orig_key:
								__a    							=					orig_key.replace('''W_k'''		,					'''self.key'''						)
				if "W_v" in orig_key:
								__a    							=					orig_key.replace('''W_v'''		,					'''self.value'''						)
				if "ff.0" in orig_key:
								__a    							=					orig_key.replace('''ff.0'''		,					'''intermediate.dense'''						)
				if "ff.2" in orig_key:
								__a    							=					orig_key.replace('''ff.2'''		,					'''output.dense'''						)
				if "ff" in orig_key:
								__a    							=					orig_key.replace('''ff'''		,					'''output.dense'''						)
				if "mlm_class" in orig_key:
								__a    							=					orig_key.replace('''mlm.mlm_class'''		,					'''cls.predictions.decoder'''						)
				if "mlm" in orig_key:
								__a    							=					orig_key.replace('''mlm'''		,					'''cls.predictions.transform'''						)
				if "backbone.backbone.encoders" in orig_key:
								__a    							=					orig_key.replace('''backbone.backbone.encoders'''		,					'''encoder.layer'''						)
				if "cls" not in orig_key:
								__a    							=					'''mra.''' + orig_key

				return orig_key


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Any:
				for key in orig_state_dict.copy().keys():
								__a    							=					orig_state_dict.pop(a__						)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__a    							=					val

				__a    							=					orig_state_dict['''cls.predictions.decoder.bias''']
				__a    							=					torch.arange(a__						).expand((1, -1)						) + 2

				return orig_state_dict


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					torch.load(a__		,					map_location='''cpu'''						)['''model_state_dict''']
				__a    							=					MraConfig.from_json_file(a__						)
				__a    							=					MraForMaskedLM(a__						)

				__a    							=					convert_checkpoint_helper(config.max_position_embeddings		,					a__						)

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

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					A  :			Any     				=				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.'
					)
					A  :			Tuple     				=				parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)