





import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_UpperCAmelCase        =				{
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

_UpperCAmelCase        =				{
    'ctrl': 2_5_6,
}

_UpperCAmelCase        =				{
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Tuple:
							UpperCamelCase_          =  set()
							UpperCamelCase_          =  word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)  )
														UpperCamelCase_          =  char

							UpperCamelCase_          =  set(a_  )
							return pairs







class 							_UpperCamelCase					(	_UpperCamelCase     ):
							_UpperCamelCase				:					Dict         =   VOCAB_FILES_NAMES
							_UpperCamelCase				:					Optional[Any]         =   PRETRAINED_VOCAB_FILES_MAP
							_UpperCamelCase				:					Optional[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCamelCase				:					Union[str, Any]         =   CONTROL_CODES

							def __init__(		self: List[str]					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: str="<unk>"					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




														"""simple docstring"""


														super().__init__(unk_token=_UpperCAmelCase					,   **_UpperCAmelCase		)

														with open(_UpperCAmelCase					,   encoding="utf-8"		) as vocab_handle:
																					UpperCamelCase_          =  json.load(_UpperCAmelCase		)
														UpperCamelCase_          =  {v: k for k, v in self.encoder.items()}
														with open(_UpperCAmelCase					,   encoding="utf-8"		) as merges_handle:
																					UpperCamelCase_          =  merges_handle.read().split("\n"		)[1:-1]
														UpperCamelCase_          =  [tuple(merge.split()		) for merge in merges]
														UpperCamelCase_          =  dict(zip(_UpperCAmelCase					,   range(len(_UpperCAmelCase		)		)		)		)
														UpperCamelCase_          =  {}

							@property
							def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




														"""simple docstring"""


														return len(self.encoder		)

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




														"""simple docstring"""


														return dict(self.encoder					,   **self.added_tokens_encoder		)

							def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> Dict:




														"""simple docstring"""


														if token in self.cache:
																					return self.cache[token]
														UpperCamelCase_          =  tuple(_UpperCAmelCase		)
														UpperCamelCase_          =  tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
														UpperCamelCase_          =  get_pairs(_UpperCAmelCase		)

														if not pairs:
																					return token

														while True:
																					UpperCamelCase_          =  min(_UpperCAmelCase					,   key=lambda _SCREAMING_SNAKE_CASE		: self.bpe_ranks.get(_UpperCAmelCase					,   float("inf"		)		)		)
																					if bigram not in self.bpe_ranks:
																												break
																					UpperCamelCase_          =  bigram
																					UpperCamelCase_          =  []
																					UpperCamelCase_          =  0
																					while i < len(_UpperCAmelCase		):
																												try:
																																			UpperCamelCase_          =  word.index(_UpperCAmelCase					,   _UpperCAmelCase		)
																												except ValueError:
																																			new_word.extend(word[i:]		)
																																			break
																												else:
																																			new_word.extend(word[i:j]		)
																																			UpperCamelCase_          =  j

																												if word[i] == first and i < len(_UpperCAmelCase		) - 1 and word[i + 1] == second:
																																			new_word.append(first + second		)
																																			i += 2
																												else:
																																			new_word.append(word[i]		)
																																			i += 1
																					UpperCamelCase_          =  tuple(_UpperCAmelCase		)
																					UpperCamelCase_          =  new_word
																					if len(_UpperCAmelCase		) == 1:
																												break
																					else:
																												UpperCamelCase_          =  get_pairs(_UpperCAmelCase		)
														UpperCamelCase_          =  '''@@ '''.join(_UpperCAmelCase		)
														UpperCamelCase_          =  word[:-4]
														UpperCamelCase_          =  word
														return word

							def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: int		)       -> Union[str, Any]:




														"""simple docstring"""


														UpperCamelCase_          =  []

														UpperCamelCase_          =  re.findall(R"\S+\n?"					,   _UpperCAmelCase		)

														for token in words:
																					split_tokens.extend(list(self.bpe(_UpperCAmelCase		).split(" "		)		)		)
														return split_tokens

							def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> List[Any]:




														"""simple docstring"""


														return self.encoder.get(_UpperCAmelCase					,   self.encoder.get(self.unk_token		)		)

							def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> List[Any]:




														"""simple docstring"""


														return self.decoder.get(_UpperCAmelCase					,   self.unk_token		)

							def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> List[str]:




														"""simple docstring"""


														UpperCamelCase_          =  ''' '''.join(_UpperCAmelCase		).replace("@@ "					,   ""		).strip()
														return out_string



							def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: int = None		)       -> Dict:




														"""simple docstring"""


														if not os.path.isdir(_UpperCAmelCase		):
																					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																					return
														UpperCamelCase_          =  os.path.join(
														    _UpperCAmelCase					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
														UpperCamelCase_          =  os.path.join(
														    _UpperCAmelCase					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

														with open(_UpperCAmelCase					,   "w"					,   encoding="utf-8"		) as f:
																					f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=_UpperCAmelCase					,   ensure_ascii=_UpperCAmelCase		) + "\n"		)

														UpperCamelCase_          =  0
														with open(_UpperCAmelCase					,   "w"					,   encoding="utf-8"		) as writer:
																					writer.write("#version: 0.2\n"		)
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda _SCREAMING_SNAKE_CASE		: kv[1]		):
																												if index != token_index:
																																			logger.warning(
																																			    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																			    " Please check that the tokenizer is not corrupted!"		)
																																			UpperCamelCase_          =  token_index
																												writer.write(" ".join(_UpperCAmelCase		) + "\n"		)
																												index += 1

														return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
     from sagemaker import Session, TrainingJobAnalytics
     from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''  ,						'''False'''     )     ) is not True  ,						reason='''Skipping test because should only be run when releasing minor transformers version'''  ,						)
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """simple docstring"""


              if self.framework == "pytorch":
                     subprocess.run(
                         f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()					,   encoding="utf-8"					,   check=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

              # creates estimator
              return HuggingFace(
                  entry_point=self.script					,   source_dir=self.env.test_path					,   role=self.env.role					,   image_uri=self.env.image_uri					,   base_job_name=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

              # result dataframe
              UpperCamelCase_          =  TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # assert kpis
              assert train_runtime <= self.results["train_runtime"]
              assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
              assert all(t <= self.results["eval_loss"] for t in eval_loss		)

              # dump tests result into json file to share in PR
              with open(f'''{estimator.latest_training_job.name}.json'''					,   "w"		) as outfile:
                     json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}					,   _SCREAMING_SNAKE_CASE		)

