





import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
      import jieba

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


__UpperCAmelCase       :   Optional[Any]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   List[str]				       =							{"vocab_file": "vocab.txt"}

__UpperCAmelCase       :   Any				       =							{
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

__UpperCAmelCase       :   Optional[Any]				       =							{
    "openbmb/cpm-ant-10b": 1_024,
}







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  __snake_case:						int   =						collections.OrderedDict()
  with open(SCREAMING_SNAKE_CASE__					,					"""r"""					,					encoding="""utf-8""") as reader:
    __snake_case:						Any   =						reader.readlines()
  for index, token in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:						Union[str, Any]   =						token.rstrip("""\n""")
    __snake_case:						Union[str, Any]   =						index
  return vocab







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    str							,				A :    int							,				A :    Dict="<unk>"							,				A :    Union[str, Any]=200		):
         __snake_case:						Union[str, Any]   =						vocab
         __snake_case:						Dict   =						unk_token
         __snake_case:						int   =						max_input_chars_per_word






       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Any		):
         __snake_case:						List[Any]   =						list(A		)
         if len(A		) > self.max_input_chars_per_word:
           return [self.unk_token]

         __snake_case:						Any   =						0
         __snake_case:						Tuple   =						[]
         while start < len(A		):
           __snake_case:						str   =						len(A		)
           __snake_case:						List[str]   =						None
           while start < end:
             __snake_case:						str   =						"""""".join(chars[start:end]		)
             if substr in self.vocab:
               __snake_case:						Union[str, Any]   =						substr
               break
             end -= 1
           if cur_substr is None:
             sub_tokens.append(self.unk_token		)
             start += 1
           else:
             sub_tokens.append(A		)
             __snake_case:						List[str]   =						end

         return sub_tokens







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     VOCAB_FILES_NAMES
       lowerCAmelCase__      		=     PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__      		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__      		=     ["""input_ids""", """attention_mask"""]
       lowerCAmelCase__      		=     False




       def __init__(  self :    Tuple							,				A :    Union[str, Any]							,				A :    Tuple="<d>"							,				A :    Union[str, Any]="</d>"							,				A :    Union[str, Any]="<s>"							,				A :    Tuple="</s>"							,				A :    List[str]="<pad>"							,				A :    str="<unk>"							,				A :    Union[str, Any]="</n>"							,				A :    int="</_>"							,				A :    Tuple="left"							,				**A :    int							,				):
         requires_backends(self							,				["""jieba"""]		)
         super().__init__(
             bod_token=A							,				eod_token=A							,				bos_token=A							,				eos_token=A							,				pad_token=A							,				unk_token=A							,				line_token=A							,				space_token=A							,				padding_side=A							,				**A							,				)
         __snake_case:						Optional[int]   =						bod_token
         __snake_case:						Dict   =						eod_token
         __snake_case:						Union[str, Any]   =						load_vocab(A		)
         __snake_case:						Optional[int]   =						self.encoder[space_token]
         __snake_case:						Optional[Any]   =						self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         __snake_case:						Any   =						collections.OrderedDict(sorted(self.encoder.items()							,				key=lambda A		: x[1]		)		)
         __snake_case:						Dict   =						{v: k for k, v in self.encoder.items()}

         __snake_case:						Union[str, Any]   =						WordpieceTokenizer(vocab=self.encoder							,				unk_token=self.unk_token		)




       @property
       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         return self.encoder[self.bod_token]




       @property
       def 							UpperCAmelCase__ (  self :    int		):
         return self.encoder[self.eod_token]




       @property
       def 							UpperCAmelCase__ (  self :    List[str]		):
         return self.encoder["\n"]




       @property
       def 							UpperCAmelCase__ (  self :    str		):
         return len(self.encoder		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         return dict(self.encoder							,				**self.added_tokens_encoder		)




       def 							UpperCAmelCase__ (  self :    str							,				A :    Optional[int]		):
         __snake_case:						List[str]   =						[]
         for x in jieba.cut(A							,				cut_all=A		):
           output_tokens.extend(self.wordpiece_tokenizer.tokenize(A		)		)
         return output_tokens




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Dict							,				**A :    Optional[Any]		):
         __snake_case:						List[Any]   =						[i for i in token_ids if i >= 0]
         __snake_case:						Dict   =						[
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(A							,				**A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    Optional[Any]		):
         return token in self.encoder




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    List[str]		):
         return "".join(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[Any]		):
         return self.encoder.get(A							,				self.encoder.get(self.unk_token		)		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]		):
         return self.decoder.get(A							,				self.unk_token		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    str							,				A :    Optional[str] = None		):
         if os.path.isdir(A		):
           __snake_case:						Dict   =						os.path.join(
               A							,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
         else:
           __snake_case:						Union[str, Any]   =						(filename_prefix + """-""" if filename_prefix else """""") + save_directory
         __snake_case:						str   =						0
         if " " in self.encoder:
           __snake_case:						List[Any]   =						self.encoder[""" """]
           del self.encoder[" "]
         if "\n" in self.encoder:
           __snake_case:						int   =						self.encoder["""\n"""]
           del self.encoder["\n"]
         __snake_case:						Optional[Any]   =						collections.OrderedDict(sorted(self.encoder.items()							,				key=lambda A		: x[1]		)		)
         with open(A							,				"""w"""							,				encoding="""utf-8"""		) as writer:
           for token, token_index in self.encoder.items():
             if index != token_index:
               logger.warning(
                   f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                   """ Please check that the vocabulary is not corrupted!"""		)
               __snake_case:						Optional[Any]   =						token_index
             writer.write(token + """\n"""		)
             index += 1
         return (vocab_file,)




       def 							UpperCAmelCase__ (  self :    int							,				A :    List[int]							,				A :    List[int] = None		):
         if token_ids_a is None:
           return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






       def 							UpperCAmelCase__ (  self :    Any							,				A :    List[int]							,				A :    Optional[List[int]] = None							,				A :    bool = False		):

         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A							,				token_ids_a=A							,				already_has_special_tokens=A		)

         if token_ids_a is not None:
           return [1] + ([0] * len(A		)) + [1] + ([0] * len(A		))
         return [1] + ([0] * len(A		))













import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
      import tensorflow as tf

      from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,				model_result["""ss"""]		):
             __snake_case:						Dict   =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Dict   =						TensorFlowBenchmark(A		)
         __snake_case:						List[str]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				only_pretrain_model=A							,				)
         __snake_case:						Any   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						int   =						TensorFlowBenchmark(A		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[int]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						str   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Tuple   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[int]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						List[str]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Dict   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[Any]   =						"""patrickvonplaten/t5-tiny-random"""
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Optional[int]   =						TensorFlowBenchmark(A							,				configs=[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""		)		) == 0							,				"""Cannot do xla on CPU."""		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				use_xla=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						int   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				save_to_csv=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				inference_time_csv_file=os.path.join(A							,				"""inf_time.csv"""		)							,				inference_memory_csv_file=os.path.join(A							,				"""inf_mem.csv"""		)							,				env_info_csv_file=os.path.join(A							,				"""env.csv"""		)							,				multi_process=A							,				)
           __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A							,				"""inf_time.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""inf_mem.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""env.csv"""		)		).exists()		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A :    Union[str, Any]		):
           self.assertTrue(hasattr(A							,				"""sequential"""		)		)
           self.assertTrue(hasattr(A							,				"""cumulative"""		)		)
           self.assertTrue(hasattr(A							,				"""current"""		)		)
           self.assertTrue(hasattr(A							,				"""total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				log_filename=os.path.join(A							,				"""log.txt"""		)							,				log_print=A							,				trace_memory_line_by_line=A							,				eager_mode=A							,				multi_process=A							,				)
           __snake_case:						Dict   =						TensorFlowBenchmark(A		)
           __snake_case:						List[str]   =						benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           self.assertTrue(Path(os.path.join(A							,				"""log.txt"""		)		).exists()		)






