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 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['''bs''']					,model_result['''ss''']							):
               UpperCAmelCase__							:				str   				=  model_result["result"][batch_size][sequence_length]
               self.assertIsNotNone(lowerCAmelCase__							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__							:				List[str]   				=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,training=lowerCAmelCase__					,inference=lowerCAmelCase__					,sequence_lengths=[8]					,batch_sizes=[1]					,eager_mode=lowerCAmelCase__					,multi_process=lowerCAmelCase__					,)
         UpperCAmelCase__							:				Any   				=  TensorFlowBenchmark(lowerCAmelCase__							)
         UpperCAmelCase__							:				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 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__							:				Dict   				=  AutoConfig.from_pretrained(lowerCAmelCase__							)
         UpperCAmelCase__							:				List[Any]   				=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,training=lowerCAmelCase__					,inference=lowerCAmelCase__					,sequence_lengths=[8]					,batch_sizes=[1]					,multi_process=lowerCAmelCase__					,)
         UpperCAmelCase__							:				Tuple   				=  TensorFlowBenchmark(lowerCAmelCase__					,[config]							)
         UpperCAmelCase__							:				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 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  "patrickvonplaten/t5-tiny-random"
         UpperCAmelCase__							:				List[str]   				=  AutoConfig.from_pretrained(lowerCAmelCase__							)
         UpperCAmelCase__							:				List[str]   				=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,training=lowerCAmelCase__					,inference=lowerCAmelCase__					,sequence_lengths=[8]					,batch_sizes=[1]					,multi_process=lowerCAmelCase__					,)
         UpperCAmelCase__							:				Union[str, Any]   				=  TensorFlowBenchmark(lowerCAmelCase__					,configs=[config]							)
         UpperCAmelCase__							:				Any   				=  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 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__							:				Any   				=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]					,training=lowerCAmelCase__					,inference=lowerCAmelCase__					,sequence_lengths=[8]					,batch_sizes=[1]					,use_xla=lowerCAmelCase__					,multi_process=lowerCAmelCase__					,)
         UpperCAmelCase__							:				Optional[int]   				=  TensorFlowBenchmark(lowerCAmelCase__							)
         UpperCAmelCase__							:				Optional[Any]   				=  benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result							)
         self.check_results_dict_not_empty(results.memory_inference_result							)




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  "sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(lowerCamelCase_							):
            self.assertTrue(hasattr(lowerCAmelCase__					,'''sequential'''							)							)
            self.assertTrue(hasattr(lowerCAmelCase__					,'''cumulative'''							)							)
            self.assertTrue(hasattr(lowerCAmelCase__					,'''current'''							)							)
            self.assertTrue(hasattr(lowerCAmelCase__					,'''total'''							)							)

         with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase__							:				Tuple   				=  TensorFlowBenchmarkArguments(
                models=[MODEL_ID]					,inference=lowerCAmelCase__					,sequence_lengths=[8]					,batch_sizes=[1]					,log_filename=os.path.join(lowerCAmelCase__					,'''log.txt'''							)					,log_print=lowerCAmelCase__					,trace_memory_line_by_line=lowerCAmelCase__					,eager_mode=lowerCAmelCase__					,multi_process=lowerCAmelCase__					,)
            UpperCAmelCase__							:				str   				=  TensorFlowBenchmark(lowerCAmelCase__							)
            UpperCAmelCase__							:				Optional[Any]   				=  benchmark.run()
            _check_summary_is_not_empty(result.inference_summary							)
            self.assertTrue(Path(os.path.join(lowerCAmelCase__					,'''log.txt'''							)							).exists()							)





'''simple docstring'''

import json
import os
from typing import Optional, Tuple

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


UpperCamelCase__     :	int				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{'vocab_file': 'vocab.json'}

UpperCamelCase__     :	Tuple				=			{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

UpperCamelCase__     :	List[Any]				=			{'mgp-str': 27}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      int      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[s]"					,lowerCamelCase_="[GO]"					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         super().__init__(
             unk_token=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,**lowerCamelCase_					,)

         with open(lowerCamelCase_					,encoding='''utf-8'''							) as vocab_handle:
            UpperCAmelCase__							:				Optional[Any]   				=  json.load(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  {v: k for k, v in self.vocab.items()}




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return len(self.vocab							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  []
         for s in text:
            char_tokens.extend(lowerCamelCase_							)
         return char_tokens




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.vocab.get(lowerCamelCase_					,self.vocab.get(self.unk_token							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return self.decoder.get(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

         return (vocab_file,)




