



"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     Tuple						):
           for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]						):
                     _UpperCAmelCase      :     Tuple        =   model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     str        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     Optional[int]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     List[str]        =   PyTorchBenchmark(A						)
           _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 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     int        =   "sgugger/tiny-distilbert-classification"
           _UpperCAmelCase      :     Optional[int]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     only_pretrain_model=A					,     )
           _UpperCAmelCase      :     List[str]        =   PyTorchBenchmark(A						)
           _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 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     Optional[int]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     Optional[int]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     torchscript=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     Optional[int]        =   PyTorchBenchmark(A						)
           _UpperCAmelCase      :     Union[str, 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(torch_device == "cpu"					,     "Cant do half precision"						)
      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[Any]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     Any        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     fpaa=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     str        =   PyTorchBenchmark(A						)
           _UpperCAmelCase      :     Union[str, Any]        =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result						)
           self.check_results_dict_not_empty(results.memory_inference_result						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Optional[Any]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     Any        =   AutoConfig.from_pretrained(A						)
           # set architectures equal to `None`
           _UpperCAmelCase      :     int        =   None
           _UpperCAmelCase      :     List[Any]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     Optional[Any]        =   PyTorchBenchmark(A					,     configs=[config]						)
           _UpperCAmelCase      :     Optional[int]        =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result						)
           self.check_results_dict_not_empty(results.memory_inference_result						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[Any]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     Dict        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     Dict        =   PyTorchBenchmark(A						)
           _UpperCAmelCase      :     Tuple        =   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result						)
           self.check_results_dict_not_empty(results.memory_train_result						)



      @unittest.skipIf(torch_device == "cpu"					,     "Can't do half precision"						)
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     int        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     fpaa=A					,     multi_process=A					,     )
           _UpperCAmelCase      :     Union[str, Any]        =   PyTorchBenchmark(A						)
           _UpperCAmelCase      :     Any        =   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result						)
           self.check_results_dict_not_empty(results.memory_train_result						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Dict        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     List[str]        =   AutoConfig.from_pretrained(A						)
           _UpperCAmelCase      :     Optional[Any]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     List[str]        =   PyTorchBenchmark(A					,     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						)



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     List[Any]        =   "sshleifer/tinier_bart"
           _UpperCAmelCase      :     Tuple        =   AutoConfig.from_pretrained(A						)
           _UpperCAmelCase      :     List[str]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     str        =   PyTorchBenchmark(A					,     configs=[config]						)
           _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 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     List[str]        =   "sshleifer/tiny-gpt2"
           _UpperCAmelCase      :     List[str]        =   AutoConfig.from_pretrained(A						)
           _UpperCAmelCase      :     Optional[Any]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     int        =   PyTorchBenchmark(A					,     configs=[config]						)
           _UpperCAmelCase      :     Dict        =   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result						)
           self.check_results_dict_not_empty(results.memory_train_result						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     List[str]        =   "sshleifer/tinier_bart"
           _UpperCAmelCase      :     Tuple        =   AutoConfig.from_pretrained(A						)
           _UpperCAmelCase      :     Union[str, Any]        =   PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,     training=A					,     inference=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=A					,     )
           _UpperCAmelCase      :     List[Any]        =   PyTorchBenchmark(A					,     configs=[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 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     Tuple        =   "sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase      :     Union[str, Any]        =   PyTorchBenchmarkArguments(
                    models=[MODEL_ID]					,     training=A					,     inference=A					,     save_to_csv=A					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     inference_time_csv_file=os.path.join(A					,     "inf_time.csv"						)					,     train_memory_csv_file=os.path.join(A					,     "train_mem.csv"						)					,     inference_memory_csv_file=os.path.join(A					,     "inf_mem.csv"						)					,     train_time_csv_file=os.path.join(A					,     "train_time.csv"						)					,     env_info_csv_file=os.path.join(A					,     "env.csv"						)					,     multi_process=A					,     )
                _UpperCAmelCase      :     int        =   PyTorchBenchmark(A						)
                benchmark.run()
                self.assertTrue(Path(os.path.join(A					,     "inf_time.csv"						)						).exists()						)
                self.assertTrue(Path(os.path.join(A					,     "train_time.csv"						)						).exists()						)
                self.assertTrue(Path(os.path.join(A					,     "inf_mem.csv"						)						).exists()						)
                self.assertTrue(Path(os.path.join(A					,     "train_mem.csv"						)						).exists()						)
                self.assertTrue(Path(os.path.join(A					,     "env.csv"						)						).exists()						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     str        =   "sshleifer/tiny-gpt2"

           def _check_summary_is_not_empty(A					:     Dict						):
                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:
                _UpperCAmelCase      :     Optional[int]        =   PyTorchBenchmarkArguments(
                    models=[MODEL_ID]					,     training=A					,     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					,     multi_process=A					,     )
                _UpperCAmelCase      :     int        =   PyTorchBenchmark(A						)
                _UpperCAmelCase      :     Dict        =   benchmark.run()
                _check_summary_is_not_empty(result.inference_summary						)
                _check_summary_is_not_empty(result.train_summary						)
                self.assertTrue(Path(os.path.join(A					,     "log.txt"						)						).exists()						)





"""simple docstring"""




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			GPTSanJapaneseTokenizer
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			{'do_clean_text': False, 'add_prefix_space': False}



      def 						snake_case_ (					self					:     Any						):
           super().setUp()

           # fmt: off
           _UpperCAmelCase      :     Any        =   ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase      :     Optional[int]        =   {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
           _UpperCAmelCase      :     List[Any]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     List[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["emoji_file"]						)
           with open(self.vocab_file					,     "w"					,     encoding="utf-8"						) as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)
           with open(self.emoji_file					,     "w"						) as emoji_writer:
                emoji_writer.write(json.dumps(A						)						)



      def 						snake_case_ (					self					:     int					,     **A					:     List[str]						):
           kwargs.update(self.special_tokens_map						)
           return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     Any						):
           _UpperCAmelCase      :     Optional[Any]        =   "こんにちは、世界。 \nこんばんは、㔺界。😀"
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。 \nこんばんは、世界。😀"
           return input_text, output_text



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     str						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   self.get_input_output_texts(A						)
           _UpperCAmelCase      :     List[Any]        =   tokenizer.encode(A					,     add_special_tokens=A						)
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.decode(A					,     clean_up_tokenization_spaces=A						)
           return text, ids



      def 						snake_case_ (					self					:     Any						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     int						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Optional[int]        =   "こんにちは、世界。　こんばんは、㔺界。"
           _UpperCAmelCase      :     Dict        =   ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
           _UpperCAmelCase      :     List[Any]        =   tokenizer.tokenize(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids without special tokens
           _UpperCAmelCase      :     Optional[int]        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
           _UpperCAmelCase      :     List[str]        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids with special tokens
           _UpperCAmelCase      :     str        =   tokens + [tokenizer.unk_token]
           _UpperCAmelCase      :     Any        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           _UpperCAmelCase      :     str        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Dict        =   "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
           _UpperCAmelCase      :     Tuple        =   "こんにちは、、、、世界。こんばんは、、、、世界。"
           _UpperCAmelCase      :     int        =   tokenizer.encode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[str]        =   "こんばんは、㔺界。😀"
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。こんばんは、世界。😀"
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.encode(prefix_text + input_text						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.encode(""					,     prefix_text=prefix_text + input_text						)
           _UpperCAmelCase      :     Optional[int]        =   tokenizer.encode(A					,     prefix_text=A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Dict        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[Any]        =   "こんばんは、㔺界。😀"

           _UpperCAmelCase      :     Optional[Any]        =   len(tokenizer.encode(A						)						) - 2
           _UpperCAmelCase      :     List[Any]        =   len(tokenizer.encode(A						)						) - 2

           _UpperCAmelCase      :     List[str]        =   [1] + [0] * (len_prefix + len_text + 1)
           _UpperCAmelCase      :     str        =   [1] * (len_prefix + len_text + 1) + [0]
           _UpperCAmelCase      :     int        =   [1] + [1] * (len_prefix) + [0] * (len_text + 1)

           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer(prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     Any        =   tokenizer(""					,     prefix_text=prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     List[Any]        =   tokenizer(A					,     prefix_text=A						).token_type_ids
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     str        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Dict        =   tokenizer.encode("あンいワ"						)
           _UpperCAmelCase      :     str        =   tokenizer.encode(""					,     prefix_text="あンいワ"						)
           _UpperCAmelCase      :     Dict        =   tokenizer.encode("いワ"					,     prefix_text="あン"						)

           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertNotEqual(A					,     A						)
           self.assertNotEqual(A					,     A						)
           self.assertEqual(x_token_a[1]					,     x_token_a[-1]						)  # SEG token
           self.assertEqual(x_token_a[1]					,     x_token_a[3]						)  # SEG token



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Tuple        =   [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
           _UpperCAmelCase      :     Tuple        =   tokenizer(A					,     padding=A						)
           _UpperCAmelCase      :     str        =   tokenizer.batch_encode_plus(A					,     padding=A						)

           # fmt: off
           _UpperCAmelCase      :     str        =   [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
           _UpperCAmelCase      :     str        =   [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
           _UpperCAmelCase      :     int        =   [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
           # fmt: on
           self.assertListEqual(x_token.input_ids					,     A						)
           self.assertListEqual(x_token.token_type_ids					,     A						)
           self.assertListEqual(x_token.attention_mask					,     A						)
           self.assertListEqual(x_token_a.input_ids					,     A						)
           self.assertListEqual(x_token_a.token_type_ids					,     A						)
           self.assertListEqual(x_token_a.attention_mask					,     A						)



      def 						snake_case_ (					self					:     List[Any]						):
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass



      def 						snake_case_ (					self					:     int						):
           # tokenizer has no padding token
           pass
