





'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   MvpTokenizer
	snake_case_      =   MvpTokenizerFast
	snake_case_      =   True
	snake_case_      =   filter_roberta_detectors



	def   _lowerCamelCase    (   self	):
			super().setUp()
			lowerCAmelCase     :    List[str]						=   [
			    "l",
			    "o",
			    "w",
			    "e",
			    "r",
			    "s",
			    "t",
			    "i",
			    "d",
			    "n",
			    "\u0120",
			    "\u0120l",
			    "\u0120n",
			    "\u0120lo",
			    "\u0120low",
			    "er",
			    "\u0120lowest",
			    "\u0120newer",
			    "\u0120wider",
			    "<unk>",
			]
			lowerCAmelCase     :    List[str]						=   dict(zip(a_		,      range(len(a_	)	)	)	)
			lowerCAmelCase     :    Optional[Any]						=   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
			lowerCAmelCase     :    Optional[Any]						=   {"unk_token": "<unk>"}

			lowerCAmelCase     :    Optional[int]						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]	)
			lowerCAmelCase     :    List[Any]						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]	)
			with open(self.vocab_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write(json.dumps(a_	) + "\n"	)
			with open(self.merges_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write("\n".join(a_	)	)



	def   _lowerCamelCase    (   self		,      **a_	):
			kwargs.update(self.special_tokens_map	)
			return self.tokenizer_class.from_pretrained(self.tmpdirname		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			kwargs.update(self.special_tokens_map	)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			return "lower newer", "lower newer"



	@cached_property
	def   _lowerCamelCase    (   self	):
			return MvpTokenizer.from_pretrained("RUCAIBox/mvp"	)



	@cached_property
	def   _lowerCamelCase    (   self	):
			return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
			lowerCAmelCase     :    List[str]						=   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase     :    int						=   tokenizer(a_		,      max_length=len(a_	)		,      padding=a_		,      return_tensors="pt"	)
					self.assertIsInstance(a_		,      a_	)

					self.assertEqual((2, 9)		,      batch.input_ids.shape	)
					self.assertEqual((2, 9)		,      batch.attention_mask.shape	)
					lowerCAmelCase     :    int						=   batch.input_ids.tolist()[0]
					self.assertListEqual(a_		,      a_	)
					# Test that special tokens are reset



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase     :    List[str]						=   tokenizer(a_		,      padding=a_		,      return_tensors="pt"	)
					# check if input_ids are returned and no labels
					self.assertIn("input_ids"		,      a_	)
					self.assertIn("attention_mask"		,      a_	)
					self.assertNotIn("labels"		,      a_	)
					self.assertNotIn("decoder_attention_mask"		,      a_	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   [
			    "Summary of the text.",
			    "Another summary.",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase     :    Union[str, Any]						=   tokenizer(text_target=a_		,      max_length=32		,      padding="max_length"		,      return_tensors="pt"	)
					self.assertEqual(32		,      targets["input_ids"].shape[1]	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase     :    List[Any]						=   tokenizer(
					    ["I am a small frog" * 1_024, "I am a small frog"]		,      padding=a_		,      truncation=a_		,      return_tensors="pt"	)
					self.assertIsInstance(a_		,      a_	)
					self.assertEqual(batch.input_ids.shape		,      (2, 1_024)	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   ["A long paragraph for summarization."]
			lowerCAmelCase     :    List[str]						=   [
			    "Summary of the text.",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase     :    Any						=   tokenizer(a_		,      text_target=a_		,      return_tensors="pt"	)
					lowerCAmelCase     :    List[Any]						=   inputs["input_ids"]
					lowerCAmelCase     :    Optional[int]						=   inputs["labels"]
					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)



	def   _lowerCamelCase    (   self	):
			pass





	def   _lowerCamelCase    (   self	):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
							lowerCAmelCase     :    Optional[Any]						=   self.rust_tokenizer_class.from_pretrained(a_		,      **a_	)
							lowerCAmelCase     :    int						=   self.tokenizer_class.from_pretrained(a_		,      **a_	)
							lowerCAmelCase     :    Dict						=   "A, <mask> AllenNLP sentence."
							lowerCAmelCase     :    Any						=   tokenizer_r.encode_plus(a_		,      add_special_tokens=a_		,      return_token_type_ids=a_	)
							lowerCAmelCase     :    Optional[int]						=   tokenizer_p.encode_plus(a_		,      add_special_tokens=a_		,      return_token_type_ids=a_	)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r["token_type_ids"]	)		,      sum(tokens_p["token_type_ids"]	)	)

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)		,      sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)		,      )

							lowerCAmelCase     :    Dict						=   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
							lowerCAmelCase     :    Tuple						=   tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p["input_ids"]		,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
							self.assertSequenceEqual(tokens_r["input_ids"]		,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

							self.assertSequenceEqual(
							    a_		,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
							self.assertSequenceEqual(
							    a_		,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)










'''simple docstring'''






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 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self		,      a_	):
			for model_result in results.values():
					for batch_size, sequence_length in zip(model_result["bs"]		,      model_result["ss"]	):
							lowerCAmelCase     :    Tuple						=   model_result["result"][batch_size][sequence_length]
							self.assertIsNotNone(a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sgugger/tiny-distilbert-classification"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      only_pretrain_model=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    str						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    List[str]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    List[Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    str						=   "patrickvonplaten/t5-tiny-random"
			lowerCAmelCase     :    Tuple						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_		,      configs=[config]	)
			lowerCAmelCase     :    List[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	):
			lowerCAmelCase     :    str						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      use_xla=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    Optional[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_		,      )
					lowerCAmelCase     :    Optional[Any]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   "sshleifer/tiny-gpt2"

			def _check_summary_is_not_empty(a_	):
					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:
					lowerCAmelCase     :    int						=   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_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					lowerCAmelCase     :    Tuple						=   benchmark.run()
					_check_summary_is_not_empty(result.inference_summary	)
					self.assertTrue(Path(os.path.join(a_		,      "log.txt"	)	).exists()	)



