






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 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['bs']		,		model_result['ss'] ):
           UpperCamelCase_									=     model_result['result'][batch_size][sequence_length]
           self.assertIsNotNone(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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[str]:
     UpperCamelCase_									=     'sgugger/tiny-distilbert-classification'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		only_pretrain_model=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		torchscript=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		fpaa=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> Tuple:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     # set architectures equal to `None`
     UpperCamelCase_									=     None
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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[str]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		fpaa=_UpperCAmelCase		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> Tuple:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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 )			-> Tuple:
     UpperCamelCase_									=     'sshleifer/tinier_bart'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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 )			-> Optional[int]:
     UpperCamelCase_									=     'sshleifer/tinier_bart'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     PyTorchBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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 )			-> Any:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     with tempfile.TemporaryDirectory() as tmp_dir:
        UpperCamelCase_									=     PyTorchBenchmarkArguments(
            models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		save_to_csv=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		inference_time_csv_file=os.path.join(_UpperCAmelCase		,		'inf_time.csv' )		,		train_memory_csv_file=os.path.join(_UpperCAmelCase		,		'train_mem.csv' )		,		inference_memory_csv_file=os.path.join(_UpperCAmelCase		,		'inf_mem.csv' )		,		train_time_csv_file=os.path.join(_UpperCAmelCase		,		'train_time.csv' )		,		env_info_csv_file=os.path.join(_UpperCAmelCase		,		'env.csv' )		,		multi_process=_UpperCAmelCase		,		)
        UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'inf_time.csv' ) ).exists() )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'train_time.csv' ) ).exists() )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'inf_mem.csv' ) ).exists() )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'train_mem.csv' ) ).exists() )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'env.csv' ) ).exists() )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'

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

     with tempfile.TemporaryDirectory() as tmp_dir:
        UpperCamelCase_									=     PyTorchBenchmarkArguments(
            models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		log_filename=os.path.join(_UpperCAmelCase		,		'log.txt' )		,		log_print=_UpperCAmelCase		,		trace_memory_line_by_line=_UpperCAmelCase		,		multi_process=_UpperCAmelCase		,		)
        UpperCamelCase_									=     PyTorchBenchmark(_UpperCAmelCase )
        UpperCamelCase_									=     benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        _check_summary_is_not_empty(result.train_summary )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'log.txt' ) ).exists() )














import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['vocab_file'] )
     with open(self.vocab_file		,		'w'		,		encoding='utf-8' ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

     UpperCamelCase_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





