


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class    UpperCAmelCase_	(							unittest.TestCase):
							'''simple docstring'''






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[int]   	=		tempfile.mkdtemp()

														UpperCamelCase    :	List[str]   	=		[
														    '[UNK]',
														    '[CLS]',
														    '[SEP]',
														    '[PAD]',
														    '[MASK]',
														    'want',
														    '##want',
														    '##ed',
														    'wa',
														    'un',
														    'runn',
														    '##ing',
														    ',',
														    'low',
														    'lowest',
														]
														UpperCamelCase    :	Dict   	=		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    :	Tuple   	=		{
														    'do_resize': True,
														    'size': 20,
														    'do_center_crop': True,
														    'crop_size': 18,
														    'do_normalize': True,
														    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
														    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
														}
														UpperCamelCase    :	str   	=		os.path.join(self.tmpdirname ,					UpperCamelCase__						)
														with open(self.image_processor_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
																					json.dump(UpperCamelCase__ ,					UpperCamelCase__						)






							def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														return BertTokenizer.from_pretrained(self.tmpdirname ,					**UpperCamelCase__						)






							def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														return BertTokenizerFast.from_pretrained(self.tmpdirname ,					**UpperCamelCase__						)






							def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,					**UpperCamelCase__						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														shutil.rmtree(self.tmpdirname						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[Any]   	=		[np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta						)]
														UpperCamelCase    :	Optional[Any]   	=		[Image.fromarray(np.moveaxis(UpperCamelCase__ ,					0 ,					-1						)						) for x in image_inputs]
														return image_inputs






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Tuple   	=		self.get_tokenizer()
														UpperCamelCase    :	Dict   	=		self.get_rust_tokenizer()
														UpperCamelCase    :	Dict   	=		self.get_image_processor()

														UpperCamelCase    :	Optional[int]   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)
														processor_slow.save_pretrained(self.tmpdirname						)
														UpperCamelCase    :	Union[str, Any]   	=		AlignProcessor.from_pretrained(self.tmpdirname ,					use_fast=UpperCamelCase__						)

														UpperCamelCase    :	List[Any]   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)
														processor_fast.save_pretrained(self.tmpdirname						)
														UpperCamelCase    :	Union[str, Any]   	=		AlignProcessor.from_pretrained(self.tmpdirname						)

														self.assertEqual(processor_slow.tokenizer.get_vocab() ,					tokenizer_slow.get_vocab()						)
														self.assertEqual(processor_fast.tokenizer.get_vocab() ,					tokenizer_fast.get_vocab()						)
														self.assertEqual(tokenizer_slow.get_vocab() ,					tokenizer_fast.get_vocab()						)
														self.assertIsInstance(processor_slow.tokenizer ,					UpperCamelCase__						)
														self.assertIsInstance(processor_fast.tokenizer ,					UpperCamelCase__						)

														self.assertEqual(processor_slow.image_processor.to_json_string() ,					image_processor.to_json_string()						)
														self.assertEqual(processor_fast.image_processor.to_json_string() ,					image_processor.to_json_string()						)
														self.assertIsInstance(processor_slow.image_processor ,					UpperCamelCase__						)
														self.assertIsInstance(processor_fast.image_processor ,					UpperCamelCase__						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		AlignProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()						)
														processor.save_pretrained(self.tmpdirname						)

														UpperCamelCase    :	Optional[Any]   	=		self.get_tokenizer(bos_token='''(BOS)''' ,					eos_token='''(EOS)'''						)
														UpperCamelCase    :	int   	=		self.get_image_processor(do_normalize=UpperCamelCase__ ,					padding_value=1.0						)

														UpperCamelCase    :	List[Any]   	=		AlignProcessor.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 ,					UpperCamelCase__						)

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






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	int   	=		self.get_image_processor()
														UpperCamelCase    :	str   	=		self.get_tokenizer()

														UpperCamelCase    :	str   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)

														UpperCamelCase    :	Union[str, Any]   	=		self.prepare_image_inputs()

														UpperCamelCase    :	int   	=		image_processor(UpperCamelCase__ ,					return_tensors='''np'''						)
														UpperCamelCase    :	Dict   	=		processor(images=UpperCamelCase__ ,					return_tensors='''np'''						)

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






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[str]   	=		self.get_image_processor()
														UpperCamelCase    :	Optional[Any]   	=		self.get_tokenizer()

														UpperCamelCase    :	List[str]   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)

														UpperCamelCase    :	Tuple   	=		'lower newer'

														UpperCamelCase    :	Tuple   	=		processor(text=UpperCamelCase__						)

														UpperCamelCase    :	int   	=		tokenizer(UpperCamelCase__ ,					padding='''max_length''' ,					max_length=64						)

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






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Dict   	=		self.get_image_processor()
														UpperCamelCase    :	List[Any]   	=		self.get_tokenizer()

														UpperCamelCase    :	str   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)

														UpperCamelCase    :	str   	=		'lower newer'
														UpperCamelCase    :	Optional[int]   	=		self.prepare_image_inputs()

														UpperCamelCase    :	Optional[int]   	=		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 pytest.raises(UpperCamelCase__						):
																					processor()






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[str]   	=		self.get_image_processor()
														UpperCamelCase    :	List[str]   	=		self.get_tokenizer()

														UpperCamelCase    :	Union[str, Any]   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)

														UpperCamelCase    :	Tuple   	=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														UpperCamelCase    :	List[str]   	=		processor.batch_decode(UpperCamelCase__						)
														UpperCamelCase    :	Any   	=		tokenizer.batch_decode(UpperCamelCase__						)

														self.assertListEqual(UpperCamelCase__ ,					UpperCamelCase__						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Dict   	=		self.get_image_processor()
														UpperCamelCase    :	int   	=		self.get_tokenizer()

														UpperCamelCase    :	Union[str, Any]   	=		AlignProcessor(tokenizer=UpperCamelCase__ ,					image_processor=UpperCamelCase__						)

														UpperCamelCase    :	str   	=		'lower newer'
														UpperCamelCase    :	List[str]   	=		self.prepare_image_inputs()

														UpperCamelCase    :	List[Any]   	=		processor(text=UpperCamelCase__ ,					images=UpperCamelCase__						)

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





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

