
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 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     tempfile.mkdtemp()

				UpperCamelCase		=     [
				    '[UNK]',
				    '[CLS]',
				    '[SEP]',
				    '[PAD]',
				    '[MASK]',
				    'want',
				    '##want',
				    '##ed',
				    'wa',
				    'un',
				    'runn',
				    '##ing',
				    ',',
				    'low',
				    'lowest',
				]
				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': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase		=     os.path.join(self.tmpdirname				,      A_       )
				with open(self.image_processor_file				,      'w'				,      encoding='utf-8'       ) as fp:
						json.dump(A_				,      A_       )



		def __UpperCamelCase ( self				,      **A_       )  ->					Dict:
				"""simple docstring"""
				return BertTokenizer.from_pretrained(self.tmpdirname				,      **A_       )



		def __UpperCamelCase ( self				,      **A_       )  ->					List[Any]:
				"""simple docstring"""
				return BertTokenizerFast.from_pretrained(self.tmpdirname				,      **A_       )



		def __UpperCamelCase ( self				,      **A_       )  ->					Tuple:
				"""simple docstring"""
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname				,      **A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     [np.random.randint(255				,      size=(3, 30, 400)				,      dtype=np.uinta       )]
				UpperCamelCase		=     [Image.fromarray(np.moveaxis(A_				,      0				,      -1       )       ) for x in image_inputs]
				return image_inputs



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.get_tokenizer()
				UpperCamelCase		=     self.get_rust_tokenizer()
				UpperCamelCase		=     self.get_image_processor()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )
				processor_slow.save_pretrained(self.tmpdirname       )
				UpperCamelCase		=     AlignProcessor.from_pretrained(self.tmpdirname				,      use_fast=A_       )

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )
				processor_fast.save_pretrained(self.tmpdirname       )
				UpperCamelCase		=     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				,      A_       )
				self.assertIsInstance(processor_fast.tokenizer				,      A_       )

				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				,      A_       )
				self.assertIsInstance(processor_fast.image_processor				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     AlignProcessor(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=A_				,      padding_value=1.0       )

				UpperCamelCase		=     AlignProcessor.from_pretrained(
				    self.tmpdirname				,      bos_token='(BOS)'				,      eos_token='(EOS)'				,      do_normalize=A_				,      padding_value=1.0       )

				self.assertEqual(processor.tokenizer.get_vocab()				,      tokenizer_add_kwargs.get_vocab()       )
				self.assertIsInstance(processor.tokenizer				,      A_       )

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



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.get_image_processor()
				UpperCamelCase		=     self.get_tokenizer()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )

				UpperCamelCase		=     self.prepare_image_inputs()

				UpperCamelCase		=     image_processor(A_				,      return_tensors='np'       )
				UpperCamelCase		=     processor(images=A_				,      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 __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.get_image_processor()
				UpperCamelCase		=     self.get_tokenizer()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )

				UpperCamelCase		=     'lower newer'

				UpperCamelCase		=     processor(text=A_       )

				UpperCamelCase		=     tokenizer(A_				,      padding='max_length'				,      max_length=64       )

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.get_image_processor()
				UpperCamelCase		=     self.get_tokenizer()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )

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

				UpperCamelCase		=     processor(text=A_				,      images=A_       )

				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(A_       ):
						processor()



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.get_image_processor()
				UpperCamelCase		=     self.get_tokenizer()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )

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

				UpperCamelCase		=     processor.batch_decode(A_       )
				UpperCamelCase		=     tokenizer.batch_decode(A_       )

				self.assertListEqual(A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.get_image_processor()
				UpperCamelCase		=     self.get_tokenizer()

				UpperCamelCase		=     AlignProcessor(tokenizer=A_				,      image_processor=A_       )

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

				UpperCamelCase		=     processor(text=A_				,      images=A_       )

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


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
