

'''simple docstring'''
import numpy as np


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  int="utf-8"							,				_UpperCamelCase		:  Optional[int]="surrogatepass"       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=bin(int.from_bytes(text.encode(_UpperCamelCase							,				_UpperCamelCase       )							,				'big'       )       )[2:]
     return bits.zfill(8 * ((len(_UpperCamelCase       ) + 7) // 8)       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  int="utf-8"							,				_UpperCamelCase		:  List[str]="surrogatepass"       )   ->    Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=int(_UpperCamelCase							,				2       )
     return n.to_bytes((n.bit_length() + 7) // 8							,				'big'       ).decode(_UpperCamelCase							,				_UpperCamelCase       ) or "\0"


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  Dict       )   ->    int:
     """simple docstring"""




     if size_par + len(_UpperCamelCase       ) <= 2**size_par - (len(_UpperCamelCase       ) - 1):
          raise ValueError('size of parity don\'t match with size of data'       )

     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=[bin(_UpperCamelCase       )[2:] for x in range(1							,				size_par + len(_UpperCamelCase       ) + 1       )]

     # sorted information data for the size of the output data
     _SCREAMING_SNAKE_CASE												=[]
     # data position template + parity
     _SCREAMING_SNAKE_CASE												=[]
     # parity bit counter
     _SCREAMING_SNAKE_CASE												=0
     # counter position of data bits
     _SCREAMING_SNAKE_CASE												=0

     for x in range(1							,				size_par + len(_UpperCamelCase       ) + 1       ):
          # Performs a template of bit positions - who should be given,
          # and who should be parity
          if qtd_bp < size_par:
               if (np.log(_UpperCamelCase       ) / np.log(2       )).is_integer():
                    data_out_gab.append('P'       )
                    _SCREAMING_SNAKE_CASE												=qtd_bp + 1
               else:
                    data_out_gab.append('D'       )
          else:
               data_out_gab.append('D'       )

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_ord.append(data[cont_data]       )
               cont_data += 1
          else:
               data_ord.append(_UpperCamelCase       )

    # Calculates parity
     _SCREAMING_SNAKE_CASE												=0  # parity bit counter
     for bp in range(1							,				size_par + 1       ):
          # Bit counter one for a given parity
          _SCREAMING_SNAKE_CASE												=0
          # counter to control the loop reading
          _SCREAMING_SNAKE_CASE												=0
          for x in data_ord:
               if x is not None:
                    try:
                         _SCREAMING_SNAKE_CASE												=(bin_pos[cont_loop])[-1 * (bp)]
                    except IndexError:
                         _SCREAMING_SNAKE_CASE												='0'
                    if aux == "1" and x == "1":
                         cont_bo += 1
               cont_loop += 1
          parity.append(cont_bo % 2       )

          qtd_bp += 1

     # Mount the message
     _SCREAMING_SNAKE_CASE												=0  # parity bit counter
     for x in range(0							,				size_par + len(_UpperCamelCase       )       ):
          if data_ord[x] is None:
               data_out.append(str(parity[cont_bp]       )       )
               cont_bp += 1
          else:
               data_out.append(data_ord[x]       )

     return data_out


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  int       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=[]
     # Parity bit counter
     _SCREAMING_SNAKE_CASE												=0
     # Counter p data bit reading
     _SCREAMING_SNAKE_CASE												=0
     # list of parity received
     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=[]

     for x in range(1							,				len(_UpperCamelCase       ) + 1       ):
          # Performs a template of bit positions - who should be given,
          #  and who should be parity
          if qtd_bp < size_par and (np.log(_UpperCamelCase       ) / np.log(2       )).is_integer():
               data_out_gab.append('P'       )
               _SCREAMING_SNAKE_CASE												=qtd_bp + 1
          else:
               data_out_gab.append('D'       )

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_output.append(data[cont_data]       )
          else:
               parity_received.append(data[cont_data]       )
          cont_data += 1

     # -----------calculates the parity with the data
     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=[]
     _SCREAMING_SNAKE_CASE												=[bin(_UpperCamelCase       )[2:] for x in range(1							,				size_par + len(_UpperCamelCase       ) + 1       )]

     #  sorted information data for the size of the output data
     _SCREAMING_SNAKE_CASE												=[]
     # Data position feedback + parity
     _SCREAMING_SNAKE_CASE												=[]
     # Parity bit counter
     _SCREAMING_SNAKE_CASE												=0
     # Counter p data bit reading
     _SCREAMING_SNAKE_CASE												=0

     for x in range(1							,				size_par + len(_UpperCamelCase       ) + 1       ):
          # Performs a template position of bits - who should be given,
          # and who should be parity
          if qtd_bp < size_par and (np.log(_UpperCamelCase       ) / np.log(2       )).is_integer():
               data_out_gab.append('P'       )
               _SCREAMING_SNAKE_CASE												=qtd_bp + 1
          else:
               data_out_gab.append('D'       )

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_ord.append(data_output[cont_data]       )
               cont_data += 1
          else:
               data_ord.append(_UpperCamelCase       )

    # Calculates parity
     _SCREAMING_SNAKE_CASE												=0  # parity bit counter
     for bp in range(1							,				size_par + 1       ):
          # Bit counter one for a certain parity
          _SCREAMING_SNAKE_CASE												=0
          # Counter to control loop reading
          _SCREAMING_SNAKE_CASE												=0
          for x in data_ord:
               if x is not None:
                    try:
                         _SCREAMING_SNAKE_CASE												=(bin_pos[cont_loop])[-1 * (bp)]
                    except IndexError:
                         _SCREAMING_SNAKE_CASE												='0'
                    if aux == "1" and x == "1":
                         cont_bo += 1
               cont_loop += 1
          parity.append(str(cont_bo % 2       )       )

          qtd_bp += 1

     # Mount the message
     _SCREAMING_SNAKE_CASE												=0  # Parity bit counter
     for x in range(0							,				size_par + len(_UpperCamelCase       )       ):
          if data_ord[x] is None:
               data_out.append(str(parity[cont_bp]       )       )
               cont_bp += 1
          else:
               data_out.append(data_ord[x]       )

     _SCREAMING_SNAKE_CASE												=parity_received == parity
     return data_output, ack



'''simple docstring'''
import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     lowerCamelCase				:   Optional[Any]      				=  False


if is_vision_available():
     from PIL import Image
else:







     class 							A__					:





       @staticmethod
       def 		A						(	*_a   :			Any							,      **_a   :			str				)			->					Optional[int]:





            '''simple docstring'''






            pass







@is_pipeline_test
@require_vision
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_VISION_2_SEQ_MAPPING
  A__					=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING





  def 		A						(	self   :			List[str]							,      _a   :			List[Any]							,      _a   :			Any							,      _a   :			Dict				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model=_a							,      tokenizer=_a							,      image_processor=_a				)
       _SCREAMING_SNAKE_CASE												=[
           Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				),
           './tests/fixtures/tests_samples/COCO/000000039769.png',
       ]
       return pipe, examples





  def 		A						(	self   :			str							,      _a   :			int							,      _a   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               [{'generated_text': ANY(_a				)}],
               [{'generated_text': ANY(_a				)}],
           ]							,      )





  @require_tf
  def 		A						(	self   :			Any				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-vit-gpt2'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      max_new_tokens=1				)
       self.assertEqual(
           _a							,      [{'generated_text': 'growth'}]							,      )





  @require_torch
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-vit-gpt2'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )





  @require_torch
  def 		A						(	self   :			Union[str, Any]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'
       _SCREAMING_SNAKE_CASE												='a photo of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertTrue(outputs[0]['generated_text'].startswith(_a				)				)





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='ydshieh/vit-gpt2-coco-en'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
               [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
           ]							,      )





  @slow
  @require_torch
  def 		A						(	self   :			List[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]				)





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='microsoft/git-base-coco'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cartoon of a purple character.'}]				)





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='a photography of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a photography of a volcano'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='microsoft/git-base-coco'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='a photo of a'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11							,      reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'				)
  @slow
  @require_torch
  def 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='google/pix2struct-ai2d-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'ash cloud'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_tf
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='ydshieh/vit-gpt2-coco-en'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
               [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
           ]							,      )
