






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Optional[Any]     							=       tempfile.mkdtemp()

          UpperCamelCase	:Tuple     							=       CLIPImageProcessor()
          UpperCamelCase	:str     							=       BertTokenizer.from_pretrained(
              '''hf-internal-testing/tiny-random-BertModel'''   ,    model_input_names=['''input_ids''', '''attention_mask''']			)

          UpperCamelCase	:int     							=       GitProcessor(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          processor.save_pretrained(self.tmpdirname			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				int:
          return AutoProcessor.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			).tokenizer
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			).image_processor
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          shutil.rmtree(self.tmpdirname			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:List[Any]     							=       [np.random.randint(255   ,    size=(3, 30, 400)   ,    dtype=np.uinta			)]

          UpperCamelCase	:int     							=       [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_   ,    0   ,    -1			)			) for x in image_inputs]

          return image_inputs
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	:Any     							=       GitProcessor(tokenizer=self.get_tokenizer()   ,    image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase	:int     							=       self.get_tokenizer(bos_token='''(BOS)'''   ,    eos_token='''(EOS)'''			)
          UpperCamelCase	:int     							=       self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_   ,    padding_value=1.0			)

          UpperCamelCase	:Optional[int]     							=       GitProcessor.from_pretrained(
              self.tmpdirname   ,    bos_token='''(BOS)'''   ,    eos_token='''(EOS)'''   ,    do_normalize=SCREAMING_SNAKE_CASE_   ,    padding_value=1.0			)

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

          self.assertEqual(processor.image_processor.to_json_string()   ,    image_processor_add_kwargs.to_json_string()			)
          self.assertIsInstance(processor.image_processor   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Union[str, Any]     							=       self.get_image_processor()
          UpperCamelCase	:Any     							=       self.get_tokenizer()

          UpperCamelCase	:Any     							=       GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Optional[int]     							=       self.prepare_image_inputs()

          UpperCamelCase	:Any     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''np'''			)
          UpperCamelCase	:Optional[int]     							=       processor(images=SCREAMING_SNAKE_CASE_   ,    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			)    ->				Any:
          UpperCamelCase	:Dict     							=       self.get_image_processor()
          UpperCamelCase	:Optional[Any]     							=       self.get_tokenizer()

          UpperCamelCase	:Optional[int]     							=       GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:List[str]     							=       '''lower newer'''

          UpperCamelCase	:List[str]     							=       processor(text=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Union[str, Any]     							=       tokenizer(SCREAMING_SNAKE_CASE_   ,    return_token_type_ids=SCREAMING_SNAKE_CASE_			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]   ,    encoded_processor[key]			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          UpperCamelCase	:int     							=       self.get_image_processor()
          UpperCamelCase	:Dict     							=       self.get_tokenizer()

          UpperCamelCase	:List[Any]     							=       GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:List[str]     							=       processor(text=SCREAMING_SNAKE_CASE_   ,    images=SCREAMING_SNAKE_CASE_			)

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

          # test if it raises when no input is passed
          with pytest.raises(SCREAMING_SNAKE_CASE_			):
              processor()
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:Optional[Any]     							=       self.get_image_processor()
          UpperCamelCase	:Optional[Any]     							=       self.get_tokenizer()

          UpperCamelCase	:List[str]     							=       GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:Tuple     							=       processor.batch_decode(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

          self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)




      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:Optional[Any]     							=       self.get_image_processor()
          UpperCamelCase	:Optional[int]     							=       self.get_tokenizer()

          UpperCamelCase	:Union[str, Any]     							=       GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:str     							=       '''lower newer'''
          UpperCamelCase	:Tuple     							=       self.prepare_image_inputs()

          UpperCamelCase	:Tuple     							=       processor(text=SCREAMING_SNAKE_CASE_   ,    images=SCREAMING_SNAKE_CASE_			)

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys()			)   ,    ['''input_ids''', '''attention_mask''', '''pixel_values''']			)













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





