


"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						int    )   ->      float:
      lowerCamelCase					:					int        =	x
      lowerCamelCase					:					Tuple        =	y
      for step in range(UpperCamelCase__    ):  # noqa: B007
            lowerCamelCase					:					Dict        =	a * a - b * b + x
            lowerCamelCase					:					List[str]        =	2 * a * b + y
            lowerCamelCase					:					Union[str, Any]        =	a_new

            # divergence happens for all complex number with an absolute value
            # greater than 4
            if a * a + b * b > 4:
                  break
      return step / (max_step - 1)




def   snake_case		(     UpperCamelCase__       :						float    )   ->      tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return (255, 255, 255)




def   snake_case		(     UpperCamelCase__       :						float    )   ->      tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 255    ) for i in colorsys.hsv_to_rgb(UpperCamelCase__			,				1			,				1    )    )




def   snake_case		(     UpperCamelCase__       :						int = 800			,				UpperCamelCase__       :						int = 600			,				UpperCamelCase__       :						float = -0.6			,				UpperCamelCase__       :						float = 0			,				UpperCamelCase__       :						float = 3.2			,				UpperCamelCase__       :						int = 50			,				UpperCamelCase__       :						bool = True			,				)   ->      Image.Image:
      lowerCamelCase					:					str        =	Image.new("""RGB"""			,				(image_width, image_height)    )
      lowerCamelCase					:					Optional[int]        =	img.load()

      # loop through the image-coordinates
      for image_x in range(UpperCamelCase__    ):
            for image_y in range(UpperCamelCase__    ):
                  # determine the figure-coordinates based on the image-coordinates
                  lowerCamelCase					:					List[Any]        =	figure_width / image_width * image_height
                  lowerCamelCase					:					List[Any]        =	figure_center_x + (image_x / image_width - 0.5) * figure_width
                  lowerCamelCase					:					Union[str, Any]        =	figure_center_y + (image_y / image_height - 0.5) * figure_height

                  lowerCamelCase					:					Tuple        =	get_distance(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        lowerCamelCase					:					List[str]        =	get_color_coded_rgb(UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Union[str, Any]        =	get_black_and_white_rgb(UpperCamelCase__    )

      return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    __lowerCamelCase      :Optional[Any]	    =		get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple     ,		__a:						Optional[int]	)->    List[str]:
           return None


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						str     ,		__a:						str     ,		__a:						str	)->    Tuple:
           return None


class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=[
         # (model_name, model_kwargs)
         ('''bert-base-cased''', {}),
         ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
     ]

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						str	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
           with NamedTemporaryFile(mode="""w+t"""	) as vocab_file:
                 vocab_file.write("""\n""".join(__a	)	)
                 vocab_file.flush()
                 lowerCamelCase					:					Dict        =	BertTokenizerFast(vocab_file.name	)

           with TemporaryDirectory() as bert_save_dir:
                 lowerCamelCase					:					List[str]        =	BertModel(BertConfig(vocab_size=len(__a	)	)	)
                 model.save_pretrained(__a	)
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		__a	)

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Optional[int]        =	self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Tuple        =	quantize(Path(__a	)	)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(__a	).stat().st_size:
                       self.fail("""Quantized model is bigger than initial ONNX model"""	)

     @require_torch
     @slow
     def 		a__						(	self:						Any	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Any        =	self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Dict        =	quantize(__a	)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(__a	).stat().st_size:
                       self.fail("""Quantized model is bigger than initial ONNX model"""	)

     def 		a__						(	self:						List[Any]     ,		__a:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[int]	)->    Any:
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCamelCase					:					Optional[Any]        =	Path(__a	).joinpath("""model.onnx"""	)

                 # Remove folder if exists
                 if path.parent.exists():
                       path.parent.rmdir()

                 # Export
                 convert(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		**__a	)

                 return path
           except Exception as e:
                 self.fail(__a	)

     @require_torch
     @require_tokenizers
     @slow
     def 		a__						(	self:						Tuple	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					List[Any]        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""pt"""	)

     @require_tf
     @require_tokenizers
     @slow
     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           from transformers import TFBertModel

           lowerCamelCase					:					Union[str, Any]        =	TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					str        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""tf"""	)

     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						str	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	FeatureExtractionPipeline(__a     ,		__a	)

           lowerCamelCase					:					List[str]        =	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	infer_shapes(__a     ,		__a	)

           # Assert all variables are present
           self.assertEqual(len(__a	)     ,		len(__a	)	)
           self.assertTrue(all(var_name in shapes for var_name in variable_names	)	)
           self.assertSequenceEqual(variable_names[:3]     ,		__a	)
           self.assertSequenceEqual(variable_names[3:]     ,		__a	)

           # Assert inputs are {0: batch, 1: sequence}
           for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
                 self.assertDictEqual(shapes[var_name]     ,		{0: """batch""", 1: """sequence"""}	)

           # Assert outputs are {0: batch, 1: sequence} and {0: batch}
           self.assertDictEqual(shapes["""output_0"""]     ,		{0: """batch""", 1: """sequence"""}	)
           self.assertDictEqual(shapes["""output_1"""]     ,		{0: """batch"""}	)

     def 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowerCamelCase					:					str        =	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncContiguousArgs()     ,		__a     ,		__a	)

           # Should have exactly the same number of args (all are valid)
           self.assertEqual(len(__a	)     ,		3	)

           # Should have exactly the same input names
           self.assertEqual(set(__a	)     ,		set(__a	)	)

           # Parameter should be reordered according to their respective place in the function:
           # (input_ids, token_type_ids, attention_mask)
           self.assertEqual(__a     ,		(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])	)

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncNonContiguousArgs()     ,		__a     ,		__a	)

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(__a	)     ,		1	)
           self.assertEqual(len(__a	)     ,		1	)

           # Should have only "input_ids"
           self.assertEqual(inputs_args[0]     ,		tokens["""input_ids"""]	)
           self.assertEqual(ordered_input_names[0]     ,		"""input_ids"""	)







     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""	)     ,		"""-test"""	)
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()	)




