





import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch


class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     @patch("sys.argv"			,      ["fakeprogrampath", "env"]  )
     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        import transformers.commands.transformers_cli

        with CaptureStd() as cs:
           transformers.commands.transformers_cli.main()
        self.assertIn("Python version"			,      cs.out  )
        self.assertIn("Platform"			,      cs.out  )
        self.assertIn("Using distributed or parallel set-up in script?"			,      cs.out  )



     @is_pt_tf_cross_test
     @patch(
         "sys.argv"			,      ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]  )
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        import transformers.commands.transformers_cli

        shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"			,      ignore_errors=snake_case  )  # cleans potential past runs
        transformers.commands.transformers_cli.main()

        # The original repo has no TF weights -- if they exist, they were created by the CLI
        self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"  )  )



     @require_torch
     @patch("sys.argv"			,      ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]  )
     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        import transformers.commands.transformers_cli

        # # remove any previously downloaded model to start clean
        shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"			,      ignore_errors=snake_case  )

        # run the command
        transformers.commands.transformers_cli.main()

        # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
        self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"  )  )
        self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"  )  )
        self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"  )  )





     @require_torch
     @patch(
         "sys.argv"			,      [
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]			,      )
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        import transformers.commands.transformers_cli

        # # remove any previously downloaded model to start clean
        shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"			,      ignore_errors=snake_case  )

        # run the command
        transformers.commands.transformers_cli.main()

        # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
        self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"  )  )
        self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"  )  )
        self.assertTrue(
            os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"  )  )







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

import numpy
from matplotlib import pyplot

_lowerCAmelCase							:      Union[str, Any]						=	0.25 + 0.0j
_lowerCAmelCase							:      str						=	-0.4 + 0.6j
_lowerCAmelCase							:      Optional[int]						=	-0.1 + 0.6_5_1j
_lowerCAmelCase							:      Union[str, Any]						=	-2.0
_lowerCAmelCase							:      str						=	56
_lowerCAmelCase							:      Optional[Any]						=	2.0
_lowerCAmelCase							:      Dict						=	666

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return numpy.exp(_lowerCAmelCase	) + c_parameter

def      __snake_case						(					_lowerCAmelCase : complex				,				_lowerCAmelCase : numpy.ndarray	) ->						numpy.ndarray:
   return z_values * z_values + c_parameter

def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						numpy.ndarray:
   A_					:				Tuple          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Optional[int]          =			x.reshape((nb_pixels, 1)	)
   A_					:				str          =			numpy.linspace(-window_size				,				_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Tuple          =			y.reshape((1, nb_pixels)	)
   return x + 1.0j * y

def      __snake_case						(					_lowerCAmelCase : Callable[[Any, numpy.ndarray], numpy.ndarray]				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : int				,				_lowerCAmelCase : numpy.ndarray				,				_lowerCAmelCase : float | None = None				,				) ->						numpy.ndarray:
   A_					:				List[Any]          =			z_a.astype("complex64"	)
   for _ in range(_lowerCAmelCase	):
      A_					:				List[Any]          =			eval_function(_lowerCAmelCase				,				_lowerCAmelCase	)
      if infinity is not None:
         numpy.nan_to_num(_lowerCAmelCase				,				copy=_lowerCAmelCase				,				nan=_lowerCAmelCase	)
         A_					:				List[Any]          =			infinity
   return z_n

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : float				,				_lowerCAmelCase : numpy.ndarray				,				) ->						None:
   A_					:				Optional[int]          =			(abs(_lowerCAmelCase	)).transpose()
   A_					:				Optional[int]          =			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      __snake_case						(					) ->						None:
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in multiply"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="invalid value encountered in multiply"				,				)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in absolute"	)
   warnings.filterwarnings(
       "ignore"				,				category=_lowerCAmelCase				,				message="overflow encountered in exp"	)


if __name__ == "__main__":
 _lowerCAmelCase							:      List[str]						=	prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 _lowerCAmelCase							:      str						=	24
 _lowerCAmelCase							:      str						=	2 * abs(c_cauliflower) + 1
 _lowerCAmelCase							:      Optional[Any]						=	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)

 _lowerCAmelCase							:      Optional[int]						=	64
 _lowerCAmelCase							:      List[Any]						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[str]						=	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)

 _lowerCAmelCase							:      Tuple						=	161
 _lowerCAmelCase							:      int						=	2 * abs(c_polynomial_a) + 1
 _lowerCAmelCase							:      List[Any]						=	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)

 _lowerCAmelCase							:      List[str]						=	12
 _lowerCAmelCase							:      str						=	1_0000.0
 _lowerCAmelCase							:      Tuple						=	iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0e10,
 )
 show_results('''e^z+c''', c_exponential, escape_radius, z_final)
