





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM






@require_torch
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
       UpperCamelCase__							:				int          =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      -1

       UpperCamelCase__							:				int          =      ids_tensor((1, 5),		vocab_size=model.config.vocab_size							).to(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      model.generate(__magic_name__,		max_new_tokens=10,		do_sample=__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      tokenizer.decode(greedy_ids[0]							)

       with CaptureStdout() as cs:
           UpperCamelCase__							:				Union[str, Any]          =      TextStreamer(__magic_name__							)
           model.generate(__magic_name__,		max_new_tokens=10,		do_sample=__magic_name__,		streamer=__magic_name__							)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__							:				List[str]          =      cs.out[:-1]

       self.assertEqual(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
       UpperCamelCase__							:				Optional[Any]          =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      -1

       UpperCamelCase__							:				int          =      ids_tensor((1, 5),		vocab_size=model.config.vocab_size							).to(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      model.generate(__magic_name__,		max_new_tokens=10,		do_sample=__magic_name__							)
       UpperCamelCase__							:				List[str]          =      tokenizer.decode(greedy_ids[0]							)

       UpperCamelCase__							:				List[Any]          =      TextIteratorStreamer(__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
       UpperCamelCase__							:				Union[str, Any]          =      Thread(target=model.generate,		kwargs=__magic_name__							)
       thread.start()
       UpperCamelCase__							:				Any          =      ''''''
       for new_text in streamer:
           streamer_text += new_text

       self.assertEqual(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
       UpperCamelCase__							:				Optional[Any]          =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      -1

       UpperCamelCase__							:				List[Any]          =      ids_tensor((1, 5),		vocab_size=model.config.vocab_size							).to(__magic_name__							)
       UpperCamelCase__							:				str          =      model.generate(__magic_name__,		max_new_tokens=10,		do_sample=__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__							:				int          =      tokenizer.decode(new_greedy_ids[0]							)

       with CaptureStdout() as cs:
           UpperCamelCase__							:				List[Any]          =      TextStreamer(__magic_name__,		skip_prompt=__magic_name__							)
           model.generate(__magic_name__,		max_new_tokens=10,		do_sample=__magic_name__,		streamer=__magic_name__							)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__							:				List[str]          =      cs.out[:-1]

       self.assertEqual(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__							:				Any          =      AutoTokenizer.from_pretrained('''distilgpt2'''							)
       UpperCamelCase__							:				Tuple          =      AutoModelForCausalLM.from_pretrained('''distilgpt2'''							).to(__magic_name__							)
       UpperCamelCase__							:				Dict          =      -1

       UpperCamelCase__							:				int          =      torch.ones((1, 5),		device=__magic_name__							).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
           UpperCamelCase__							:				List[Any]          =      TextStreamer(__magic_name__,		skip_special_tokens=__magic_name__							)
           model.generate(__magic_name__,		max_new_tokens=1,		do_sample=__magic_name__,		streamer=__magic_name__							)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__							:				Any          =      cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__							:				List[Any]          =      tokenizer(__magic_name__,		return_tensors='''pt'''							)
       self.assertEqual(streamer_text_tokenized.input_ids.shape,		(1, 1)							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
       UpperCamelCase__							:				Any          =      AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(__magic_name__							)
       UpperCamelCase__							:				Dict          =      -1

       UpperCamelCase__							:				List[str]          =      ids_tensor((1, 5),		vocab_size=model.config.vocab_size							).to(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      TextIteratorStreamer(__magic_name__,		timeout=0.001							)
       UpperCamelCase__							:				Dict          =      {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
       UpperCamelCase__							:				Optional[Any]          =      Thread(target=model.generate,		kwargs=__magic_name__							)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(__magic_name__							):
           UpperCamelCase__							:				Dict          =      ''''''
           for new_text in streamer:
               streamer_text += new_text








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			complex		,   __UpperCAmelCase:			str = "x"		,   __UpperCAmelCase:			float = 10**-10		,   __UpperCAmelCase:			int = 1		,   )      ->	complex:
    UpperCamelCase__							:				Optional[int]          =      symbols(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      lambdify(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      lambdify(__UpperCAmelCase		,   diff(__UpperCAmelCase		,   __UpperCAmelCase      )      )

    UpperCamelCase__							:				int          =      starting_point

    while True:
        if diff_function(__UpperCAmelCase      ) != 0:
            UpperCamelCase__							:				Optional[int]          =      prev_guess - multiplicity * func(__UpperCAmelCase      ) / diff_function(
                __UpperCAmelCase      )
        else:
            raise ZeroDivisionError('''Could not find root'''      ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess      ) < precision:
            return next_guess

        UpperCamelCase__							:				Tuple          =      next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

  # Find value of e
  print(
      'The root of log(y) - 1 = 0 is ',
      F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
  )

  # Exponential Roots
  print(
      'The root of exp(x) - 1 = 0 is',
      F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

