

"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


