






def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		# "extended trapezoidal rule"
		# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
		snake_case_				:							Optional[Any]						 =       (boundary[1] - boundary[0]) / steps
		snake_case_				:							str						 =       boundary[0]
		snake_case_				:							Dict						 =       boundary[1]
		snake_case_				:							str						 =       make_points(_a  ,      _a  ,      _a    )
		snake_case_				:							str						 =       0.0
		y += (h / 2.0) * f(_a    )
		for i in x_i:
				# print(i)
				y += h * f(_a    )
		y += (h / 2.0) * f(_a    )
		return y

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[Any]  ,      _a       :			Tuple    ):
		snake_case_				:							Tuple						 =       a + h
		while x < (b - h):
				yield x
				snake_case_				:							List[str]						 =       x + h

def      lowerCAmelCase__	(    _a       :			Tuple    ):  # enter your function here
		snake_case_				:							Tuple						 =       (x - 0) * (x - 0)
		return y

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       0.0  # Lower bound of integration
		snake_case_				:							List[Any]						 =       1.0  # Upper bound of integration
		snake_case_				:							Tuple						 =       10.0  # define number of steps or resolution
		snake_case_				:							List[Any]						 =       [a, b]  # define boundary of integration
		snake_case_				:							Any						 =       method_a(_a  ,      _a    )
		print(F'''y = {y}'''    )


if __name__ == "__main__":
							main()








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,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
