


'''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,
)


__UpperCAmelCase       =							{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

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

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

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


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


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       =							[
		    '''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

		__UpperCAmelCase       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



'''simple docstring'''






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def  _snake_case       (					A			,   A			,   A			,   A			,   )						->   list[float]:
							lowerCAmelCase__       , lowerCAmelCase__	       =     coefficient_matrix.shape
							lowerCAmelCase__       , lowerCAmelCase__	       =     constant_matrix.shape

							if rowsa != colsa:
														lowerCAmelCase__	       =     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if colsa != 1:
														lowerCAmelCase__	       =     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if rowsa != rowsa:
														lowerCAmelCase__	       =     (
														    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
														    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(A	)

							if len(A	) != rowsa:
														lowerCAmelCase__	       =     (
														    '''Number of initial values must be equal to number of rows in coefficient '''
														    F"""matrix but received {len(A	)} and {rowsa}"""
														)
														raise ValueError(A	)

							if iterations <= 0:
														raise ValueError('''Iterations must be at least 1'''	)

							lowerCAmelCase__	       =     np.concatenate(
							    (coefficient_matrix, constant_matrix)			,   axis=1	)

							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							strictly_diagonally_dominant(A	)

							# Iterates the whole matrix for given number of times
							for _ in range(A	):
														lowerCAmelCase__	       =     []
														for row in range(A	):
																					lowerCAmelCase__	       =     0
																					for col in range(A	):
																												if col == row:
																																			lowerCAmelCase__	       =     table[row][col]
																												elif col == cols - 1:
																																			lowerCAmelCase__	       =     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowerCAmelCase__	       =     (temp + val) / denom
																					new_val.append(A	)
														lowerCAmelCase__	       =     new_val

							return [float(A	) for i in new_val]


def  _snake_case       (					A	)						->   bool:
							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							lowerCAmelCase__	       =     True

							for i in range(0			,   A	):
														lowerCAmelCase__	       =     0
														for j in range(0			,   cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()