




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer




class     snake_case__							:
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Dict							)					->     List[Any]:
							raise NotImplementedError()





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     List[Any]:
							raise NotImplementedError()




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     "AutoTokenizer"					,      __lowerCamelCase				:     bool = False					,      **__lowerCamelCase				:     str							)					->     Any:
							a    =			tokenizer
							a    =			skip_prompt
							a    =			decode_kwargs

							# variables used in the streaming process
							a    =			[]
							a    =			0
							a    =			True





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str							)					->     List[Any]:
							if len(value.shape							) > 1 and value.shape[0] > 1:
										raise ValueError("TextStreamer only supports batch size 1"							)
							elif len(value.shape							) > 1:
										a    =			value[0]

							if self.skip_prompt and self.next_tokens_are_prompt:
										a    =			False
										return

							# Add the new token to the cache and decodes the entire thing.
							self.token_cache.extend(value.tolist()							)
							a    =			self.tokenizer.decode(self.token_cache					,      **self.decode_kwargs							)

							# After the symbol for a new line, we flush the cache.
							if text.endswith("\n"							):
										a    =			text[self.print_len :]
										a    =			[]
										a    =			0
							# If the last token is a CJK character, we print the characters.
							elif len(__lowerCamelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
										a    =			text[self.print_len :]
										self.print_len += len(__lowerCamelCase							)
							# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
							# which may change with the subsequent token -- there are probably smarter ways to do this!)
							else:
										a    =			text[self.print_len : text.rfind(" "							) + 1]
										self.print_len += len(__lowerCamelCase							)

							self.on_finalized_text(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							# Flush the cache, if it exists
							if len(self.token_cache							) > 0:
										a    =			self.tokenizer.decode(self.token_cache					,      **self.decode_kwargs							)
										a    =			text[self.print_len :]
										a    =			[]
										a    =			0
							else:
										a    =			""

							a    =			True
							self.on_finalized_text(__lowerCamelCase					,      stream_end=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     bool = False							)					->     Dict:
							print(__lowerCamelCase					,      flush=__lowerCamelCase					,      end="" if not stream_end else None							)





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     List[Any]							)					->     Any:
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0X4e_00 and cp <= 0X9f_ff)
							    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
							    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
							    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
							    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
							    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
							    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
							):  #
										return True

							return False




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     "AutoTokenizer"					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     Optional[float] = None					,      **__lowerCamelCase				:     Any							)					->     int:
							super().__init__(__lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase							)
							a    =			Queue()
							a    =			None
							a    =			timeout





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     bool = False							)					->     Tuple:
							self.text_queue.put(__lowerCamelCase					,      timeout=self.timeout							)
							if stream_end:
										self.text_queue.put(self.stop_signal					,      timeout=self.timeout							)





				def __iter__(      self				:     Dict							)					->     str:
							return self





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union[str, Any]:
							a    =			self.text_queue.get(timeout=self.timeout							)
							if value == self.stop_signal:
										raise StopIteration()
							else:
										return value












from __future__ import annotations

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




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

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

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

			if rowsa != rowsa:
						a    =			(
						    "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:
						a    =			(
						    "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"   )

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

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

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

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			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()






