from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer
class SCREAMING_SNAKE_CASE_						:







   def 		lowerCamelCase_     (			self		:		List[str]					,					lowerCamelCase_		:		List[str]							):





    """simple docstring"""


    raise NotImplementedError()







   def 		lowerCamelCase_     (			self		:		Optional[int]							):





    """simple docstring"""


    raise NotImplementedError()





class SCREAMING_SNAKE_CASE_						(	lowercase_						):







   def __init__(			self		:		Optional[Any]					,					lowerCamelCase_		:		"AutoTokenizer"					,					lowerCamelCase_		:		bool = False					,					**lowerCamelCase_		:		Dict							):





    """simple docstring"""


    UpperCamelCase          =	tokenizer
    UpperCamelCase          =	skip_prompt
    UpperCamelCase          =	decode_kwargs

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







   def 		lowerCamelCase_     (			self		:		str					,					lowerCamelCase_		:		List[Any]							):





    """simple docstring"""


    if len(value.shape							) > 1 and value.shape[0] > 1:
     raise ValueError("""TextStreamer only supports batch size 1"""							)
    elif len(value.shape							) > 1:
     UpperCamelCase          =	value[0]

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

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

    # After the symbol for a new line, we flush the cache.
    if text.endswith("""\n"""							):
     UpperCamelCase          =	text[self.print_len :]
     UpperCamelCase          =	[]
     UpperCamelCase          =	0
    # If the last token is a CJK character, we print the characters.
    elif len(SCREAMING_SNAKE_CASE_							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
     UpperCamelCase          =	text[self.print_len :]
     self.print_len += len(SCREAMING_SNAKE_CASE_							)
    # 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:
     UpperCamelCase          =	text[self.print_len : text.rfind(""" """							) + 1]
     self.print_len += len(SCREAMING_SNAKE_CASE_							)

    self.on_finalized_text(SCREAMING_SNAKE_CASE_							)







   def 		lowerCamelCase_     (			self		:		List[Any]							):





    """simple docstring"""


    if len(self.token_cache							) > 0:
     UpperCamelCase          =	self.tokenizer.decode(self.token_cache					,					**self.decode_kwargs							)
     UpperCamelCase          =	text[self.print_len :]
     UpperCamelCase          =	[]
     UpperCamelCase          =	0
    else:
     UpperCamelCase          =	''

    UpperCamelCase          =	True
    self.on_finalized_text(SCREAMING_SNAKE_CASE_					,					stream_end=SCREAMING_SNAKE_CASE_							)







   def 		lowerCamelCase_     (			self		:		Optional[int]					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		bool = False							):





    """simple docstring"""


    print(SCREAMING_SNAKE_CASE_					,					flush=SCREAMING_SNAKE_CASE_					,					end="""""" if not stream_end else None							)







   def 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		str							):





    """simple docstring"""


    if (
        (cp >= 0x4e00 and cp <= 0x9fff)
        or (cp >= 0x3400 and cp <= 0x4dbf)  #
        or (cp >= 0x20000 and cp <= 0x2a6df)  #
        or (cp >= 0x2a700 and cp <= 0x2b73f)  #
        or (cp >= 0x2b740 and cp <= 0x2b81f)  #
        or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
        or (cp >= 0xf900 and cp <= 0xfaff)
        or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
    ):  #
     return True

    return False





class SCREAMING_SNAKE_CASE_						(	lowercase_						):







   def __init__(			self		:		Optional[Any]					,					lowerCamelCase_		:		"AutoTokenizer"					,					lowerCamelCase_		:		bool = False					,					lowerCamelCase_		:		Optional[float] = None					,					**lowerCamelCase_		:		List[str]							):





    """simple docstring"""


    super().__init__(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_							)
    UpperCamelCase          =	Queue()
    UpperCamelCase          =	None
    UpperCamelCase          =	timeout







   def 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		bool = False							):





    """simple docstring"""


    self.text_queue.put(SCREAMING_SNAKE_CASE_					,					timeout=self.timeout							)
    if stream_end:
     self.text_queue.put(self.stop_signal					,					timeout=self.timeout							)







   def __iter__(			self		:		int							):





    """simple docstring"""


    return self







   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	self.text_queue.get(timeout=self.timeout							)
    if value == self.stop_signal:
     raise StopIteration()
    else:
     return value





"""simple docstring"""







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)





