

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   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	inspect.getfile(accelerate.test_utils)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "test_script.py"])
									SCREAMING_SNAKE_CASE_:			List[str]  		=	os.path.sep.join(
									    mod_file.split(os.path.sep)[:-1] + ["scripts", "test_distributed_data_loop.py"])
									SCREAMING_SNAKE_CASE_:			int  		=	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["scripts", "test_ops.py"])



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



			@require_multi_gpu
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									print(F"Found {torch.cuda.device_count()} devices.")
									SCREAMING_SNAKE_CASE_:			str  		=	["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(lowerCAmelCase__       ,						env=os.environ.copy())



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




			@require_multi_gpu
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									print(F"Found {torch.cuda.device_count()} devices, using 2 devices only")
									SCREAMING_SNAKE_CASE_:			str  		=	["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(lowerCAmelCase__       ,						env=os.environ.copy())


if __name__ == "__main__":
						lowerCAmelCase							:  int            =						Accelerator()
						lowerCAmelCase							:  Dict            =						(accelerator.state.process_index + 2, 10)
						lowerCAmelCase							:  int            =						torch.randint(0, 10, shape).to(accelerator.device)

						lowerCAmelCase							:  Any            =						""""""

						lowerCAmelCase							:  List[str]            =						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)."

						lowerCAmelCase							:  Union[str, 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."
						lowerCAmelCase							:  int            =						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)









import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM



@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			int  		=	TextStreamer(lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(greedy_ids[0])

									SCREAMING_SNAKE_CASE_:			int  		=	TextIteratorStreamer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	""
									for new_text in streamer:
															streamer_text += new_text

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	greedy_ids[:, input_ids.shape[1] :]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.decode(new_greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Dict  		=	TextStreamer(lowerCAmelCase__       ,						skip_prompt=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Any  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
									# with actual models -- the dummy models' tokenizers are not aligned with their models, and
									# `skip_special_tokens=True` has no effect on them
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained("distilgpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, 5)       ,						device=lowerCAmelCase__).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TextStreamer(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=1       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									SCREAMING_SNAKE_CASE_:			str  		=	cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="pt")
									self.assertEqual(streamer_text_tokenized.input_ids.shape       ,						(1, 1))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	TextIteratorStreamer(lowerCAmelCase__       ,						timeout=0.001)
									SCREAMING_SNAKE_CASE_:			Any  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	""
															for new_text in streamer:
																					streamer_text += new_text






