
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


lowercase__	:int										=					"\\n\n"

lowercase__	:Optional[int]										=					"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

lowercase__	:List[str]										=					"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,						_KWARGS_DESCRIPTION	)
class        lowercase	(      datasets.Metric	):




							def 				A__   (       self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,citation=_CITATION     ,inputs_description=_KWARGS_DESCRIPTION     ,features=datasets.Features(
												        {
												            '''input_texts''': datasets.Value('''string'''),
												        })     ,reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']     ,)





							def 				A__   (       self     ,A__     ,A__     ,A__ = 1_6     ,A__ = True     ,A__=None):
												if device is not None:
																	assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
																	if device == "gpu":
																						lowercase					=		'''cuda'''
												else:
																	lowercase					=		'''cuda''' if torch.cuda.is_available() else '''cpu'''

												lowercase					=		AutoModelForCausalLM.from_pretrained(A__)
												lowercase					=		model.to(A__)

												lowercase					=		AutoTokenizer.from_pretrained(A__)

												# if batch_size > 1 (which generally leads to padding being required), and
												# if there is not an already assigned pad_token, assign an existing
												# special token to also be the padding token
												if tokenizer.pad_token is None and batch_size > 1:
																	lowercase					=		list(tokenizer.special_tokens_map_extended.values())
																	# check that the model already has at least one special token defined
																	assert (
																	    len(A__) > 0
																	), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
																	# assign one of the special tokens to also be the pad token
																	tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]})

												if add_start_token:
																	# leave room for <BOS> token to be added:
																	assert (
																	    tokenizer.bos_token is not None
																	), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
																	lowercase					=		model.config.max_length - 1
												else:
																	lowercase					=		model.config.max_length

												lowercase					=		tokenizer(
												    A__     ,add_special_tokens=A__     ,padding=A__     ,truncation=A__     ,max_length=A__     ,return_tensors='''pt'''     ,return_attention_mask=A__     ,).to(A__)

												lowercase					=		encodings['''input_ids''']
												lowercase					=		encodings['''attention_mask''']

												# check that each input is long enough:
												if add_start_token:
																	assert torch.all(torch.ge(attn_masks.sum(1)     ,1)), "Each input text must be at least one token long."
												else:
																	assert torch.all(
																	    torch.ge(attn_masks.sum(1)     ,2)), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

												lowercase					=		[]
												lowercase					=		CrossEntropyLoss(reduction='''none''')

												for start_index in logging.tqdm(range(0     ,len(A__)     ,A__)):
																	lowercase					=		min(start_index + batch_size     ,len(A__))
																	lowercase					=		encoded_texts[start_index:end_index]
																	lowercase					=		attn_masks[start_index:end_index]

																	if add_start_token:
																						lowercase					=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(A__)
																						lowercase					=		torch.cat([bos_tokens_tensor, encoded_batch]     ,dim=1)
																						lowercase					=		torch.cat(
																						    [torch.ones(bos_tokens_tensor.size()     ,dtype=torch.intaa).to(A__), attn_mask]     ,dim=1)

																	lowercase					=		encoded_batch

																	with torch.no_grad():
																						lowercase					=		model(A__     ,attention_mask=A__).logits

																	lowercase					=		out_logits[..., :-1, :].contiguous()
																	lowercase					=		labels[..., 1:].contiguous()
																	lowercase					=		attn_mask[..., 1:].contiguous()

																	lowercase					=		torch.expa(
																	    (loss_fct(shift_logits.transpose(1     ,2)     ,A__) * shift_attention_mask_batch).sum(1)
																	    / shift_attention_mask_batch.sum(1))

																	ppls += perplexity_batch.tolist()

												return {"perplexities": ppls, "mean_perplexity": np.mean(A__)}






import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					try:
										lowercase					=		os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										lowercase					=		default
					else:
										# KEY is set, convert it to True or False.
										try:
															lowercase					=		strtobool(lowerCAmelCase__	)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(f'If set, {key} must be yes or no.'	)
					return _value


lowercase__	:Dict										=					parse_flag_from_env("RUN_SLOW", default=False)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skip('''Test was skipped'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(_run_slow_tests				,    '''test is slow'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(not torch.cuda.is_available()				,    '''test requires only a CPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.is_available()				,    '''test requires a GPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_xpu_available()				,    '''test requires a XPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_mps_available()				,    '''test requires a `mps` backend support in `torch`'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()				,    '''test requires the Hugging Face suite'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_bnb_available()				,    '''test requires the bitsandbytes library'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_tpu_available()				,    '''test requires TPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() == 1				,    '''test requires a GPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() == 1				,    '''test requires a XPU'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() > 1				,    '''test requires multiple GPUs'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() > 1				,    '''test requires multiple XPUs'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_safetensors_available()				,    '''test requires safetensors'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_deepspeed_available()				,    '''test requires DeepSpeed'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_torch_version('''>='''				,    '''1.12.0'''	)				,    '''test requires torch version >= 1.12.0'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__=None				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if test_case is None:
										return partial(lowerCAmelCase__				,    version=lowerCAmelCase__	)
					return unittest.skipUnless(is_torch_version('''>='''				,    lowerCAmelCase__	)				,    f'test requires torch version >= {version}'	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_tensorboard_available()				,    '''test requires Tensorboard'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_wandb_available()				,    '''test requires wandb'''	)(lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(is_comet_ml_available()				,    '''test requires comet_ml'''	)(lowerCAmelCase__	)


lowercase__	:Dict										=					(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return unittest.skipUnless(
					    _atleast_one_tracker_available				,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''				,    )(lowerCAmelCase__	)







class        lowercase	(      unittest.TestCase	):
							lowercase_ : int       =True




							@classmethod
							def 				A__   (       cls):
												lowercase					=		tempfile.mkdtemp()




							@classmethod
							def 				A__   (       cls):
												if os.path.exists(cls.tmpdir):
																	shutil.rmtree(cls.tmpdir)





							def 				A__   (       self):
												if self.clear_on_setup:
																	for path in Path(self.tmpdir).glob('''**/*'''):
																						if path.is_file():
																											path.unlink()
																						elif path.is_dir():
																											shutil.rmtree(A__)







class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												super().tearDown()
												# Reset the state of the AcceleratorState singleton.
												AcceleratorState._reset_state()
												PartialState._reset_state()







class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self     ,A__):
												lowercase					=		mocks if isinstance(A__     ,(tuple, list)) else [mocks]
												for m in self.mocks:
																	m.start()
																	self.addCleanup(m.stop)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		AcceleratorState()
					lowercase					=		tensor[None].clone().to(state.device	)
					lowercase					=		gather(lowerCAmelCase__	).cpu()
					lowercase					=		tensor[0].cpu()
					for i in range(tensors.shape[0]	):
										if not torch.equal(tensors[i]				,    lowerCAmelCase__	):
															return False
					return True







class        lowercase	:




							def __init__(       self     ,A__     ,A__     ,A__):
												lowercase					=		returncode
												lowercase					=		stdout
												lowercase					=		stderr



async def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					while True:
										lowercase					=		await stream.readline()
										if line:
															callback(lowerCAmelCase__	)
										else:
															break



async def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=False				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					if echo:
										print('''\nRunning: '''				,    ''' '''.join(lowerCAmelCase__	)	)

					lowercase					=		await asyncio.create_subprocess_exec(
					    cmd[0]				,    *cmd[1:]				,    stdin=lowerCAmelCase__				,    stdout=asyncio.subprocess.PIPE				,    stderr=asyncio.subprocess.PIPE				,    env=lowerCAmelCase__				,    )

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					lowercase					=		[]
					lowercase					=		[]

					def tee(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=""	):
										lowercase					=		line.decode('''utf-8'''	).rstrip()
										sink.append(lowerCAmelCase__	)
										if not quiet:
															print(lowerCAmelCase__				,    lowerCAmelCase__				,    file=lowerCAmelCase__	)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout				,    lambda lowerCAmelCase__	: tee(lowerCAmelCase__				,    lowerCAmelCase__				,    sys.stdout				,    label='''stdout:'''	)	)	),
					        asyncio.create_task(_read_stream(p.stderr				,    lambda lowerCAmelCase__	: tee(lowerCAmelCase__				,    lowerCAmelCase__				,    sys.stderr				,    label='''stderr:'''	)	)	),
					    ]				,    timeout=lowerCAmelCase__				,    )
					return _RunOutput(await p.wait()				,    lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=180				,    lowerCAmelCase__=False				,    lowerCAmelCase__=True	):
					'''simple docstring'''
					lowercase					=		asyncio.get_event_loop()
					lowercase					=		loop.run_until_complete(
					    _stream_subprocess(lowerCAmelCase__				,    env=lowerCAmelCase__				,    stdin=lowerCAmelCase__				,    timeout=lowerCAmelCase__				,    quiet=lowerCAmelCase__				,    echo=lowerCAmelCase__	)	)

					lowercase					=		''' '''.join(lowerCAmelCase__	)
					if result.returncode > 0:
										lowercase					=		'''\n'''.join(result.stderr	)
										raise RuntimeError(
										    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
										    f'The combined stderr from workers follows:\n{stderr}'	)

					return result







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							pass



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					try:
										lowercase					=		subprocess.check_output(lowerCAmelCase__				,    stderr=subprocess.STDOUT	)
										if return_stdout:
															if hasattr(lowerCAmelCase__				,    '''decode'''	):
																				lowercase					=		output.decode('''utf-8'''	)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    f'Command `{" ".join(lowerCAmelCase__	)}` failed with the following error:\n\n{e.output.decode()}'	) from e




