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        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
												UpperCamelCase_		:							int  =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[Any]  =		-1

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

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

												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
												UpperCamelCase_		:							Union[str, Any]  =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		-1

												UpperCamelCase_		:							Optional[Any]  =		ids_tensor((1, 5)			,     vocab_size=model.config.vocab_size						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		model.generate(__UpperCamelCase			,     max_new_tokens=10			,     do_sample=__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		tokenizer.decode(greedy_ids[0]						)

												UpperCamelCase_		:							Dict  =		TextIteratorStreamer(__UpperCamelCase						)
												UpperCamelCase_		:							str  =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
												UpperCamelCase_		:							List[str]  =		Thread(target=model.generate			,     kwargs=__UpperCamelCase						)
												thread.start()
												UpperCamelCase_		:							Optional[Any]  =		""""""
												for new_text in streamer:
																		streamer_text += new_text

												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Tuple  =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
												UpperCamelCase_		:							Optional[int]  =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__UpperCamelCase						)
												UpperCamelCase_		:							List[str]  =		-1

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

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

												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												# 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
												UpperCamelCase_		:							Optional[int]  =		AutoTokenizer.from_pretrained("""distilgpt2"""						)
												UpperCamelCase_		:							Optional[Any]  =		AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[Any]  =		-1

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

												# 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
												UpperCamelCase_		:							Optional[Any]  =		cs.out[:-1]  # Remove the final "\n"
												UpperCamelCase_		:							Tuple  =		tokenizer(__UpperCamelCase			,     return_tensors="""pt"""						)
												self.assertEqual(streamer_text_tokenized.input_ids.shape			,     (1, 1)						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							Any  =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
												UpperCamelCase_		:							Optional[int]  =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__UpperCamelCase						)
												UpperCamelCase_		:							str  =		-1

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

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







import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

SCREAMING_SNAKE_CASE		:     List[str]         =	"\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"

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





						def        A_							(self						)				->				str:
												return datasets.MetricInfo(
												    description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
												        {
												            """X""": datasets.Sequence(datasets.Value("""float"""			,     id="""sequence"""						)			,     id="""X"""						),
												        }						)			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		np.array(__UpperCamelCase						)

												# Assert that arrays are 2D
												if len(X.shape						) != 2:
																		raise ValueError("""Expected `X` to be a 2D vector"""						)
												if len(reference_distribution.shape						) != 2:
																		raise ValueError("""Expected `reference_distribution` to be a 2D vector"""						)
												if reference_distribution.shape[0] < 2:
																		raise ValueError(
																		    """Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"""						)

												# Get mahalanobis distance for each prediction
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






