


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 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(_lowerCAmelCase							)
										lowercase_					=      -1

										lowercase_					=      ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size							).to(_lowerCAmelCase							)
										lowercase_					=      model.generate(_lowerCAmelCase				,       max_new_tokens=10				,       do_sample=_lowerCAmelCase							)
										lowercase_					=      tokenizer.decode(greedy_ids[0]							)

										with CaptureStdout() as cs:
															lowercase_					=      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
										lowercase_					=      cs.out[:-1]

										self.assertEqual(_lowerCAmelCase				,       _lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(_lowerCAmelCase							)
										lowercase_					=      -1

										lowercase_					=      ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size							).to(_lowerCAmelCase							)
										lowercase_					=      model.generate(_lowerCAmelCase				,       max_new_tokens=10				,       do_sample=_lowerCAmelCase							)
										lowercase_					=      tokenizer.decode(greedy_ids[0]							)

										lowercase_					=      TextIteratorStreamer(_lowerCAmelCase							)
										lowercase_					=      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
										lowercase_					=      Thread(target=model.generate				,       kwargs=_lowerCAmelCase							)
										thread.start()
										lowercase_					=      """"""
										for new_text in streamer:
															streamer_text += new_text

										self.assertEqual(_lowerCAmelCase				,       _lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(_lowerCAmelCase							)
										lowercase_					=      -1

										lowercase_					=      ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size							).to(_lowerCAmelCase							)
										lowercase_					=      model.generate(_lowerCAmelCase				,       max_new_tokens=10				,       do_sample=_lowerCAmelCase							)
										lowercase_					=      greedy_ids[:, input_ids.shape[1] :]
										lowercase_					=      tokenizer.decode(new_greedy_ids[0]							)

										with CaptureStdout() as cs:
															lowercase_					=      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
										lowercase_					=      cs.out[:-1]

										self.assertEqual(_lowerCAmelCase				,       _lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained("""distilgpt2"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained("""distilgpt2"""							).to(_lowerCAmelCase							)
										lowercase_					=      -1

										lowercase_					=      torch.ones((1, 5)				,       device=_lowerCAmelCase							).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
															lowercase_					=      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
										lowercase_					=      cs.out[:-1]  # Remove the final "\n"
										lowercase_					=      tokenizer(_lowerCAmelCase				,       return_tensors="""pt"""							)
										self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)							)







					def 	UpperCAmelCase__							(  self							: Optional[int]							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(_lowerCAmelCase							)
										lowercase_					=      -1

										lowercase_					=      ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size							).to(_lowerCAmelCase							)
										lowercase_					=      TextIteratorStreamer(_lowerCAmelCase				,       timeout=0.001							)
										lowercase_					=      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
										lowercase_					=      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							):
															lowercase_					=      """"""
															for new_text in streamer:
																				streamer_text += new_text









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

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




 def 				_snake_case (						self		)     ->				Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(self._get_feature_types()		)      ,  reference_urls=[
             "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
         ]      ,  )




 def 				_snake_case (						self		)     ->				Tuple:
     if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"		)		),
             "references": datasets.Sequence(datasets.Value("float"		)		),
         }
     else:
         return {
             "predictions": datasets.Value("float"		),
             "references": datasets.Value("float"		),
         }





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


