import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						)



class __A     :

							'''simple docstring'''

							def __init__(				self   ,	_snake_case   ,	_snake_case=14   ,	_snake_case=7   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=99   ,	_snake_case=32   ,	_snake_case=5   ,	_snake_case=4   ,	_snake_case=37   ,	_snake_case="gelu"   ,	_snake_case=0.1   ,	_snake_case=0.1   ,	_snake_case=512   ,	_snake_case=16   ,	_snake_case=2   ,	_snake_case=0.02   ,	_snake_case=3   ,	_snake_case=4   ,	_snake_case=None   ,	):
												_lowerCAmelCase			:		int          = parent
												_lowerCAmelCase			:		Optional[int]          = batch_size
												_lowerCAmelCase			:		Tuple          = seq_length
												_lowerCAmelCase			:		Tuple          = is_training
												_lowerCAmelCase			:		Optional[Any]          = use_token_type_ids
												_lowerCAmelCase			:		Any          = use_input_mask
												_lowerCAmelCase			:		Union[str, Any]          = use_labels
												_lowerCAmelCase			:		Optional[int]          = use_mc_token_ids
												_lowerCAmelCase			:		List[Any]          = vocab_size
												_lowerCAmelCase			:		Optional[Any]          = hidden_size
												_lowerCAmelCase			:		Optional[Any]          = num_hidden_layers
												_lowerCAmelCase			:		List[Any]          = num_attention_heads
												_lowerCAmelCase			:		int          = intermediate_size
												_lowerCAmelCase			:		Optional[int]          = hidden_act
												_lowerCAmelCase			:		Tuple          = hidden_dropout_prob
												_lowerCAmelCase			:		Tuple          = attention_probs_dropout_prob
												_lowerCAmelCase			:		List[Any]          = max_position_embeddings
												_lowerCAmelCase			:		Optional[int]          = type_vocab_size
												_lowerCAmelCase			:		Tuple          = type_sequence_label_size
												_lowerCAmelCase			:		int          = initializer_range
												_lowerCAmelCase			:		str          = num_labels
												_lowerCAmelCase			:		Tuple          = num_choices
												_lowerCAmelCase			:		Any          = scope
												_lowerCAmelCase			:		Tuple          = self.vocab_size - 1






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[int]          = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size )

												_lowerCAmelCase			:		Optional[int]          = None
												if self.use_input_mask:
																	_lowerCAmelCase			:		Dict          = random_attention_mask([self.batch_size, self.seq_length] )

												_lowerCAmelCase			:		Union[str, Any]          = None
												if self.use_token_type_ids:
																	_lowerCAmelCase			:		Dict          = ids_tensor([self.batch_size, self.seq_length]   ,	self.type_vocab_size )

												_lowerCAmelCase			:		Optional[Any]          = None
												if self.use_mc_token_ids:
																	_lowerCAmelCase			:		int          = ids_tensor([self.batch_size, self.num_choices]   ,	self.seq_length )

												_lowerCAmelCase			:		str          = None
												_lowerCAmelCase			:		List[Any]          = None
												_lowerCAmelCase			:		Tuple          = None
												if self.use_labels:
																	_lowerCAmelCase			:		Tuple          = ids_tensor([self.batch_size]   ,	self.type_sequence_label_size )
																	_lowerCAmelCase			:		Optional[int]          = ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels )
																	_lowerCAmelCase			:		Optional[int]          = ids_tensor([self.batch_size]   ,	self.num_choices )

												_lowerCAmelCase			:		Union[str, Any]          = self.get_config()

												_lowerCAmelCase			:		Dict          = ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,	2 )

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												return CTRLConfig(
												    vocab_size=self.vocab_size   ,	n_embd=self.hidden_size   ,	n_layer=self.num_hidden_layers   ,	n_head=self.num_attention_heads   ,	n_positions=self.max_position_embeddings   ,	pad_token_id=self.pad_token_id   ,	)






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	*_snake_case ):
												_lowerCAmelCase			:		List[str]          = CTRLModel(config=__A )
												model.to(__A )
												model.eval()

												model(__A   ,	token_type_ids=__A   ,	head_mask=__A )
												model(__A   ,	token_type_ids=__A )
												_lowerCAmelCase			:		List[str]          = model(__A )
												self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size) )
												self.parent.assertEqual(len(result.past_key_values )   ,	config.n_layer )






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	*_snake_case ):
												_lowerCAmelCase			:		Optional[Any]          = CTRLLMHeadModel(__A )
												model.to(__A )
												model.eval()

												_lowerCAmelCase			:		Any          = model(__A   ,	token_type_ids=__A   ,	labels=__A )
												self.parent.assertEqual(result.loss.shape   ,	() )
												self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size) )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[Any]          = self.prepare_config_and_inputs()

												(
												    _lowerCAmelCase     
												)			:		List[str]          = config_and_inputs

												_lowerCAmelCase			:		str          = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

												return config, inputs_dict






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	*_snake_case ):
												_lowerCAmelCase			:		Optional[int]          = self.num_labels
												_lowerCAmelCase			:		Union[str, Any]          = CTRLForSequenceClassification(__A )
												model.to(__A )
												model.eval()
												_lowerCAmelCase			:		int          = ids_tensor([self.batch_size]   ,	self.type_sequence_label_size )
												_lowerCAmelCase			:		int          = model(__A   ,	token_type_ids=__A   ,	labels=__A )
												self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels) )



@require_torch
class __A     (      snake_case__		,snake_case__		,snake_case__		,unittest.TestCase						):

							'''simple docstring'''

							a_     =					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							a_     =					(CTRLLMHeadModel,) if is_torch_available() else ()
							a_     =					(
							    {
							        '''feature-extraction''': CTRLModel,
							        '''text-classification''': CTRLForSequenceClassification,
							        '''text-generation''': CTRLLMHeadModel,
							        '''zero-shot''': CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a_     =					True
							a_     =					False
							a_     =					False






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

												return False






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Tuple          = CTRLModelTester(self )
												_lowerCAmelCase			:		Optional[int]          = ConfigTester(self   ,	config_class=__A   ,	n_embd=37 )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												self.config_tester.run_common_tests()






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[Any]          = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*__A )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[str]          = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*__A )






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												pass






							@slow
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase			:		List[str]          = CTRLModel.from_pretrained(__A )
																	self.assertIsNotNone(__A )






							@unittest.skip("The model doesn\'t support left padding" )  # and it's not used enough to be worth fixing :)
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												pass



@require_torch
class __A     (      unittest.TestCase						):

							'''simple docstring'''

							def 	SCREAMING_SNAKE_CASE__			(				self ):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()






							@slow
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Dict          = CTRLLMHeadModel.from_pretrained("ctrl" )
												model.to(__A )
												_lowerCAmelCase			:		Union[str, Any]          = torch.tensor(
												    [[1_1859, 0, 1611, 8]]   ,	dtype=torch.long   ,	device=__A )  # Legal the president is
												_lowerCAmelCase			:		Dict          = [
												    1_1859,
												    0,
												    1611,
												    8,
												    5,
												    150,
												    2_6449,
												    2,
												    19,
												    348,
												    469,
												    3,
												    2595,
												    48,
												    2_0740,
												    24_6533,
												    24_6533,
												    19,
												    30,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

												_lowerCAmelCase			:		List[str]          = model.generate(__A   ,	do_sample=__A )
												self.assertListEqual(output_ids[0].tolist()   ,	__A )








import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowercase											=    """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

_lowercase											=    """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

_lowercase											=    """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""


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


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       inputs_description=_KWARGS_DESCRIPTION						,       features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''string'''						,       id='''sequence'''				),
                          '''references''': datasets.Value('''string'''						,       id='''sequence'''				),
                      }				)						,       codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']						,       reference_urls=[
                      '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
                      '''https://github.com/google-research/google-research/tree/master/rouge''',
                  ]						,       )





       def 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

              for ref, pred in zip(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				int		=scorer.score(__A						,       __A				)
                     if use_aggregator:
                            aggregator.add_scores(__A				)
                     else:
                            scores.append(__A				)

              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=aggregator.aggregate()
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		={}
                     for key in scores[0]:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=[score[key] for score in scores]

              return result
