"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
           GPTNeoXModel,
       )





class 				_A		:

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=64	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=None	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_input_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_labels
            lowercase														=			num_choices
            lowercase														=			scope
            lowercase														=			vocab_size - 1

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_input_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_labels:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.num_labels		)

            lowercase														=			self.get_config()

            return config, input_ids, input_mask, token_labels

      def        A__ (		self		):







            """simple docstring"""



            return GPTNeoXConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=__lowerCAmelCase	, initializer_range=self.initializer_range	, pad_token_id=self.pad_token_id	, )

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.prepare_config_and_inputs()

            lowercase														=			True

            return config, input_ids, input_mask, token_labels

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			GPTNeoXModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            lowercase														=			model(__lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			True
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			GPTNeoXForCausalLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForQuestionAnswering(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            self.parent.assertEqual(result.start_logits.shape	, (self.batch_size, self.seq_length)		)
            self.parent.assertEqual(result.end_logits.shape	, (self.batch_size, self.seq_length)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForSequenceClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			ids_tensor([self.batch_size]	, self.type_sequence_label_size		)
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForTokenClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			True
            lowercase														=			GPTNeoXForCausalLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()

            # first forward pass
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, use_cache=__lowerCAmelCase		)
            lowercase														=			outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            lowercase														=			ids_tensor((self.batch_size, 3)	, config.vocab_size		)
            lowercase														=			ids_tensor((self.batch_size, 3)	, vocab_size=2		)

            # append to next input_ids and
            lowercase														=			torch.cat([input_ids, next_tokens]	, dim=-1		)
            lowercase														=			torch.cat([input_mask, next_mask]	, dim=-1		)

            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, output_hidden_states=__lowerCAmelCase		)
            lowercase														=			output_from_no_past["""hidden_states"""][0]
            lowercase														=			model(
                __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, past_key_values=__lowerCAmelCase	, output_hidden_states=__lowerCAmelCase	, )["""hidden_states"""][0]

            # select random slice
            lowercase														=			ids_tensor((1,)	, output_from_past.shape[-1]		).item()
            lowercase														=			output_from_no_past[:, -3:, random_slice_idx].detach()
            lowercase														=			output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-3		)		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict






@require_torch
class 				_A		( lowerCAmelCase						,					lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Union[str, Any]													=			(
          (
              GPTNeoXModel,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      snake_case__    :     str													=			(GPTNeoXForCausalLM,) if is_torch_available() else ()
      snake_case__    :     int													=			(
          {
              'feature-extraction': GPTNeoXModel,
              'question-answering': GPTNeoXForQuestionAnswering,
              'text-classification': GPTNeoXForSequenceClassification,
              'text-generation': GPTNeoXForCausalLM,
              'token-classification': GPTNeoXForTokenClassification,
              'zero-shot': GPTNeoXForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__    :     str													=			False
      snake_case__    :     str													=			False
      snake_case__    :     Optional[int]													=			False
      snake_case__    :     List[str]													=			False

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			GPTNeoXModelTester(self		)
            lowercase														=			ConfigTester(self	, config_class=__lowerCAmelCase	, hidden_size=64	, num_attention_heads=8		)

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_decoder()

            lowercase														=			None

            self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase		)

      @unittest.skip(reason="""Feed forward chunking is not implemented"""		)
      def        A__ (		self		):







            """simple docstring"""



            pass







      @parameterized.expand([("""linear""",), ("""dynamic""",)]		)
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()
            lowercase														=			ids_tensor([1, 10]	, config.vocab_size		)
            lowercase														=			ids_tensor([1, int(config.max_position_embeddings * 1.5		)]	, config.vocab_size		)

            set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            original_model.to(__lowerCAmelCase		)
            original_model.eval()
            lowercase														=			original_model(__lowerCAmelCase		).last_hidden_state
            lowercase														=			original_model(__lowerCAmelCase		).last_hidden_state

            set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
            lowercase														=			{"""type""": scaling_type, """factor""": 10.0}
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            scaled_model.to(__lowerCAmelCase		)
            scaled_model.eval()
            lowercase														=			scaled_model(__lowerCAmelCase		).last_hidden_state
            lowercase														=			scaled_model(__lowerCAmelCase		).last_hidden_state

            # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
            # maximum sequence length, so the outputs for the short input should match.
            if scaling_type == "dynamic":
                  self.assertTrue(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)
            else:
                  self.assertFalse(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)

            # The output should be different for long inputs
            self.assertFalse(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)






@require_torch
class 				_A		( unittest.TestCase			):

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)
            for checkpointing in [True, False]:
                  lowercase														=			GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)

                  if checkpointing:
                        model.gradient_checkpointing_enable()
                  else:
                        model.gradient_checkpointing_disable()
                  model.to(__lowerCAmelCase		)

                  lowercase														=			tokenizer("""My favorite food is"""	, return_tensors="""pt"""		).to(__lowerCAmelCase		)
                  # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
                  # See: https://github.com/huggingface/transformers/pull/24193
                  lowercase														=			"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

                  lowercase														=			model.generate(**__lowerCAmelCase	, do_sample=__lowerCAmelCase	, max_new_tokens=20		)
                  lowercase														=			tokenizer.batch_decode(__lowerCAmelCase		)[0]

                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)




"""simple docstring"""






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


__lowerCAmelCase					: str        ="""\
@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\",
}
"""

__lowerCAmelCase					: str        ="""\
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
"""

__lowerCAmelCase					: Optional[int]        ="""
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 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """simple docstring"""



            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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

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

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

            return result



