





'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      __lowerCamelCase						: List[str]		=   'platform'

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      ):


  """simple docstring"""

  if attention_mask is None:
    _UpperCamelCase       			=np.where(input_ids != config.pad_token_id  ,      1  ,      0   )
  if decoder_attention_mask is None:
    _UpperCamelCase       			=np.where(decoder_input_ids != config.pad_token_id  ,      1  ,      0   )
  if head_mask is None:
    _UpperCamelCase       			=np.ones((config.encoder_layers, config.encoder_attention_heads)   )
  if decoder_head_mask is None:
    _UpperCamelCase       			=np.ones((config.decoder_layers, config.decoder_attention_heads)   )
  if cross_attn_head_mask is None:
    _UpperCamelCase       			=np.ones((config.decoder_layers, config.decoder_attention_heads)   )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }







class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Any=13							,	UpperCamelCase__    :					Optional[Any]=7							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					Dict=False							,	UpperCamelCase__    :					Union[str, Any]=99							,	UpperCamelCase__    :					List[Any]=16							,	UpperCamelCase__    :					Optional[Any]=2							,	UpperCamelCase__    :					List[Any]=4							,	UpperCamelCase__    :					Optional[int]=4							,	UpperCamelCase__    :					Any="gelu"							,	UpperCamelCase__    :					List[Any]=0.1							,	UpperCamelCase__    :					Union[str, Any]=0.1							,	UpperCamelCase__    :					Dict=32							,	UpperCamelCase__    :					Optional[int]=2							,	UpperCamelCase__    :					Dict=1							,	UpperCamelCase__    :					int=0							,	UpperCamelCase__    :					Optional[Any]=0.02							,	)     ->						Dict:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=seq_length
    _UpperCamelCase       			=is_training
    _UpperCamelCase       			=use_labels
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=max_position_embeddings
    _UpperCamelCase       			=eos_token_id
    _UpperCamelCase       			=pad_token_id
    _UpperCamelCase       			=bos_token_id
    _UpperCamelCase       			=initializer_range


  def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
    _UpperCamelCase       			=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,	self.vocab_size  )							,	3							,	self.vocab_size  )
    _UpperCamelCase       			=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,	dtype=np.intaa  ))							,	-1  )

    _UpperCamelCase       			=shift_tokens_right(UpperCamelCase__							,	1							,	2  )

    _UpperCamelCase       			=BlenderbotSmallConfig(
        vocab_size=self.vocab_size							,	d_model=self.hidden_size							,	encoder_layers=self.num_hidden_layers							,	decoder_layers=self.num_hidden_layers							,	encoder_attention_heads=self.num_attention_heads							,	decoder_attention_heads=self.num_attention_heads							,	encoder_ffn_dim=self.intermediate_size							,	decoder_ffn_dim=self.intermediate_size							,	dropout=self.hidden_dropout_prob							,	attention_dropout=self.attention_probs_dropout_prob							,	max_position_embeddings=self.max_position_embeddings							,	eos_token_id=self.eos_token_id							,	bos_token_id=self.bos_token_id							,	pad_token_id=self.pad_token_id							,	initializer_range=self.initializer_range							,	use_cache=UpperCamelCase__							,	)
    _UpperCamelCase       			=prepare_blenderbot_inputs_dict(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )
    return config, inputs_dict


  def   UpperCamelCase__      (				self    :					str  )     ->						Tuple:
    _UpperCamelCase			,     _UpperCamelCase       			=self.prepare_config_and_inputs()
    return config, inputs_dict


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Optional[Any]  )     ->						Union[str, Any]:
    _UpperCamelCase       			=20
    _UpperCamelCase       			=model_class_name(UpperCamelCase__  )

    _UpperCamelCase       			=model.encode(inputs_dict['''input_ids''']  )

    _UpperCamelCase			,     _UpperCamelCase       			=(
        inputs_dict['''decoder_input_ids'''],
        inputs_dict['''decoder_attention_mask'''],
    )

    _UpperCamelCase       			=model.init_cache(decoder_input_ids.shape[0]							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,	dtype='''i4'''  )

    _UpperCamelCase       			=jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]							,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,	)
    _UpperCamelCase       			=model.decode(
        decoder_input_ids[:, :-1]							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	past_key_values=UpperCamelCase__							,	decoder_position_ids=UpperCamelCase__							,	)

    _UpperCamelCase       			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,	dtype='''i4'''  )
    _UpperCamelCase       			=model.decode(
        decoder_input_ids[:, -1:]							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	past_key_values=outputs_cache.past_key_values							,	decoder_position_ids=UpperCamelCase__							,	)

    _UpperCamelCase       			=model.decode(UpperCamelCase__							,	UpperCamelCase__  )

    _UpperCamelCase       			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
    self.parent.assertTrue(diff < 1E-3							,	msg=F'''Max diff is {diff}'''  )




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						List[Any]:
    _UpperCamelCase       			=20
    _UpperCamelCase       			=model_class_name(UpperCamelCase__  )

    _UpperCamelCase       			=model.encode(inputs_dict['''input_ids''']  )

    _UpperCamelCase			,     _UpperCamelCase       			=(
        inputs_dict['''decoder_input_ids'''],
        inputs_dict['''decoder_attention_mask'''],
    )

    _UpperCamelCase       			=jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
        ]							,	axis=-1							,	)

    _UpperCamelCase       			=model.init_cache(decoder_input_ids.shape[0]							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]							,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,	)

    _UpperCamelCase       			=model.decode(
        decoder_input_ids[:, :-1]							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	past_key_values=UpperCamelCase__							,	decoder_position_ids=UpperCamelCase__							,	)
    _UpperCamelCase       			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,	dtype='''i4'''  )
    _UpperCamelCase       			=model.decode(
        decoder_input_ids[:, -1:]							,	UpperCamelCase__							,	past_key_values=outputs_cache.past_key_values							,	decoder_attention_mask=UpperCamelCase__							,	decoder_position_ids=UpperCamelCase__							,	)

    _UpperCamelCase       			=model.decode(UpperCamelCase__							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__  )

    _UpperCamelCase       			=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
    self.parent.assertTrue(diff < 1E-3							,	msg=F'''Max diff is {diff}'''  )







@require_flax
class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				99


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Tuple:
    _UpperCamelCase       			=np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ]							,	dtype=np.intaa							,	)

    _UpperCamelCase       			=input_ids.shape[0]
    _UpperCamelCase       			=BlenderbotSmallConfig(
        vocab_size=self.vocab_size							,	d_model=24							,	encoder_layers=2							,	decoder_layers=2							,	encoder_attention_heads=2							,	decoder_attention_heads=2							,	encoder_ffn_dim=32							,	decoder_ffn_dim=32							,	max_position_embeddings=48							,	eos_token_id=2							,	pad_token_id=1							,	bos_token_id=0							,	)
    return config, input_ids, batch_size


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Dict:
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=self._get_config_and_data()
    _UpperCamelCase       			=FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase__  )
    _UpperCamelCase       			=lm_model(input_ids=UpperCamelCase__  )
    _UpperCamelCase       			=(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['''logits'''].shape							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						str:
    _UpperCamelCase       			=BlenderbotSmallConfig(
        vocab_size=self.vocab_size							,	d_model=14							,	encoder_layers=2							,	decoder_layers=2							,	encoder_attention_heads=2							,	decoder_attention_heads=2							,	encoder_ffn_dim=8							,	decoder_ffn_dim=8							,	max_position_embeddings=48							,	)
    _UpperCamelCase       			=FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase__  )
    _UpperCamelCase       			=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,	dtype=np.intaa  )
    _UpperCamelCase       			=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,	dtype=np.intaa  )
    _UpperCamelCase       			=lm_model(input_ids=UpperCamelCase__							,	decoder_input_ids=UpperCamelCase__  )
    _UpperCamelCase       			=(*summary.shape, config.vocab_size)
    self.assertEqual(outputs['''logits'''].shape							,	UpperCamelCase__  )




  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,	dtype=np.intaa  )
    _UpperCamelCase       			=shift_tokens_right(UpperCamelCase__							,	1							,	2  )
    _UpperCamelCase       			=np.equal(UpperCamelCase__							,	1  ).astype(np.floataa  ).sum()
    _UpperCamelCase       			=np.equal(UpperCamelCase__							,	1  ).astype(np.floataa  ).sum()
    self.assertEqual(shifted.shape							,	input_ids.shape  )
    self.assertEqual(UpperCamelCase__							,	n_pad_before - 1  )
    self.assertTrue(np.equal(shifted[:, 0]							,	2  ).all()  )







@require_flax
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase   ,     lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				True
  lowerCAmelCase_					=				(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCAmelCase_					=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    _UpperCamelCase       			=FlaxBlenderbotSmallModelTester(self  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__  ):
        _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )
        _UpperCamelCase       			=model_class(UpperCamelCase__  )

        @jax.jit
        def encode_jitted(UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					Any=None							,	**UpperCamelCase__    :					Union[str, Any]  ):
          return model.encode(input_ids=UpperCamelCase__							,	attention_mask=UpperCamelCase__  )

        with self.subTest('''JIT Enabled'''  ):
          _UpperCamelCase       			=encode_jitted(**UpperCamelCase__  ).to_tuple()

        with self.subTest('''JIT Disabled'''  ):
          with jax.disable_jit():
            _UpperCamelCase       			=encode_jitted(**UpperCamelCase__  ).to_tuple()

        self.assertEqual(len(UpperCamelCase__  )							,	len(UpperCamelCase__  )  )
        for jitted_output, output in zip(UpperCamelCase__							,	UpperCamelCase__  ):
          self.assertEqual(jitted_output.shape							,	output.shape  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__  ):
        _UpperCamelCase       			=model_class(UpperCamelCase__  )
        _UpperCamelCase       			=model.encode(inputs_dict['''input_ids''']							,	inputs_dict['''attention_mask''']  )

        _UpperCamelCase       			={
            '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
            '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
            '''encoder_outputs''': encoder_outputs,
        }

        @jax.jit
        def decode_jitted(UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Union[str, Any]  ):
          return model.decode(
              decoder_input_ids=UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	encoder_outputs=UpperCamelCase__							,	)

        with self.subTest('''JIT Enabled'''  ):
          _UpperCamelCase       			=decode_jitted(**UpperCamelCase__  ).to_tuple()

        with self.subTest('''JIT Disabled'''  ):
          with jax.disable_jit():
            _UpperCamelCase       			=decode_jitted(**UpperCamelCase__  ).to_tuple()

        self.assertEqual(len(UpperCamelCase__  )							,	len(UpperCamelCase__  )  )
        for jitted_output, output in zip(UpperCamelCase__							,	UpperCamelCase__  ):
          self.assertEqual(jitted_output.shape							,	output.shape  )




  @slow
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[Any]:
    for model_class_name in self.all_model_classes:
      _UpperCamelCase       			=model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''  )
      # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
      _UpperCamelCase       			=np.ones((1, 1)  ) * model.config.eos_token_id
      _UpperCamelCase       			=model(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )













'''simple docstring'''






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCamelCase						: str		=   '\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__lowerCamelCase						: Optional[int]		=   '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__lowerCamelCase						: str		=   '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					str  )     ->						List[str]:
    if version.parse(scb.__version__  ) < version.parse('''1.4.12'''  ):
      raise ImportWarning(
          '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
          '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''  )
    return datasets.MetricInfo(
        description=_DESCRIPTION							,	citation=_CITATION							,	homepage='''http://www.cs.umd.edu/~snover/tercom/'''							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
            {
                '''predictions''': datasets.Value('''string'''							,	id='''sequence'''  ),
                '''references''': datasets.Sequence(datasets.Value('''string'''							,	id='''sequence'''  )							,	id='''references'''  ),
            }  )							,	codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']							,	reference_urls=[
            '''https://github.com/jhclark/tercom''',
        ]							,	)




  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	)     ->						str:
    _UpperCamelCase       			=len(references[0]  )
    if any(len(UpperCamelCase__  ) != references_per_prediction for refs in references  ):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
    _UpperCamelCase       			=[[refs[i] for refs in references] for i in range(UpperCamelCase__  )]

    _UpperCamelCase       			=TER(
        normalized=UpperCamelCase__							,	no_punct=UpperCamelCase__							,	asian_support=UpperCamelCase__							,	case_sensitive=UpperCamelCase__							,	)
    _UpperCamelCase       			=sb_ter.corpus_score(UpperCamelCase__							,	UpperCamelCase__  )

    return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






