

'''simple docstring'''





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

import datasets


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

UpperCamelCase_    						=       """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

UpperCamelCase_    						=       """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""





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







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										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  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		bool = False		,					UpperCAmelCase__     :		bool = False		,					UpperCAmelCase__     :		bool = False		,					UpperCAmelCase__     :		bool = False		,					):
										'''simple docstring'''
										lowercase :							Optional[int]    		=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'''   )
										lowercase :							Optional[int]    		=[[refs[i] for refs in references] for i in range(UpperCAmelCase__   )]

										lowercase :							str    		=TER(
										    normalized=UpperCAmelCase__		,					no_punct=UpperCAmelCase__		,					asian_support=UpperCAmelCase__		,					case_sensitive=UpperCAmelCase__		,					)
										lowercase :							Optional[Any]    		=sb_ter.corpus_score(UpperCAmelCase__		,					UpperCAmelCase__   )

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








'''simple docstring'''





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFTransfoXLForSequenceClassification,
							    TFTransfoXLLMHeadModel,
							    TFTransfoXLModel,
							)





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		List[str]		,					UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							Any    		=parent
										lowercase :							Optional[int]    		=13
										lowercase :							Union[str, Any]    		=7
										lowercase :							str    		=30
										lowercase :							Optional[int]    		=self.seq_length + self.mem_len
										lowercase :							Dict    		=15
										lowercase :							List[str]    		=True
										lowercase :							Optional[int]    		=True
										lowercase :							Tuple    		=99
										lowercase :							str    		=[10, 50, 80]
										lowercase :							List[Any]    		=32
										lowercase :							Optional[int]    		=32
										lowercase :							int    		=4
										lowercase :							Any    		=8
										lowercase :							List[Any]    		=128
										lowercase :							List[str]    		=2
										lowercase :							Tuple    		=2
										lowercase :							int    		=None
										lowercase :							Optional[int]    		=1
										lowercase :							int    		=0
										lowercase :							List[str]    		=3
										lowercase :							str    		=self.vocab_size - 1
										lowercase :							Tuple    		=0.01







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							str    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )
										lowercase :							Tuple    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )

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

										lowercase :							Union[str, Any]    		=TransfoXLConfig(
										    vocab_size=self.vocab_size		,					mem_len=self.mem_len		,					clamp_len=self.clamp_len		,					cutoffs=self.cutoffs		,					d_model=self.hidden_size		,					d_embed=self.d_embed		,					n_head=self.num_attention_heads		,					d_head=self.d_head		,					d_inner=self.d_inner		,					div_val=self.div_val		,					n_layer=self.num_hidden_layers		,					eos_token_id=self.eos_token_id		,					pad_token_id=self.vocab_size - 1		,					init_range=self.init_range		,					num_labels=self.num_labels		,					)

										return (config, input_ids_a, input_ids_a, lm_labels)







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										random.seed(self.seed   )
										tf.random.set_seed(self.seed   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=TFTransfoXLModel(UpperCAmelCase__   )

										lowercase		,	lowercase :							Optional[Any]    		=model(UpperCAmelCase__   ).to_tuple()

										lowercase :							List[str]    		={'''input_ids''': input_ids_a, '''mems''': mems_a}

										lowercase		,	lowercase :							Any    		=model(UpperCAmelCase__   ).to_tuple()

										self.parent.assertEqual(hidden_states_a.shape		,					(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertEqual(hidden_states_a.shape		,					(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]		,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,					)
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]		,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,					)







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							int    		=TFTransfoXLLMHeadModel(UpperCAmelCase__   )

										lowercase		,	lowercase :							Tuple    		=model(UpperCAmelCase__   ).to_tuple()

										lowercase :							Optional[Any]    		={'''input_ids''': input_ids_a, '''labels''': lm_labels}
										lowercase		,	lowercase :							Optional[int]    		=model(UpperCAmelCase__   ).to_tuple()

										lowercase		,	lowercase :							List[str]    		=model([input_ids_a, mems_a]   ).to_tuple()

										lowercase :							int    		={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

										lowercase		,	lowercase :							str    		=model(UpperCAmelCase__   ).to_tuple()

										self.parent.assertEqual(lm_logits_a.shape		,					(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]		,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,					)

										self.parent.assertEqual(lm_logits_a.shape		,					(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]		,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers		,					)







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=TFTransfoXLForSequenceClassification(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=model(UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.prepare_config_and_inputs()
										((lowercase)		,	(lowercase)		,	(lowercase)		,	(lowercase)) :							Optional[Any]    		=config_and_inputs
										lowercase :							Union[str, Any]    		={'''input_ids''': input_ids_a}
										return config, inputs_dict





@require_tf
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    (
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				lowerCamelCase_		       =    () if is_tf_available() else ()
				lowerCamelCase_		       =    (
				    {
				        'feature-extraction': TFTransfoXLModel,
				        'text-classification': TFTransfoXLForSequenceClassification,
				        'text-generation': TFTransfoXLLMHeadModel,
				        'zero-shot': TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																# tokenizer.
																return True

										return False







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=TFTransfoXLModelTester(self   )
										lowercase :							Optional[int]    		=ConfigTester(self		,					config_class=UpperCAmelCase__		,					d_embed=37   )







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										self.model_tester.set_seed()
										lowercase :							List[str]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										self.model_tester.set_seed()
										lowercase :							Any    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Tuple    		=self.model_tester.prepare_config_and_inputs_for_common()
										lowercase :							int    		=[TFTransfoXLForSequenceClassification]

										for model_class in self.all_model_classes:
																lowercase :							str    		=model_class(UpperCAmelCase__   )
																assert isinstance(model.get_input_embeddings()		,					tf.keras.layers.Layer   )
																if model_class in list_other_models_with_output_ebd:
																						lowercase :							Union[str, Any]    		=model.get_output_embeddings()
																						assert isinstance(UpperCAmelCase__		,					tf.keras.layers.Layer   )
																						lowercase :							Any    		=model.get_bias()
																						assert name is None
																else:
																						lowercase :							Optional[int]    		=model.get_output_embeddings()
																						assert x is None
																						lowercase :							Optional[int]    		=model.get_bias()
																						assert name is None







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										# TODO JP: Make TransfoXL XLA compliant
										pass







				@slow
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase :							int    		=TFTransfoXLModel.from_pretrained(UpperCAmelCase__   )
																self.assertIsNotNone(UpperCAmelCase__   )







				@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''   )
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										pass





@require_tf
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@unittest.skip('''Skip test until #12651 is resolved.'''   )
				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
										# fmt: off
										lowercase :							Tuple    		=tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]		,					dtype=tf.intaa   )  # noqa: E231
										# fmt: on
										#  In 1991 , the remains of Russian Tsar Nicholas II and his family
										#  ( except for Alexei and Maria ) are discovered .
										#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
										#  remainder of the story . 1883 Western Siberia ,
										#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
										#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
										#  father initially slaps him for making such an accusation , Rasputin watches as the
										#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
										#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
										#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

										# fmt: off
										lowercase :							Optional[int]    		=[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # noqa: E231
										# fmt: on
										#  In 1991, the remains of Russian Tsar Nicholas II and his family (
										#  except for Alexei and Maria ) are discovered. The voice of young son,
										#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
										#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
										#  and a group of men to perform magic. Rasputin has a vision and
										#  denounces one of the men as a horse thief. Although his father initially
										#  slaps him for making such an accusation, Rasputin watches as the man
										#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
										#  of the Virgin Mary, prompting him to become a priest.
										#  Rasputin quickly becomes famous, with people, even a bishop, begging for
										#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
										# Nicholas II and his family were discovered. The voice of <unk> young son,
										# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

										lowercase :							int    		=model.generate(UpperCAmelCase__		,					max_length=200		,					do_sample=UpperCAmelCase__   )
										self.assertListEqual(output_ids[0].numpy().tolist()		,					UpperCAmelCase__   )





