



"""simple docstring"""







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCAmelCase:  Optional[Any]			     =				"""\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

UpperCAmelCase:  List[str]			     =				"""
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
"""

UpperCAmelCase:  List[Any]			     =				"""
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer 'id' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - 'id': id of the question-answer pair (see above),
            - 'answers': a list of Dict {'text': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    'exact': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'total': Number of score considered
    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'HasAns_f1': The F-score of predicted tokens versus the gold answer
    'HasAns_total': Number of score considered
    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'NoAns_f1': The F-score of predicted tokens versus the gold answer
    'NoAns_total': Number of score considered
    'best_exact': Best exact match (with varying threshold)
    'best_exact_thresh': No-answer probability threshold associated to the best exact match
    'best_f1': Best F1 (with varying threshold)
    'best_f1_thresh': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}
"""





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




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			return datasets.MetricInfo(
			    description=_DESCRIPTION				,citation=_CITATION				,inputs_description=_KWARGS_DESCRIPTION				,features=datasets.Features(
			        {
			            """predictions""": {
			                """id""": datasets.Value("""string"""   ),
			                """prediction_text""": datasets.Value("""string"""   ),
			                """no_answer_probability""": datasets.Value("""float32"""   ),
			            },
			            """references""": {
			                """id""": datasets.Value("""string"""   ),
			                """answers""": datasets.features.Sequence(
			                    {"""text""": datasets.Value("""string"""   ), """answer_start""": datasets.Value("""int32"""   )}   ),
			            },
			        }   )				,codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]				,reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]				,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_=1.0   ):
			_lowercase				: List[str]     =   {p["""id"""]: p["""no_answer_probability"""] for p in predictions}
			_lowercase				: Tuple     =   [{"""paragraphs""": [{"""qas""": references}]}]
			_lowercase				: Optional[Any]     =   {p["""id"""]: p["""prediction_text"""] for p in predictions}

			_lowercase				: str     =   make_qid_to_has_ans(UpperCAmelCase_   )  # maps qid to True/False
			_lowercase				: List[Any]     =   [k for k, v in qid_to_has_ans.items() if v]
			_lowercase				: Tuple     =   [k for k, v in qid_to_has_ans.items() if not v]

			_lowercase     ,  _lowercase				: List[str]     =   get_raw_scores(UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   apply_no_ans_threshold(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: List[str]     =   apply_no_ans_threshold(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   make_eval_dict(UpperCAmelCase_				,UpperCAmelCase_   )

			if has_ans_qids:
					_lowercase				: List[Any]     =   make_eval_dict(UpperCAmelCase_				,UpperCAmelCase_				,qid_list=UpperCAmelCase_   )
					merge_eval(UpperCAmelCase_				,UpperCAmelCase_				,"""HasAns"""   )
			if no_ans_qids:
					_lowercase				: Tuple     =   make_eval_dict(UpperCAmelCase_				,UpperCAmelCase_				,qid_list=UpperCAmelCase_   )
					merge_eval(UpperCAmelCase_				,UpperCAmelCase_				,"""NoAns"""   )
			find_all_best_thresh(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			return dict(UpperCAmelCase_   )








"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
		UpperCAmelCase:  List[str]			     =				"""platform"""

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=None   ,				):
		if attention_mask is None:
				_lowercase				: Union[str, Any]     =   np.where(input_ids != config.pad_token_id   ,				1   ,				0      )
		if decoder_attention_mask is None:
				_lowercase				: Dict     =   np.where(decoder_input_ids != config.pad_token_id   ,				1   ,				0      )
		if head_mask is None:
				_lowercase				: Any     =   np.ones((config.encoder_layers, config.encoder_attention_heads)      )
		if decoder_head_mask is None:
				_lowercase				: Union[str, Any]     =   np.ones((config.decoder_layers, config.decoder_attention_heads)      )
		if cross_attn_head_mask is None:
				_lowercase				: Optional[int]     =   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				,UpperCAmelCase_				,UpperCAmelCase_=13				,UpperCAmelCase_=7				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=99				,UpperCAmelCase_=16				,UpperCAmelCase_=2				,UpperCAmelCase_=4				,UpperCAmelCase_=4				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.1				,UpperCAmelCase_=32				,UpperCAmelCase_=2				,UpperCAmelCase_=1				,UpperCAmelCase_=0				,UpperCAmelCase_=0.02				,):
			_lowercase				: List[Any]     =   parent
			_lowercase				: str     =   batch_size
			_lowercase				: Tuple     =   seq_length
			_lowercase				: Optional[Any]     =   is_training
			_lowercase				: Optional[Any]     =   use_labels
			_lowercase				: Optional[int]     =   vocab_size
			_lowercase				: List[Any]     =   hidden_size
			_lowercase				: Dict     =   num_hidden_layers
			_lowercase				: Optional[Any]     =   num_attention_heads
			_lowercase				: Optional[Any]     =   intermediate_size
			_lowercase				: Tuple     =   hidden_act
			_lowercase				: Tuple     =   hidden_dropout_prob
			_lowercase				: Tuple     =   attention_probs_dropout_prob
			_lowercase				: Union[str, Any]     =   max_position_embeddings
			_lowercase				: Any     =   eos_token_id
			_lowercase				: Optional[Any]     =   pad_token_id
			_lowercase				: Dict     =   bos_token_id
			_lowercase				: Any     =   initializer_range






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,self.vocab_size   )				,3				,self.vocab_size   )
			_lowercase				: Optional[int]     =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,dtype=np.intaa   ))				,-1   )

			_lowercase				: List[str]     =   shift_tokens_right(UpperCAmelCase_				,1				,2   )

			_lowercase				: Union[str, Any]     =   MarianConfig(
			    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_				,)
			_lowercase				: Union[str, Any]     =   prepare_marian_inputs_dict(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			return config, inputs_dict






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: List[Any]     =   self.prepare_config_and_inputs()
			return config, inputs_dict






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   20
			_lowercase				: Dict     =   model_class_name(UpperCAmelCase_   )

			_lowercase				: str     =   model.encode(inputs_dict["""input_ids"""]   )

			_lowercase     ,  _lowercase				: int     =   (
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,dtype="""i4"""   )

			_lowercase				: Union[str, Any]     =   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,)
			_lowercase				: str     =   model.decode(
			    decoder_input_ids[:, :-1]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Tuple     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,dtype="""i4"""   )
			_lowercase				: Tuple     =   model.decode(
			    decoder_input_ids[:, -1:]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=outputs_cache.past_key_values				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Optional[int]     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Tuple     =   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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: int     =   20
			_lowercase				: Tuple     =   model_class_name(UpperCAmelCase_   )

			_lowercase				: Tuple     =   model.encode(inputs_dict["""input_ids"""]   )

			_lowercase     ,  _lowercase				: Union[str, Any]     =   (
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

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

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,)

			_lowercase				: Dict     =   model.decode(
			    decoder_input_ids[:, :-1]				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,past_key_values=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)
			_lowercase				: List[Any]     =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,dtype="""i4"""   )
			_lowercase				: List[Any]     =   model.decode(
			    decoder_input_ids[:, -1:]				,UpperCAmelCase_				,past_key_values=outputs_cache.past_key_values				,decoder_attention_mask=UpperCAmelCase_				,decoder_position_ids=UpperCAmelCase_				,)

			_lowercase				: Tuple     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_   )

			_lowercase				: Optional[Any]     =   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	(     snake_case					,   unittest.TestCase					,   snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  True
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	SCREAMING_SNAKE_CASE_							:       str						       =  (FlaxMarianMTModel,) if is_flax_available() else ()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   FlaxMarianModelTester(self   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Optional[Any]     =   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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: str     =   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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Optional[int]     =   self._prepare_for_class(UpperCAmelCase_				,UpperCAmelCase_   )
							_lowercase				: Optional[int]     =   model_class(UpperCAmelCase_   )

							@jax.jit
							def encode_jitted(UpperCAmelCase_				,UpperCAmelCase_=None				,**UpperCAmelCase_   ):
									return model.encode(input_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_   )

							with self.subTest("""JIT Enabled"""   ):
									_lowercase				: List[Any]     =   encode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Union[str, Any]     =   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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Any     =   model_class(UpperCAmelCase_   )
							_lowercase				: List[str]     =   model.encode(inputs_dict["""input_ids"""]				,inputs_dict["""attention_mask"""]   )

							_lowercase				: str     =   {
							    """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_				,UpperCAmelCase_				,UpperCAmelCase_   ):
									return model.decode(
									    decoder_input_ids=UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_				,encoder_outputs=UpperCAmelCase_				,)

							with self.subTest("""JIT Enabled"""   ):
									_lowercase				: List[Any]     =   decode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Optional[Any]     =   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    lowerCamelCase__					(     self   ):
			for model_class_name in self.all_model_classes:
					_lowercase				: List[Any]     =   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""   )
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					_lowercase				: Union[str, Any]     =   np.ones((1, 1)   ) * model.config.eos_token_id
					_lowercase				: Dict     =   model(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  None






	@classmethod
	def    lowerCamelCase__					(     cls   ):
			_lowercase				: str     =   f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls






	@cached_property
	def    lowerCamelCase__					(     self   ):
			return MarianTokenizer.from_pretrained(self.model_name   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.tokenizer.eos_token_id






	@cached_property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: FlaxMarianMTModel     =   FlaxMarianMTModel.from_pretrained(self.model_name   )
			self.assertEqual(model.config.decoder_start_token_id				,model.config.pad_token_id   )
			return model






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: int     =   self.translate_src_text(**UpperCAmelCase_   )
			self.assertListEqual(self.expected_text				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: Dict     =   self.tokenizer(self.src_text				,padding=UpperCAmelCase_				,return_tensors="""np"""				,**UpperCAmelCase_   )
			_lowercase				: List[Any]     =   self.model.generate(
			    model_inputs.input_ids				,attention_mask=model_inputs.attention_mask				,num_beams=2				,max_length=1_28				,).sequences
			_lowercase				: int     =   self.tokenizer.batch_decode(UpperCAmelCase_				,skip_special_tokens=UpperCAmelCase_   )
			return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Any						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Dict						       =  [
	    "I am a small frog.",
	    "Now I can forget the 100 words of german that I know.",
	]
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  [
	    "Je suis une petite grenouille.",
	    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Any						       =  [
	    "Donnez moi le micro.",
	    "Tom et Mary étaient assis à une table.",  # Accents
	]
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  [
	    "Give me the microphone.",
	    "Tom and Mary were sitting at a table.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  "mt"
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  "de"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  [
	    "I am a small frog.",
	    "Now I can forget the 100 words of german that I know.",
	    "Tom asked his teacher for advice.",
	    "That's how I would do it.",
	    "Tom really admired Mary's courage.",
	    "Turn around and close your eyes.",
	]
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  [
	    "Ich bin ein kleiner Frosch.",
	    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
	    "Tom bat seinen Lehrer um Rat.",
	    "So würde ich das machen.",
	    "Tom bewunderte Marias Mut wirklich.",
	    "Drehen Sie sich um und schließen Sie die Augen.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "zh"
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["My name is Wolfgang and I live in Berlin"]
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["我叫沃尔夫冈 我住在柏林"]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "ru"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  ["Он показал мне рукопись своей новой пьесы."]
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  ["Il m'a montré le manuscrit de sa nouvelle pièce."]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "en"
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  "ROMANCE"
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  [
	    ">>fr<< Don't spend so much time watching TV.",
	    ">>pt<< Your message has been sent.",
	    ">>es<< He's two years older than me.",
	]
	SCREAMING_SNAKE_CASE_							:       int						       =  [
	    "Ne passez pas autant de temps à regarder la télé.",
	    "A sua mensagem foi enviada.",
	    "Es dos años más viejo que yo.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()



