from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
@require_tf
class 	_a  :



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					MarianConfig
	__SCREAMING_SNAKE_CASE              =					{}
	__SCREAMING_SNAKE_CASE              =					'gelu'


	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=7       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=99       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=37       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=20       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=1       ,  lowerCAmelCase_=0       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =seq_length
		_lowercase   =is_training
		_lowercase   =use_labels
		_lowercase   =vocab_size
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =intermediate_size

		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =max_position_embeddings
		_lowercase   =eos_token_id
		_lowercase   =pad_token_id
		_lowercase   =bos_token_id


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size       )
		_lowercase   =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )       ,  1       )
		_lowercase   =tf.concat([input_ids, eos_tensor]       ,  axis=1       )

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

		_lowercase   =self.config_cls(
		    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_ids=[2]       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.pad_token_id       ,  **self.config_updates       ,  )
		_lowercase   =prepare_marian_inputs_dict(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		return config, inputs_dict


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =TFMarianModel(config=lowerCAmelCase_       ).get_decoder()
		_lowercase   =inputs_dict["input_ids"]

		_lowercase   =input_ids[:1, :]
		_lowercase   =inputs_dict["attention_mask"][:1, :]
		_lowercase   =inputs_dict["head_mask"]
		_lowercase   =1

		# first forward pass
		_lowercase   =model(lowerCAmelCase_       ,  attention_mask=lowerCAmelCase_       ,  head_mask=lowerCAmelCase_       ,  use_cache=lowerCAmelCase_       )

		_lowercase   ,  _lowercase   =outputs.to_tuple()

		# create hypothetical next token and extent to next_input_ids
		_lowercase   =ids_tensor((self.batch_size, 3)       ,  config.vocab_size       )
		_lowercase   =tf.cast(ids_tensor((self.batch_size, 3)       ,  2       )       ,  tf.inta       )

		# append to next input_ids and
		_lowercase   =tf.concat([input_ids, next_tokens]       ,  axis=-1       )
		_lowercase   =tf.concat([attention_mask, next_attn_mask]       ,  axis=-1       )

		_lowercase   =model(lowerCAmelCase_       ,  attention_mask=lowerCAmelCase_       )[0]
		_lowercase   =model(lowerCAmelCase_       ,  attention_mask=lowerCAmelCase_       ,  past_key_values=lowerCAmelCase_       )[0]

		self.parent.assertEqual(next_tokens.shape[1]       ,  output_from_past.shape[1]       )

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

		# test that outputs are equal for slice
		tf.debugging.assert_near(lowerCAmelCase_       ,  lowerCAmelCase_       ,  rtol=1e-3       )

def 	__lowerCamelCase							(				__a    : Dict      ,							__a    : Dict      ,							__a    : Tuple      ,							__a    : Any=None      ,							__a    : Any=None      ,							__a    : Dict=None      ,							__a    : int=None      ,							__a    : Dict=None      ,							)      ->       List[Any]:
	if attention_mask is None:
		_lowercase   =tf.cast(tf.math.not_equal(__a      ,							config.pad_token_id		)      ,							tf.inta		)
	if decoder_attention_mask is None:
		_lowercase   =tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape      ,							dtype=tf.inta		),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,							config.pad_token_id		)      ,							tf.inta		),
		    ]      ,							axis=-1      ,							)
	if head_mask is None:
		_lowercase   =tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
	if decoder_head_mask is None:
		_lowercase   =tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
	if cross_attn_head_mask is None:
		_lowercase   =tf.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": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}
@require_tf
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE              =					(TFMarianMTModel,) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE              =					(
	    {
	        'conversational': TFMarianMTModel,
	        'feature-extraction': TFMarianModel,
	        'summarization': TFMarianMTModel,
	        'text2text-generation': TFMarianMTModel,
	        'translation': TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE              =					True
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =TFMarianModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       )


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_       )

@require_tf
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					10_00  # show more chars for failing integration tests


	@classmethod
	def __lowerCAmelCase      (   cls       ):
		_lowercase   =F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
		return cls


	@cached_property
	def __lowerCAmelCase      (   self       ):
		return AutoTokenizer.from_pretrained(self.model_name       )


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


	@cached_property
	def __lowerCAmelCase      (   self       ):
		warnings.simplefilter("error"       )
		_lowercase   =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
		assert isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =model.config
		self.assertListEqual(c.bad_words_ids       ,  [[c.pad_token_id]]       )
		self.assertEqual(c.max_length       ,  512       )
		self.assertEqual(c.decoder_start_token_id       ,  c.pad_token_id       )
		return model


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.translate_src_text(**lowerCAmelCase_       )
		self.assertListEqual(self.expected_text       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.tokenizer(self.src_text       ,  **lowerCAmelCase_       ,  padding=lowerCAmelCase_       ,  return_tensors="tf"       )
		_lowercase   =self.model.generate(
		    model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2       ,  max_length=128       )
		_lowercase   =self.tokenizer.batch_decode(generated_ids.numpy()       ,  skip_special_tokens=lowerCAmelCase_       )
		return generated_words

@require_sentencepiece
@require_tokenizers
@require_tf
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



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


	@unittest.skip("Skipping until #12647 is resolved."       )
	@slow
	def __lowerCAmelCase      (   self       ):
		self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'en'
	__SCREAMING_SNAKE_CASE              =					'zh'
	__SCREAMING_SNAKE_CASE              =					['My name is Wolfgang and I live in Berlin']
	__SCREAMING_SNAKE_CASE              =					['我叫沃尔夫冈 我住在柏林']


	@unittest.skip("Skipping until #12647 is resolved."       )
	@slow
	def __lowerCAmelCase      (   self       ):
		self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'en'
	__SCREAMING_SNAKE_CASE              =					'ROMANCE'
	__SCREAMING_SNAKE_CASE              =					[
	    '>>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              =					[
	    'Ne passez pas autant de temps à regarder la télé.',
	    'A sua mensagem foi enviada.',
	    'Es dos años más viejo que yo.',
	]


	@unittest.skip("Skipping until #12647 is resolved."       )
	@slow
	def __lowerCAmelCase      (   self       ):
		self._assert_generated_batch_equal_expected()


	@unittest.skip("Skipping until #12647 is resolved."       )
	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =TranslationPipeline(self.model       ,  self.tokenizer       ,  framework="tf"       )
		_lowercase   =pipeline(self.src_text       )
		self.assertEqual(self.expected_text       ,  [x["translation_text"] for x in output]       )







'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase__            =     "\\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"

lowercase__            =     "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

lowercase__            =     "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"




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






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Union[str, Any]:
      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='https://github.com/mjpost/sacreBLEU'	,     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']	,     reference_urls=[
              'https://github.com/mjpost/sacreBLEU',
              'https://en.wikipedia.org/wiki/BLEU',
              'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
          ]	,     )




     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	int	,     lowercase_					:	List[Any]="exp"	,     lowercase_					:	Optional[int]=None	,     lowercase_					:	List[str]=False	,     lowercase_					:	Tuple=False	,     lowercase_					:	int=None	,     lowercase_					:	str=False	,     )		->      Dict:
      UpperCAmelCase			:      Union[str, Any]		   =					len(references[0]			)
      if any(len(lowercase_			) != references_per_prediction for refs in references			):
       raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
      UpperCAmelCase			:      Optional[Any]		   =					[[refs[i] for refs in references] for i in range(lowercase_			)]
      UpperCAmelCase			:      List[Any]		   =					scb.corpus_bleu(
          lowercase_	,     lowercase_	,     smooth_method=lowercase_	,     smooth_value=lowercase_	,     force=lowercase_	,     lowercase=lowercase_	,     use_effective_order=lowercase_	,     **({'tokenize': tokenize} if tokenize else {})	,     )
      UpperCAmelCase			:      Union[str, Any]		   =					{
          'score': output.score,
          'counts': output.counts,
          'totals': output.totals,
          'precisions': output.precisions,
          'bp': output.bp,
          'sys_len': output.sys_len,
          'ref_len': output.ref_len,
      }
      return output_dict




