



'''simple docstring'''





from __future__ import annotations

import unittest

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

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


if is_tf_available():
  import numpy
  import tensorflow as tf

  from transformers.models.esm.modeling_tf_esm import (
      TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEsmForMaskedLM,
      TFEsmForSequenceClassification,
      TFEsmForTokenClassification,
      TFEsmModel,
  )






class __A    :



 '''simple docstring'''







 def __init__(self				,     A				,     )					->  Optional[int]:


      """simple docstring"""



      _a											=						parent
      _a											=						13
      _a											=						7
      _a											=						True
      _a											=						True
      _a											=						True
      _a											=						99
      _a											=						32
      _a											=						2
      _a											=						4
      _a											=						37
      _a											=						'''gelu'''
      _a											=						0.1
      _a											=						0.1
      _a											=						512
      _a											=						16
      _a											=						2
      _a											=						0.02
      _a											=						3
      _a											=						4
      _a											=						None



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



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

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

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

      _a											=						EsmConfig(
          vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     pad_token_id=1				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     )

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



 def       a__						(self )					->  str:


      """simple docstring"""



      (
          (
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					
      )											=						self.prepare_config_and_inputs()

      _a											=						True
      _a											=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      _a											=						ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2 )

      return (
          config,
          input_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  str:


      """simple docstring"""



      _a											=						TFEsmModel(config=A )
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _a											=						model(A )

      _a											=						[input_ids, input_mask]
      _a											=						model(A )

      _a											=						model(A )

      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A				,     A				,     A				,     )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						True

      _a											=						TFEsmModel(config=A )
      _a											=						{
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''encoder_hidden_states''': encoder_hidden_states,
          '''encoder_attention_mask''': encoder_attention_mask,
      }
      _a											=						model(A )

      _a											=						[input_ids, input_mask]
      _a											=						model(A				,     encoder_hidden_states=A )

      # Also check the case where encoder outputs are not passed
      _a											=						model(A				,     attention_mask=A )

      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						TFEsmForMaskedLM(config=A )
      _a											=						model([input_ids, input_mask] )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						self.num_labels
      _a											=						TFEsmForTokenClassification(config=A )
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _a											=						model(A )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels) )



 def       a__						(self )					->  int:


      """simple docstring"""



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






@require_tf
class __A    (						A     ,     A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: str    = (
     (
         TFEsmModel,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 __lowerCamelCase					: Union[str, Any]    = (
     {
         'feature-extraction': TFEsmModel,
         'fill-mask': TFEsmForMaskedLM,
         'text-classification': TFEsmForSequenceClassification,
         'token-classification': TFEsmForTokenClassification,
         'zero-shot': TFEsmForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowerCamelCase					: Dict    = False
 __lowerCamelCase					: Tuple    = False



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						TFEsmModelTester(self )
      _a											=						ConfigTester(self				,     config_class=A				,     hidden_size=37 )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*A )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A )



 @slow
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a											=						TFEsmModel.from_pretrained(A )
           self.assertIsNotNone(A )



 @unittest.skip('''Protein models do not support embedding resizing.''' )
 def       a__						(self )					->  str:


      """simple docstring"""



      pass



 @unittest.skip('''Protein models do not support embedding resizing.''' )
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      pass



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a											=						model_class(A )
           assert isinstance(model.get_input_embeddings()				,     tf.keras.layers.Layer )
           if model_class is TFEsmForMaskedLM:
                # Output embedding test differs from the main test because they're a matrix, not a layer
                _a											=						model.get_bias()
                assert isinstance(A				,     A )
                for k, v in name.items():
                     assert isinstance(A				,     tf.Variable )
           else:
                _a											=						model.get_output_embeddings()
                assert x is None
                _a											=						model.get_bias()
                assert name is None






@require_tf
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @slow
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )

      _a											=						tf.constant([[0, 1, 2, 3, 4, 5]] )
      _a											=						model(A )[0]
      _a											=						[1, 6, 33]
      self.assertEqual(list(output.numpy().shape )				,     A )
      # compare the actual values for a slice.
      _a											=						tf.constant(
          [
              [
                  [8.921518, -10.589814, -6.4671307],
                  [-6.3967156, -13.911377, -1.1211915],
                  [-7.781247, -13.951557, -3.740592],
              ]
          ] )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-2 ) )



 @slow
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )

      _a											=						tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
      _a											=						model(A )[0]
      # compare the actual values for a slice.
      _a											=						tf.constant(
          [
              [
                  [0.14443092, 0.54125327, 0.3247739],
                  [0.30340484, 0.00526676, 0.31077722],
                  [0.32278043, -0.24987096, 0.3414628],
              ]
          ] )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-4 ) )








'''simple docstring'''





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase_	 =						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

lowercase_	 =						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

lowercase_	 =						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"






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



 '''simple docstring'''







 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' )				,     id='''references''' ),
              } )				,     codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']				,     reference_urls=[
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ]				,     )



 def       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a))											=						score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }



