






"""simple docstring"""



from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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 tensorflow as tf

    from transformers import (
        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        LayoutLMvaConfig,
        TFLayoutLMvaForQuestionAnswering,
        TFLayoutLMvaForSequenceClassification,
        TFLayoutLMvaForTokenClassification,
        TFLayoutLMvaModel,
    )

if is_vision_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor

class        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=2							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=2							,		_lowerCamelCase=7							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=9_9							,		_lowerCamelCase=3_6							,		_lowerCamelCase=2							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase=1_6							,		_lowerCamelCase=2							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=6							,		_lowerCamelCase=6							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=None							,		_lowerCamelCase=1_0_0_0							,		):
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       patch_size
      lowercase							    =       is_training
      lowercase							    =       use_input_mask
      lowercase							    =       use_token_type_ids
      lowercase							    =       use_labels
      lowercase							    =       vocab_size
      lowercase							    =       hidden_size
      lowercase							    =       num_hidden_layers
      lowercase							    =       num_attention_heads
      lowercase							    =       intermediate_size
      lowercase							    =       hidden_act
      lowercase							    =       hidden_dropout_prob
      lowercase							    =       attention_probs_dropout_prob
      lowercase							    =       max_position_embeddings
      lowercase							    =       type_vocab_size
      lowercase							    =       type_sequence_label_size
      lowercase							    =       initializer_range
      lowercase							    =       coordinate_size
      lowercase							    =       shape_size
      lowercase							    =       num_labels
      lowercase							    =       num_choices
      lowercase							    =       scope
      lowercase							    =       range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      lowercase							    =       text_seq_length
      lowercase							    =       (image_size // patch_size) ** 2 + 1
      lowercase							    =       self.text_seq_length + self.image_seq_length







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length]							,		self.vocab_size       )

      lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length, 4]							,		self.range_bbox       )
      lowercase							    =       bbox.numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]       ):
        for j in range(bbox.shape[1]       ):
          if bbox[i, j, 3] < bbox[i, j, 1]:
            lowercase							    =       bbox[i, j, 3]
            lowercase							    =       bbox[i, j, 1]
            lowercase							    =       tmp_coordinate
          if bbox[i, j, 2] < bbox[i, j, 0]:
            lowercase							    =       bbox[i, j, 2]
            lowercase							    =       bbox[i, j, 0]
            lowercase							    =       tmp_coordinate
      lowercase							    =       tf.constant(_lowerCamelCase       )

      lowercase							    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      lowercase							    =       None
      if self.use_input_mask:
        lowercase							    =       random_attention_mask([self.batch_size, self.text_seq_length]       )

      lowercase							    =       None
      if self.use_token_type_ids:
        lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length]							,		self.type_vocab_size       )

      lowercase							    =       None
      lowercase							    =       None
      if self.use_labels:
        lowercase							    =       ids_tensor([self.batch_size]							,		self.type_sequence_label_size       )
        lowercase							    =       ids_tensor([self.batch_size, self.text_seq_length]							,		self.num_labels       )

      lowercase							    =       LayoutLMvaConfig(
          vocab_size=self.vocab_size							,		hidden_size=self.hidden_size							,		num_hidden_layers=self.num_hidden_layers							,		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							,		coordinate_size=self.coordinate_size							,		shape_size=self.shape_size							,		input_size=self.image_size							,		patch_size=self.patch_size							,		)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       TFLayoutLMvaModel(config=_lowerCamelCase       )

      # text + image
      lowercase							    =       model(_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		training=_lowerCamelCase       )
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		training=_lowerCamelCase							,		)
      lowercase							    =       model(_lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		training=_lowerCamelCase       )

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

      # text only
      lowercase							    =       model(_lowerCamelCase							,		training=_lowerCamelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape							,		(self.batch_size, self.text_seq_length, self.hidden_size)       )

      # image only
      lowercase							    =       model({'pixel_values': pixel_values}							,		training=_lowerCamelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape							,		(self.batch_size, self.image_seq_length, self.hidden_size)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.num_labels
      lowercase							    =       TFLayoutLMvaForSequenceClassification(config=_lowerCamelCase       )
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase							,		training=_lowerCamelCase							,		)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.num_labels
      lowercase							    =       TFLayoutLMvaForTokenClassification(config=_lowerCamelCase       )
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase							,		training=_lowerCamelCase							,		)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.text_seq_length, self.num_labels)       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       2
      lowercase							    =       TFLayoutLMvaForQuestionAnswering(config=_lowerCamelCase       )
      lowercase							    =       model(
          _lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		start_positions=_lowerCamelCase							,		end_positions=_lowerCamelCase							,		training=_lowerCamelCase							,		)
      self.parent.assertEqual(result.start_logits.shape							,		(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape							,		(self.batch_size, self.seq_length)       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.prepare_config_and_inputs()
      ((lowercase)    ,     (lowercase)    ,     (lowercase)    ,     (lowercase)    ,     (lowercase)    ,     (lowercase)    ,     (lowercase)    ,     (lowercase))							    =       config_and_inputs
      lowercase							    =       {
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict


@require_tf
class        a     (   a_,				a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =(
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase_ :     str	     =(
        {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    UpperCAmelCase_ :     Tuple	     =False
    UpperCAmelCase_ :     Union[str, Any]	     =False
    UpperCAmelCase_ :     Any	     =False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      return True







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=False       ):
      lowercase							    =       copy.deepcopy(_lowerCamelCase       )

      if model_class in get_values(_lowerCamelCase       ):
        lowercase							    =       {
            k: tf.tile(tf.expand_dims(_lowerCamelCase							,		1       )							,		(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)       )
            if isinstance(_lowerCamelCase							,		tf.Tensor       ) and v.ndim > 0
            else v
            for k, v in inputs_dict.items()
        }

      if return_labels:
        if model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       tf.ones(self.model_tester.batch_size							,		dtype=tf.intaa       )
        elif model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       tf.zeros(self.model_tester.batch_size							,		dtype=tf.intaa       )
          lowercase							    =       tf.zeros(self.model_tester.batch_size							,		dtype=tf.intaa       )
        elif model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       tf.zeros(self.model_tester.batch_size							,		dtype=tf.intaa       )
        elif model_class in get_values(_lowerCamelCase       ):
          lowercase							    =       tf.zeros(
              (self.model_tester.batch_size, self.model_tester.text_seq_length)							,		dtype=tf.intaa       )

      return inputs_dict







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       TFLayoutLMvaModelTester(self       )
      lowercase							    =       ConfigTester(self							,		config_class=_lowerCamelCase							,		hidden_size=3_7       )







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







    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
        lowercase							    =       model_class(_lowerCamelCase       )
        if getattr(_lowerCamelCase							,		'hf_compute_loss'							,		_lowerCamelCase       ):
          # The number of elements in the loss should be the same as the number of elements in the label
          lowercase							    =       self._prepare_for_class(inputs_dict.copy()							,		_lowerCamelCase							,		return_labels=_lowerCamelCase       )
          lowercase							    =       prepared_for_class[
              sorted(prepared_for_class.keys() - inputs_dict.keys()							,		reverse=_lowerCamelCase       )[0]
          ]
          lowercase							    =       added_label.shape.as_list()[:1]

          # Test that model correctly compute the loss with kwargs
          lowercase							    =       self._prepare_for_class(inputs_dict.copy()							,		_lowerCamelCase							,		return_labels=_lowerCamelCase       )
          lowercase							    =       prepared_for_class.pop('input_ids'       )

          lowercase							    =       model(_lowerCamelCase							,		**_lowerCamelCase       )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

          # Test that model correctly compute the loss when we mask some positions
          lowercase							    =       self._prepare_for_class(inputs_dict.copy()							,		_lowerCamelCase							,		return_labels=_lowerCamelCase       )
          lowercase							    =       prepared_for_class.pop('input_ids'       )
          if "labels" in prepared_for_class:
            lowercase							    =       prepared_for_class['labels'].numpy()
            if len(labels.shape       ) > 1 and labels.shape[1] != 1:
              lowercase							    =       -1_0_0
              lowercase							    =       tf.convert_to_tensor(_lowerCamelCase       )
              lowercase							    =       model(_lowerCamelCase							,		**_lowerCamelCase       )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
              self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
          lowercase							    =       self._prepare_for_class(inputs_dict.copy()							,		_lowerCamelCase							,		return_labels=_lowerCamelCase       )
          lowercase							    =       model(_lowerCamelCase       )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

          # Test that model correctly compute the loss with a tuple
          lowercase							    =       self._prepare_for_class(inputs_dict.copy()							,		_lowerCamelCase							,		return_labels=_lowerCamelCase       )

          # Get keys that were added with the _prepare_for_class function
          lowercase							    =       prepared_for_class.keys() - inputs_dict.keys()
          lowercase							    =       inspect.signature(model.call       ).parameters
          lowercase							    =       list(signature.keys()       )

          # Create a dictionary holding the location of the tensors in the tuple
          lowercase							    =       {0: 'input_ids'}
          for label_key in label_keys:
            lowercase							    =       signature_names.index(_lowerCamelCase       )
            lowercase							    =       label_key
          lowercase							    =       sorted(tuple_index_mapping.items()       )
          # Initialize a list with their default values, update the values and convert to a tuple
          lowercase							    =       []

          for name in signature_names:
            if name != "kwargs":
              list_input.append(signature[name].default       )

          for index, value in sorted_tuple_index_mapping:
            lowercase							    =       prepared_for_class[value]

          lowercase							    =       tuple(_lowerCamelCase       )

          # Send to model
          lowercase							    =       model(tuple_input[:-1]       )[0]

          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )







    def 			UpperCamelCase_      (			self       ):
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowercase							    =       type
        self.model_tester.create_and_check_model(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(
          _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(
          _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      (
          (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     (
          lowercase
      )    ,     
      )							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(
          _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )







    @slow
    def 			UpperCamelCase_      (			self       ):
      for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase							    =       TFLayoutLMvaModel.from_pretrained(_lowerCamelCase       )
        self.assertIsNotNone(_lowerCamelCase       )


def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image



@require_tf
class        a     (   unittest.TestCase	):







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      return LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase       ) if is_vision_available() else None







    @slow
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'       )

      lowercase							    =       self.default_image_processor
      lowercase							    =       prepare_img()
      lowercase							    =       image_processor(images=_lowerCamelCase							,		return_tensors='tf'       ).pixel_values

      lowercase							    =       tf.constant([[1, 2]]       )
      lowercase							    =       tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]       )							,		axis=0       )

      # forward pass
      lowercase							    =       model(input_ids=_lowerCamelCase							,		bbox=_lowerCamelCase							,		pixel_values=_lowerCamelCase							,		training=_lowerCamelCase       )

      # verify the logits
      lowercase							    =       (1, 1_9_9, 7_6_8)
      self.assertEqual(outputs.last_hidden_state.shape							,		_lowerCamelCase       )

      lowercase							    =       tf.constant(
          [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]       )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]							,		_lowerCamelCase							,		atol=1e-4       )       )










"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase		:  Tuple						=		logging.getLogger()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Path		,					__snake_case			:  list    ):





  '''simple docstring'''





  lowercase							    =       '\n'.join(__snake_case    )
  Path(__snake_case    ).open('w'    ).writelines(__snake_case    )


_UpperCamelCase		:  Union[str, Any]						=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase		:  Union[str, Any]						=		'sshleifer/bart-tiny-random'
_UpperCamelCase		:  Tuple						=		'sshleifer/tiny-mbart'

_UpperCamelCase		:  Union[str, Any]						=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        a     (   a_	):







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()
      lowercase							    =       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
      _dump_articles(_lowerCamelCase							,		_lowerCamelCase       )

      lowercase							    =       str(Path(self.get_auto_remove_tmp_dir()       ) / 'scores.json'       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        run_generate()
        assert Path(_lowerCamelCase       ).exists()
        # os.remove(Path(output_file_name))







    def 			UpperCamelCase_      (			self       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([BART_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      self.run_eval_tester(_lowerCamelCase       )







    @parameterized.expand([T5_TINY, MBART_TINY]       )
    @slow
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       ) / 'utest_input.source'
      lowercase							    =       input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      lowercase							    =       {
          'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
          'de': [
              'Maschinelles Lernen ist großartig, oder?',
              'Ich esse gerne Bananen',
              'Morgen ist wieder ein toller Tag!',
          ],
      }

      lowercase							    =       Path(self.get_auto_remove_tmp_dir()       )
      lowercase							    =       str(tmp_dir / 'scores.json'       )
      lowercase							    =       str(tmp_dir / 'val.target'       )
      _dump_articles(_lowerCamelCase							,		text['en']       )
      _dump_articles(_lowerCamelCase							,		text['de']       )
      lowercase							    =       'translation_en_to_de' if model == T5_TINY else 'summarization'
      lowercase							    =       F'\n            run_eval_search.py\n            {model}\n            {str(_lowerCamelCase       )}\n            {str(_lowerCamelCase       )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
      testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']       )

      with patch.object(_lowerCamelCase							,		'argv'							,		_lowerCamelCase       ):
        with CaptureStdout() as cs:
          run_search()
        lowercase							    =       [' num_beams | length_penalty', model, 'Best score args']
        lowercase							    =       ['Info']
        if "translation" in task:
          expected_strings.append('bleu'       )
        else:
          expected_strings.extend(_lowerCamelCase       )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(_lowerCamelCase       ).exists()
        os.remove(Path(_lowerCamelCase       )       )


