





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets


class    __SCREAMING_SNAKE_CASE			( UpperCamelCase__					):







       @slow
       @require_torch
       def 						__lowerCamelCase    (      self     :	str			)   ->Dict:
          lowerCamelCase__   :						int					   =							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,					'''prajjwal1/bert-tiny'''			)
          lowerCamelCase__   :						Tuple					   =							BertTokenizer.from_pretrained('''bert-base-uncased'''			)

          lowerCamelCase__   :						Dict					   =							bertabert.config.encoder.vocab_size
          lowerCamelCase__   :						List[str]					   =							tokenizer.sep_token_id
          lowerCamelCase__   :						Dict					   =							tokenizer.cls_token_id
          lowerCamelCase__   :						List[Any]					   =							1_2_8

          lowerCamelCase__   :						Tuple					   =							datasets.load_dataset('''cnn_dailymail'''		,					'''3.0.0'''		,					split='''train[:1%]'''			)
          lowerCamelCase__   :						List[str]					   =							datasets.load_dataset('''cnn_dailymail'''		,					'''3.0.0'''		,					split='''validation[:1%]'''			)

          lowerCamelCase__   :						Union[str, Any]					   =							train_dataset.select(range(3_2			)			)
          lowerCamelCase__   :						str					   =							val_dataset.select(range(1_6			)			)

          lowerCamelCase__   :						Dict					   =							4

          def _map_to_encoder_decoder_inputs(A     :	str			):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             lowerCamelCase__   :						List[Any]					   =							tokenizer(batch['''article''']		,					padding='''max_length'''		,					truncation=_a		,					max_length=5_1_2			)
             lowerCamelCase__   :						int					   =							tokenizer(batch['''highlights''']		,					padding='''max_length'''		,					truncation=_a		,					max_length=1_2_8			)
             lowerCamelCase__   :						Union[str, Any]					   =							inputs.input_ids
             lowerCamelCase__   :						List[Any]					   =							inputs.attention_mask

             lowerCamelCase__   :						Dict					   =							outputs.input_ids
             lowerCamelCase__   :						str					   =							outputs.input_ids.copy()
             lowerCamelCase__   :						Union[str, Any]					   =							[
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
             ]
             lowerCamelCase__   :						Dict					   =							outputs.attention_mask

             assert all(len(_a			) == 5_1_2 for x in inputs.input_ids			)
             assert all(len(_a			) == 1_2_8 for x in outputs.input_ids			)

             return batch

          def _compute_metrics(A     :	Optional[Any]			):
             lowerCamelCase__   :						int					   =							pred.label_ids
             lowerCamelCase__   :						str					   =							pred.predictions

             # all unnecessary tokens are removed
             lowerCamelCase__   :						Dict					   =							tokenizer.batch_decode(_a		,					skip_special_tokens=_a			)
             lowerCamelCase__   :						Optional[Any]					   =							tokenizer.batch_decode(_a		,					skip_special_tokens=_a			)

             lowerCamelCase__   :						Tuple					   =							sum([int(pred_str[i] == label_str[i]			) for i in range(len(_a			)			)]			) / len(_a			)

             return {"accuracy": accuracy}

          # map train dataset
          lowerCamelCase__   :						Dict					   =							train_dataset.map(
              _map_to_encoder_decoder_inputs		,					batched=_a		,					batch_size=_a		,					remove_columns=['''article''', '''highlights''']		,					)
          train_dataset.set_format(
              type='''torch'''		,					columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,					)

          # same for validation dataset
          lowerCamelCase__   :						Optional[int]					   =							val_dataset.map(
              _map_to_encoder_decoder_inputs		,					batched=_a		,					batch_size=_a		,					remove_columns=['''article''', '''highlights''']		,					)
          val_dataset.set_format(
              type='''torch'''		,					columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,					)

          lowerCamelCase__   :						Optional[int]					   =							self.get_auto_remove_tmp_dir()

          lowerCamelCase__   :						Any					   =							SeqaSeqTrainingArguments(
              output_dir=_a		,					per_device_train_batch_size=_a		,					per_device_eval_batch_size=_a		,					predict_with_generate=_a		,					evaluation_strategy='''steps'''		,					do_train=_a		,					do_eval=_a		,					warmup_steps=0		,					eval_steps=2		,					logging_steps=2		,					)

          # instantiate trainer
          lowerCamelCase__   :						Optional[Any]					   =							SeqaSeqTrainer(
              model=_a		,					args=_a		,					compute_metrics=_compute_metrics		,					train_dataset=_a		,					eval_dataset=_a		,					tokenizer=_a		,					)

          # start training
          trainer.train()









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DonutImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=False		,     _a=True		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Dict:
           _A							:     str			=						parent
           _A							:     int			=						batch_size
           _A							:     Optional[int]			=						num_channels
           _A							:     List[Any]			=						image_size
           _A							:     int			=						min_resolution
           _A							:     Optional[int]			=						max_resolution
           _A							:     Any			=						do_resize
           _A							:     List[str]			=						size if size is not None else {"""height""": 18, """width""": 20}
           _A							:     Optional[int]			=						do_thumbnail
           _A							:     str			=						do_align_axis
           _A							:     List[Any]			=						do_pad
           _A							:     Optional[Any]			=						do_normalize
           _A							:     Tuple			=						image_mean
           _A							:     List[str]			=						image_std



     def 					a__	(  self				)						->							Optional[int]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_thumbnail": self.do_thumbnail,
               "do_align_long_axis": self.do_align_axis,
               "do_pad": self.do_pad,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DonutImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						DonutImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_thumbnail"""				)				)
           self.assertTrue(hasattr(_a		,     """do_align_long_axis"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 20}				)

           _A							:     Optional[int]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=42				)
           self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42}				)

           # Previous config had dimensions in (width, height) order
           _A							:     List[str]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=(42, 84)				)
           self.assertEqual(image_processor.size		,     {"""height""": 84, """width""": 42}				)

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

     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Dict			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     Any			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

     @is_flaky()
     def 					a__	(  self				)						->							Dict:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     int			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     List[str]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )



     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Optional[int]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     Any			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     str			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

