



'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

@is_pipeline_test
class 							a__( unittest.TestCase     ):
    lowercase__			=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    lowercase__			=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  int						,      __snake_case			:  Any     ):
      a						: Union[str, Any]					       =					TextaTextGenerationPipeline(model=__snake_case						,      tokenizer=__snake_case     )
      return generator, ["Something to write", "Something else"]







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]     ):
      a						: List[str]					       =					generator('Something there'     )
      self.assertEqual(__snake_case						,      [{'generated_text': ANY(__snake_case     )}]     )
      # These are encoder decoder, they don't just append to incoming string
      self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

      a						: Optional[Any]					       =					generator(['This is great !', 'Something else']						,      num_return_sequences=2						,      do_sample=__snake_case     )
      self.assertEqual(
          __snake_case						,      [
              [{'generated_text': ANY(__snake_case     )}, {'generated_text': ANY(__snake_case     )}],
              [{'generated_text': ANY(__snake_case     )}, {'generated_text': ANY(__snake_case     )}],
          ]						,      )

      a						: int					       =					generator(
          ['This is great !', 'Something else']						,      num_return_sequences=2						,      batch_size=2						,      do_sample=__snake_case     )
      self.assertEqual(
          __snake_case						,      [
              [{'generated_text': ANY(__snake_case     )}, {'generated_text': ANY(__snake_case     )}],
              [{'generated_text': ANY(__snake_case     )}, {'generated_text': ANY(__snake_case     )}],
          ]						,      )

      with self.assertRaises(__snake_case     ):
        generator(4     )







    @require_torch
    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Union[str, Any]					       =					pipeline('text2text-generation'						,      model='patrickvonplaten/t5-tiny-random'						,      framework='pt'     )
      # do_sample=False necessary for reproducibility
      a						: Optional[int]					       =					generator('Something there'						,      do_sample=__snake_case     )
      self.assertEqual(__snake_case						,      [{'generated_text': ''}]     )

      a						: Union[str, Any]					       =					3
      a						: List[Any]					       =					generator(
          'Something there'						,      num_return_sequences=__snake_case						,      num_beams=__snake_case						,      )
      a						: Optional[Any]					       =					[
          {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
          {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
          {'generated_text': ''},
      ]
      self.assertEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					generator('This is a test'						,      do_sample=__snake_case						,      num_return_sequences=2						,      return_tensors=__snake_case     )
      self.assertEqual(
          __snake_case						,      [
              {'generated_token_ids': ANY(torch.Tensor     )},
              {'generated_token_ids': ANY(torch.Tensor     )},
          ]						,      )
      a						: Tuple					       =					generator.model.config.eos_token_id
      a						: List[Any]					       =					'<pad>'
      a						: Optional[Any]					       =					generator(
          ['This is a test', 'This is a second test']						,      do_sample=__snake_case						,      num_return_sequences=2						,      batch_size=2						,      return_tensors=__snake_case						,      )
      self.assertEqual(
          __snake_case						,      [
              [
                  {'generated_token_ids': ANY(torch.Tensor     )},
                  {'generated_token_ids': ANY(torch.Tensor     )},
              ],
              [
                  {'generated_token_ids': ANY(torch.Tensor     )},
                  {'generated_token_ids': ANY(torch.Tensor     )},
              ],
          ]						,      )







    @require_tf
    def       lowercase_	(				self			:  Optional[int]     ):
      a						: str					       =					pipeline('text2text-generation'						,      model='patrickvonplaten/t5-tiny-random'						,      framework='tf'     )
      # do_sample=False necessary for reproducibility
      a						: Dict					       =					generator('Something there'						,      do_sample=__snake_case     )
      self.assertEqual(__snake_case						,      [{'generated_text': ''}]     )




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )