


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase__        =				logging.get_logger(__name__)







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     ['''pixel_values''']





     def __init__(self						,	UpperCAmelCase = True						,	UpperCAmelCase = None						,	UpperCAmelCase = True						,	UpperCAmelCase = None						,	UpperCAmelCase = PILImageResampling.BICUBIC						,	UpperCAmelCase = True						,	UpperCAmelCase = 1 / 2_5_5						,	UpperCAmelCase = True						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  None:
      super().__init__(**UpperCAmelCase			)
      _lowercase				 =crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
      _lowercase				 =get_size_dict(UpperCAmelCase						,	param_name='''crop_size'''			)
      _lowercase				 =size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      _lowercase				 =get_size_dict(UpperCAmelCase			)

      _lowercase				 =do_center_crop
      _lowercase				 =crop_size
      _lowercase				 =do_resize
      _lowercase				 =size
      _lowercase				 =resample
      _lowercase				 =do_rescale
      _lowercase				 =rescale_factor
      _lowercase				 =do_normalize
      _lowercase				 =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _lowercase				 =image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  np.ndarray:
      _lowercase				 =self.size if size is None else size
      _lowercase				 =get_size_dict(UpperCAmelCase			)
      _lowercase				 =get_size_dict(UpperCAmelCase						,	param_name='''crop_size'''			)

      _lowercase ,       _lowercase				 =get_image_size(UpperCAmelCase			)
      _lowercase				 =min(UpperCAmelCase						,	UpperCAmelCase			)
      _lowercase				 =(size['''height'''] / crop_size['''height''']) * min_dim
      _lowercase				 =(size['''width'''] / crop_size['''width''']) * min_dim
      return center_crop(UpperCAmelCase						,	size=(cropped_height, cropped_width)						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = PIL.Image.BICUBIC						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  np.ndarray:
      _lowercase				 =get_size_dict(UpperCAmelCase			)
      if "height" not in size or "width" not in size:
       raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"			)
      return resize(
          UpperCAmelCase						,	size=(size['''height'''], size['''width'''])						,	resample=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  int:
      return rescale(UpperCAmelCase						,	scale=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  np.ndarray:
      return normalize(UpperCAmelCase						,	mean=UpperCAmelCase						,	std=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = ChannelDimension.FIRST						,	**UpperCAmelCase						,	)	->  PIL.Image.Image:
      _lowercase				 =do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowercase				 =crop_size if crop_size is not None else self.crop_size
      _lowercase				 =get_size_dict(UpperCAmelCase						,	param_name='''crop_size'''			)
      _lowercase				 =do_resize if do_resize is not None else self.do_resize
      _lowercase				 =size if size is not None else self.size
      _lowercase				 =get_size_dict(UpperCAmelCase			)
      _lowercase				 =resample if resample is not None else self.resample
      _lowercase				 =do_rescale if do_rescale is not None else self.do_rescale
      _lowercase				 =rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowercase				 =do_normalize if do_normalize is not None else self.do_normalize
      _lowercase				 =image_mean if image_mean is not None else self.image_mean
      _lowercase				 =image_std if image_std is not None else self.image_std

      _lowercase				 =make_list_of_images(UpperCAmelCase			)

      if not valid_images(UpperCAmelCase			):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

      if do_center_crop and crop_size is None:
       raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''			)

      if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.'''			)

      if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

      if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''			)

      # All transformations expect numpy arrays.
      _lowercase				 =[to_numpy_array(UpperCAmelCase			) for image in images]

      if do_center_crop:
       _lowercase				 =[self.center_crop(UpperCAmelCase						,	UpperCAmelCase						,	size=UpperCAmelCase			) for image in images]

      if do_resize:
       _lowercase				 =[self.resize(image=UpperCAmelCase						,	size=UpperCAmelCase						,	resample=UpperCAmelCase			) for image in images]

      if do_rescale:
       _lowercase				 =[self.rescale(image=UpperCAmelCase						,	scale=UpperCAmelCase			) for image in images]

      if do_normalize:
       _lowercase				 =[self.normalize(image=UpperCAmelCase						,	mean=UpperCAmelCase						,	std=UpperCAmelCase			) for image in images]

      _lowercase				 =[to_channel_dimension_format(UpperCAmelCase						,	UpperCAmelCase			) for image in images]

      _lowercase				 ={'''pixel_values''': images}
      return BatchFeature(data=UpperCAmelCase						,	tensor_type=UpperCAmelCase			)





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class  lowerCamelCase__  (			unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     SCREAMING_SNAKE_CASE__      =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      if isinstance(model.config						,	UpperCAmelCase			):
       _lowercase ,       _lowercase				 =list(tokenizer.lang_code_to_id.keys()			)[:2]
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang=UpperCAmelCase						,	tgt_lang=UpperCAmelCase			)
      else:
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      return translator, ["Some string", "Some other text"]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =translator('''Some string'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''', '''other string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}, {'''translation_text''': ANY(UpperCAmelCase			)}]			)





     @require_torch
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)







class  lowerCamelCase__  (			unittest.TestCase):





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      # We don't provide a default for this pair
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation_cn_to_ar'''			)

      # but we do for this one
      _lowercase				 =pipeline(task='''translation_en_to_de'''			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''en'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''de'''			)





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)
      _lowercase				 =MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)

      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      # Missing src_lang, tgt_lang
      with self.assertRaises(UpperCAmelCase			):
       translator('''This is a test'''			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''hi_IN'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''यह एक परीक्षण है'''}]			)

      # src_lang, tgt_lang can be defined at pipeline call time
      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      _lowercase				 =translator('''This is a test'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)





     @require_torch
     def 	__A   (self			)	->  Tuple:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      _lowercase				 =pipeline(task='''translation_cn_to_ar'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''cn'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''ar'''			)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      with pytest.warns(UpperCAmelCase						,	match=R'''.*translation_en_to_de.*'''			):
       _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator.task						,	'''translation_en_to_de'''			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''en'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''de'''			)





     @require_torch
     def 	__A   (self			)	->  List[str]:
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation'''			)

