

'''simple docstring'''

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 							_snake_case	(    unittest.TestCase  ):
   _A    :       Any      				=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   _A    :       Optional[Any]      				=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def       __UpperCamelCase  (	self     : str       ,SCREAMING_SNAKE_CASE__     : List[Any]       ,SCREAMING_SNAKE_CASE__     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[Any]							):
     if isinstance(model.config       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any   =   list(tokenizer.lang_code_to_id.keys()							)[:2]
       SCREAMING_SNAKE_CASE:Optional[Any]   =   TranslationPipeline(model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__       ,src_lang=SCREAMING_SNAKE_CASE__       ,tgt_lang=SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:List[str]   =   TranslationPipeline(model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__							)
     return translator, ["Some string", "Some other text"]
   def       __UpperCamelCase  (	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : int							):
     SCREAMING_SNAKE_CASE:List[Any]   =   translator("Some string"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": ANY(SCREAMING_SNAKE_CASE__							)}]							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   translator(["Some string"]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": ANY(SCREAMING_SNAKE_CASE__							)}]							)

     SCREAMING_SNAKE_CASE:int   =   translator(["Some string", "other string"]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": ANY(SCREAMING_SNAKE_CASE__							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE__							)}]							)
   @require_torch
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   pipeline("translation_en_to_ro"       ,model="patrickvonplaten/t5-tiny-random"       ,framework="pt"							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   translator("This is a test string"       ,max_length=20							)
     self.assertEqual(
         SCREAMING_SNAKE_CASE__       ,[
             {
                 "translation_text": (
                     "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                     " Beide Beide"
                 )
             }
         ]       ,)
   @require_tf
   def       __UpperCamelCase  (	self     : List[str]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   pipeline("translation_en_to_ro"       ,model="patrickvonplaten/t5-tiny-random"       ,framework="tf"							)
     SCREAMING_SNAKE_CASE:str   =   translator("This is a test string"       ,max_length=20							)
     self.assertEqual(
         SCREAMING_SNAKE_CASE__       ,[
             {
                 "translation_text": (
                     "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                     " Beide Beide"
                 )
             }
         ]       ,)
   @require_torch
   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   pipeline("translation_en_to_de"       ,model="patrickvonplaten/t5-tiny-random"       ,framework="pt"							)
     SCREAMING_SNAKE_CASE:Dict   =   translator("This is a test string"       ,max_length=20							)
     self.assertEqual(
         SCREAMING_SNAKE_CASE__       ,[
             {
                 "translation_text": (
                     "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                     " urine urine urine urine urine urine urine"
                 )
             }
         ]       ,)







   @require_tf
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   pipeline("translation_en_to_de"       ,model="patrickvonplaten/t5-tiny-random"       ,framework="tf"							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   translator("This is a test string"       ,max_length=20							)
     self.assertEqual(
         SCREAMING_SNAKE_CASE__       ,[
             {
                 "translation_text": (
                     "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                     " urine urine urine urine urine urine urine"
                 )
             }
         ]       ,)





class 							_snake_case	(    unittest.TestCase  ):
   @require_torch
   @slow
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     # We don't provide a default for this pair
     with self.assertRaises(SCREAMING_SNAKE_CASE__							):
       pipeline(task="translation_cn_to_ar"							)

     # but we do for this one
     SCREAMING_SNAKE_CASE:Optional[int]   =   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       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]   =   MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
     SCREAMING_SNAKE_CASE:List[str]   =   MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

     SCREAMING_SNAKE_CASE:Any   =   pipeline(task="translation"       ,model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__							)
     # Missing src_lang, tgt_lang
     with self.assertRaises(SCREAMING_SNAKE_CASE__							):
       translator("This is a test"							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   translator("This is a test"       ,src_lang="en_XX"       ,tgt_lang="ar_AR"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": "هذا إختبار"}]							)

     SCREAMING_SNAKE_CASE:Optional[int]   =   translator("This is a test"       ,src_lang="en_XX"       ,tgt_lang="hi_IN"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": "यह एक परीक्षण है"}]							)

     # src_lang, tgt_lang can be defined at pipeline call time
     SCREAMING_SNAKE_CASE:int   =   pipeline(task="translation"       ,model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__       ,src_lang="en_XX"       ,tgt_lang="ar_AR"							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   translator("This is a test"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,[{"translation_text": "هذا إختبار"}]							)
   @require_torch
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   'patrickvonplaten/t5-tiny-random'
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   pipeline(task="translation_cn_to_ar"       ,model=SCREAMING_SNAKE_CASE__							)
     self.assertEqual(translator._preprocess_params["src_lang"]       ,"cn"							)
     self.assertEqual(translator._preprocess_params["tgt_lang"]       ,"ar"							)
   @require_torch
   def       __UpperCamelCase  (	self     : Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]   =   'patrickvonplaten/t5-tiny-random'
     with pytest.warns(SCREAMING_SNAKE_CASE__       ,match=R".*translation_en_to_de.*"							):
       SCREAMING_SNAKE_CASE:int   =   pipeline(task="translation"       ,model=SCREAMING_SNAKE_CASE__							)
     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       __UpperCamelCase  (	self     : int							):
     with self.assertRaises(SCREAMING_SNAKE_CASE__							):
       pipeline(task="translation"							)









'''simple docstring'''

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_      =							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case	(    _a  ):
   _A    :       List[str]      				=   ['''pixel_values''']
   def __init__(	self     : int       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Union[int, float] = 1 / 255       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,**SCREAMING_SNAKE_CASE__     : int       ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple   =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:Any   =   get_size_dict(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple   =   do_resize
     SCREAMING_SNAKE_CASE:Any   =   size
     SCREAMING_SNAKE_CASE:Any   =   resample
     SCREAMING_SNAKE_CASE:Tuple   =   do_center_crop
     SCREAMING_SNAKE_CASE:List[str]   =   crop_size
     SCREAMING_SNAKE_CASE:Dict   =   do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]   =   rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   do_normalize
     SCREAMING_SNAKE_CASE:List[str]   =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Dict   =   image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Any   =   do_convert_rgb
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : str       ,):
     SCREAMING_SNAKE_CASE:List[str]   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   get_resize_output_image_size(
         SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[Any]       ,):
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__							)
     return center_crop(SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Dict       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[int, float]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[int]       ,):
     return rescale(SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     return normalize(SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)







   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : ImageInput       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : int = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : float = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, TensorType]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[ChannelDimension] = ChannelDimension.FIRST       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     SCREAMING_SNAKE_CASE:List[Any]   =   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:int   =   size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Dict   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int   =   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Optional[int]   =   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Any   =   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:Dict   =   get_size_dict(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:Any   =   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:Tuple   =   do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Tuple   =   image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Dict   =   image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE:Any   =   make_list_of_images(SCREAMING_SNAKE_CASE__							)

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

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

     if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."							)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       SCREAMING_SNAKE_CASE:Any   =   [convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:Tuple   =   [to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Dict   =   [self.resize(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_center_crop:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.center_crop(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.rescale(image=SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.normalize(image=SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int   =   {"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,tensor_type=SCREAMING_SNAKE_CASE__							)






