

import unittest

import numpy as np

from transformers.testing_utils import 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 ViTImageProcessor


class  A						(							unittest.TestCase ):







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       str,lowercase_ :       List[str]=1_3,lowercase_ :       Optional[int]=3,lowercase_ :       int=2_2_4,lowercase_ :       Tuple=3_0,lowercase_ :       int=4_0_0,lowercase_ :       Union[str, Any]=True,lowercase_ :       List[str]=None,lowercase_ :       Any=True,lowercase_ :       Optional[Any]=[0.5, 0.5, 0.5],lowercase_ :       List[str]=[0.5, 0.5, 0.5],)->    Tuple:





        '''simple docstring'''







        A__          =   size if size is not None else {"height": 1_8, "width": 1_8}
        A__          =   parent
        A__          =   batch_size
        A__          =   num_channels
        A__          =   image_size
        A__          =   min_resolution
        A__          =   max_resolution
        A__          =   do_resize
        A__          =   size
        A__          =   do_normalize
        A__          =   image_mean
        A__          =   image_std


    def 						snake_case__     (      self :       int		)->    int:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }




@require_torch
@require_vision
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							ViTImageProcessor if is_vision_available() else None


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





        '''simple docstring'''







        A__          =   EfficientFormerImageProcessorTester(self		)


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Optional[Any]:





        '''simple docstring'''







        return self.image_proc_tester.prepare_image_processor_dict()


    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        A__          =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(snake_case__,'image_mean'		)		)
        self.assertTrue(hasattr(snake_case__,'image_std'		)		)
        self.assertTrue(hasattr(snake_case__,'do_normalize'		)		)
        self.assertTrue(hasattr(snake_case__,'do_resize'		)		)
        self.assertTrue(hasattr(snake_case__,'size'		)		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__          =   prepare_image_inputs(self.image_proc_tester,equal_resolution=snake_case__		)
        for image in image_inputs:
            self.assertIsInstance(snake_case__,Image.Image		)

        # Test not batched input
        A__          =   image_processor(image_inputs[0],return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)

        # Test batched
        A__          =   image_processor(snake_case__,return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)


    def 						snake_case__     (      self :       Optional[int]		)->    List[Any]:





        '''simple docstring'''







        A__          =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__          =   prepare_image_inputs(self.image_proc_tester,equal_resolution=snake_case__,numpify=snake_case__		)
        for image in image_inputs:
            self.assertIsInstance(snake_case__,np.ndarray		)

        # Test not batched input
        A__          =   image_processor(image_inputs[0],return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)

        # Test batched
        A__          =   image_processor(snake_case__,return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)


    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        A__          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__          =   prepare_image_inputs(self.image_proc_tester,equal_resolution=snake_case__,torchify=snake_case__		)
        for image in image_inputs:
            self.assertIsInstance(snake_case__,torch.Tensor		)

        # Test not batched input
        A__          =   image_processor(image_inputs[0],return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)

        # Test batched
        A__          =   image_processor(snake_case__,return_tensors='pt'		).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            ),)





"""simple docstring"""







import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





