

import random
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():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      _SCREAMING_SNAKE_CASE          = _SCREAMING_SNAKE_CASE          = _SCREAMING_SNAKE_CASE          = _SCREAMING_SNAKE_CASE          = None


class     lowerCamelCase_   ( unittest.TestCase):




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=30     ,     _UpperCAmelCase=400     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=1 / 255     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=FLAVA_IMAGE_MEAN     ,     _UpperCAmelCase=FLAVA_IMAGE_STD     ,     _UpperCAmelCase=14     ,     _UpperCAmelCase=75     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=16     ,     _UpperCAmelCase=0.3     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=FLAVA_CODEBOOK_MEAN     ,     _UpperCAmelCase=FLAVA_CODEBOOK_STD     ,     ):
   __UpperCAmelCase  :     int		=    size if size is not None else {'height': 224, 'width': 224}
   __UpperCAmelCase  :     Tuple		=    crop_size if crop_size is not None else {'height': 224, 'width': 224}
   __UpperCAmelCase  :     Any		=    codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
   __UpperCAmelCase  :     List[str]		=    codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

   __UpperCAmelCase  :     Optional[int]		=    parent
   __UpperCAmelCase  :     Dict		=    batch_size
   __UpperCAmelCase  :     int		=    num_channels
   __UpperCAmelCase  :     List[str]		=    do_resize
   __UpperCAmelCase  :     Optional[int]		=    do_rescale
   __UpperCAmelCase  :     Optional[Any]		=    rescale_factor
   __UpperCAmelCase  :     str		=    min_resolution
   __UpperCAmelCase  :     Union[str, Any]		=    max_resolution
   __UpperCAmelCase  :     List[Any]		=    size
   __UpperCAmelCase  :     int		=    resample if resample is not None else PILImageResampling.BICUBIC
   __UpperCAmelCase  :     Optional[Any]		=    do_normalize
   __UpperCAmelCase  :     int		=    image_mean
   __UpperCAmelCase  :     int		=    image_std
   __UpperCAmelCase  :     Optional[int]		=    do_center_crop
   __UpperCAmelCase  :     List[str]		=    crop_size

   __UpperCAmelCase  :     int		=    input_size_patches
   __UpperCAmelCase  :     Union[str, Any]		=    total_mask_patches
   __UpperCAmelCase  :     Optional[int]		=    mask_group_max_patches
   __UpperCAmelCase  :     Dict		=    mask_group_min_patches
   __UpperCAmelCase  :     Any		=    mask_group_min_aspect_ratio
   __UpperCAmelCase  :     List[str]		=    mask_group_max_aspect_ratio

   __UpperCAmelCase  :     Optional[int]		=    codebook_do_resize
   __UpperCAmelCase  :     Optional[int]		=    codebook_size
   __UpperCAmelCase  :     Dict		=    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
   __UpperCAmelCase  :     int		=    codebook_do_center_crop
   __UpperCAmelCase  :     Optional[int]		=    codebook_crop_size
   __UpperCAmelCase  :     List[str]		=    codebook_do_map_pixels
   __UpperCAmelCase  :     List[str]		=    codebook_do_normalize
   __UpperCAmelCase  :     Optional[Any]		=    codebook_image_mean
   __UpperCAmelCase  :     Union[str, Any]		=    codebook_image_std




 def 						a					( self       ):
   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
       "resample": self.resample,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "input_size_patches": self.input_size_patches,
       "total_mask_patches": self.total_mask_patches,
       "mask_group_max_patches": self.mask_group_max_patches,
       "mask_group_min_patches": self.mask_group_min_patches,
       "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
       "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
       "codebook_do_resize": self.codebook_do_resize,
       "codebook_size": self.codebook_size,
       "codebook_resample": self.codebook_resample,
       "codebook_do_center_crop": self.codebook_do_center_crop,
       "codebook_crop_size": self.codebook_crop_size,
       "codebook_do_map_pixels": self.codebook_do_map_pixels,
       "codebook_do_normalize": self.codebook_do_normalize,
       "codebook_image_mean": self.codebook_image_mean,
       "codebook_image_std": self.codebook_image_std,
   }




 def 						a					( self       ):
   return (self.size["height"], self.size["width"])




 def 						a					( self       ):
   return (
       (self.input_size_patches, self.input_size_patches)
       if not isinstance(self.input_size_patches     ,     _UpperCAmelCase       )
       else self.input_size_patches
   )




 def 						a					( self       ):
   return (self.codebook_size["height"], self.codebook_size["width"])



@require_torch
@require_vision
class     lowerCamelCase_   ( _lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  FlavaImageProcessor if is_vision_available() else None
 _lowerCAmelCase       		=  None




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    FlavaImageProcessingTester(self       )




 @property
 def 						a					( self       ):
   return self.image_processor_tester.prepare_image_processor_dict()




 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'image_mean'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'image_std'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_normalize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_resize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'resample'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'crop_size'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_center_crop'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_rescale'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'rescale_factor'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'masking_generator'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_do_resize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_size'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_resample'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_do_center_crop'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_crop_size'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_do_map_pixels'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_do_normalize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_image_mean'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'codebook_image_std'       )       )




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size     ,     {'height': 224, 'width': 224}       )
   self.assertEqual(image_processor.crop_size     ,     {'height': 224, 'width': 224}       )
   self.assertEqual(image_processor.codebook_size     ,     {'height': 112, 'width': 112}       )
   self.assertEqual(image_processor.codebook_crop_size     ,     {'height': 112, 'width': 112}       )

   __UpperCAmelCase  :     Optional[Any]		=    self.image_processing_class.from_dict(
       self.image_processor_dict     ,     size=42     ,     crop_size=84     ,     codebook_size=33     ,     codebook_crop_size=66       )
   self.assertEqual(image_processor.size     ,     {'height': 42, 'width': 42}       )
   self.assertEqual(image_processor.crop_size     ,     {'height': 84, 'width': 84}       )
   self.assertEqual(image_processor.codebook_size     ,     {'height': 33, 'width': 33}       )
   self.assertEqual(image_processor.codebook_crop_size     ,     {'height': 66, 'width': 66}       )




 def 						a					( self       ):
   pass




 def 						a					( self       ):
   # Initialize image_processing
   __UpperCAmelCase  :     Union[str, Any]		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __UpperCAmelCase  :     Optional[int]		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     PIL.Image.Image       )

   # Test not batched input
   __UpperCAmelCase  :     Union[str, Any]		=    image_processing(image_inputs[0]     ,     return_tensors='pt'       )

   # Test no bool masked pos
   self.assertFalse('bool_masked_pos' in encoded_images       )

   __UpperCAmelCase, __UpperCAmelCase  :     str		=    self.image_processor_tester.get_expected_image_size()

   self.assertEqual(
       encoded_images.pixel_values.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

   # Test batched
   __UpperCAmelCase  :     Union[str, Any]		=    image_processing(_UpperCAmelCase     ,     return_tensors='pt'       )
   __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    self.image_processor_tester.get_expected_image_size()

   # Test no bool masked pos
   self.assertFalse('bool_masked_pos' in encoded_images       )

   self.assertEqual(
       encoded_images.pixel_values.shape     ,     (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   # Initialize image_processing
   __UpperCAmelCase  :     Optional[Any]		=    self.image_processing_class(**self.image_processor_dict       )
   # create random tensors
   __UpperCAmelCase  :     int		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase     ,     **_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     _UpperCAmelCase       )

   # Test not batched input
   __UpperCAmelCase  :     Optional[int]		=    image_processing(image_inputs[0]     ,     return_tensors='pt'       )

   __UpperCAmelCase, __UpperCAmelCase  :     Tuple		=    self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

   __UpperCAmelCase  :     Tuple		=    image_processing(_UpperCAmelCase     ,     return_image_mask=_UpperCAmelCase     ,     return_tensors='pt'       )

   __UpperCAmelCase, __UpperCAmelCase  :     Tuple		=    self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape     ,     (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )

   __UpperCAmelCase, __UpperCAmelCase  :     Any		=    self.image_processor_tester.get_expected_mask_size()
   self.assertEqual(
       encoded_images.bool_masked_pos.shape     ,     (
           self.image_processor_tester.batch_size,
           expected_height,
           expected_width,
       )     ,     )

   # Test batched
   __UpperCAmelCase  :     int		=    image_processing(_UpperCAmelCase     ,     return_tensors='pt'       ).pixel_values

   __UpperCAmelCase, __UpperCAmelCase  :     Optional[Any]		=    self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.shape     ,     (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )

   # Test masking
   __UpperCAmelCase  :     List[str]		=    image_processing(_UpperCAmelCase     ,     return_image_mask=_UpperCAmelCase     ,     return_tensors='pt'       )

   __UpperCAmelCase, __UpperCAmelCase  :     Dict		=    self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape     ,     (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )

   __UpperCAmelCase, __UpperCAmelCase  :     Dict		=    self.image_processor_tester.get_expected_mask_size()
   self.assertEqual(
       encoded_images.bool_masked_pos.shape     ,     (
           self.image_processor_tester.batch_size,
           expected_height,
           expected_width,
       )     ,     )




 def 						a					( self       ):
   self._test_call_framework(np.ndarray     ,     prepare_kwargs={'numpify': True}       )




 def 						a					( self       ):
   self._test_call_framework(torch.Tensor     ,     prepare_kwargs={'torchify': True}       )




 def 						a					( self       ):
   # Initialize image_processing
   random.seed(1_234       )
   __UpperCAmelCase  :     Dict		=    self.image_processing_class(**self.image_processor_dict       )
   __UpperCAmelCase  :     Optional[Any]		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase     ,     torchify=_UpperCAmelCase       )

   # Test not batched input
   __UpperCAmelCase  :     Any		=    image_processing(image_inputs[0]     ,     return_image_mask=_UpperCAmelCase     ,     return_tensors='pt'       )
   self.assertEqual(encoded_images.bool_masked_pos.sum().item()     ,     75       )




 def 						a					( self       ):
   # Initialize image_processing
   __UpperCAmelCase  :     List[str]		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __UpperCAmelCase  :     Optional[int]		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     PIL.Image.Image       )

   # Test not batched input
   __UpperCAmelCase  :     int		=    image_processing(image_inputs[0]     ,     return_codebook_pixels=_UpperCAmelCase     ,     return_tensors='pt'       )
   __UpperCAmelCase, __UpperCAmelCase  :     Any		=    self.image_processor_tester.get_expected_codebook_image_size()
   self.assertEqual(
       encoded_images.codebook_pixel_values.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

   # Test batched
   __UpperCAmelCase  :     Union[str, Any]		=    image_processing(_UpperCAmelCase     ,     return_codebook_pixels=_UpperCAmelCase     ,     return_tensors='pt'       )
   __UpperCAmelCase, __UpperCAmelCase  :     List[Any]		=    self.image_processor_tester.get_expected_codebook_image_size()
   self.assertEqual(
       encoded_images.codebook_pixel_values.shape     ,     (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )     ,     )








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





