

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 ConvNextImageProcessor






class 	__magic_name__				(       unittest.TestCase							):






     def __init__(							self		,	_lowercase		,	_lowercase=7		,	_lowercase=3		,	_lowercase=18		,	_lowercase=30		,	_lowercase=400		,	_lowercase=True		,	_lowercase=None		,	_lowercase=0.875		,	_lowercase=True		,	_lowercase=[0.5, 0.5, 0.5]		,	_lowercase=[0.5, 0.5, 0.5]		,	)->    str:
         UpperCamelCase_      =	size if size is not None else {"shortest_edge": 20}
         UpperCamelCase_      =	parent
         UpperCamelCase_      =	batch_size
         UpperCamelCase_      =	num_channels
         UpperCamelCase_      =	image_size
         UpperCamelCase_      =	min_resolution
         UpperCamelCase_      =	max_resolution
         UpperCamelCase_      =	do_resize
         UpperCamelCase_      =	size
         UpperCamelCase_      =	crop_pct
         UpperCamelCase_      =	do_normalize
         UpperCamelCase_      =	image_mean
         UpperCamelCase_      =	image_std






     def   UpperCAmelCase_							(							self  )->    Dict:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






@require_torch
@require_vision
class 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			ConvNextImageProcessor if is_vision_available() else None






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	ConvNextImageProcessingTester(self  )






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()






     def   UpperCAmelCase_							(							self  )->    Any:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(_lowercase		,	"do_resize"  )  )
         self.assertTrue(hasattr(_lowercase		,	"size"  )  )
         self.assertTrue(hasattr(_lowercase		,	"crop_pct"  )  )
         self.assertTrue(hasattr(_lowercase		,	"do_normalize"  )  )
         self.assertTrue(hasattr(_lowercase		,	"image_mean"  )  )
         self.assertTrue(hasattr(_lowercase		,	"image_std"  )  )






     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 20}  )

         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict		,	size=42  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 42}  )






     def   UpperCAmelCase_							(							self  )->    Any:
         pass






     def   UpperCAmelCase_							(							self  )->    str:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	Image.Image  )

         # Test not batched input
         UpperCamelCase_      =	image_processing(image_inputs[0]		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(_lowercase		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)






     def   UpperCAmelCase_							(							self  )->    int:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	numpify=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	np.ndarray  )

         # Test not batched input
         UpperCamelCase_      =	image_processing(image_inputs[0]		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(_lowercase		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)






     def   UpperCAmelCase_							(							self  )->    List[Any]:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	torchify=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	torch.Tensor  )

         # Test not batched input
         UpperCamelCase_      =	image_processing(image_inputs[0]		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)

         # Test batched
         UpperCamelCase_      =	image_processing(_lowercase		,	return_tensors="pt"  ).pixel_values
         self.assertEqual(
             encoded_images.shape		,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )		,	)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
