


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 PoolFormerImageProcessor

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=7      , __snake_case=3      , __snake_case=3_0      , __snake_case=4_0_0      , __snake_case=True      , __snake_case=None      , __snake_case=0.9      , __snake_case=None      , __snake_case=True      , __snake_case=[0.5, 0.5, 0.5]      , __snake_case=[0.5, 0.5, 0.5]      , ):
         snake_case					=    size if size is not None else {'''shortest_edge''': 3_0}
         snake_case					=    crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    num_channels
         snake_case					=    min_resolution
         snake_case					=    max_resolution
         snake_case					=    do_resize_and_center_crop
         snake_case					=    size
         snake_case					=    crop_pct
         snake_case					=    crop_size
         snake_case					=    do_normalize
         snake_case					=    image_mean
         snake_case					=    image_std




    def 		a_      (	self      ):
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }






@require_torch
@require_vision
class   A__ (   lowerCAmelCase__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		PoolFormerImageProcessor if is_vision_available() else None
    def 		a_      (	self      ):
         snake_case					=    PoolFormerImageProcessingTester(self      )
    @property
    def 		a_      (	self      ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 		a_      (	self      ):
         snake_case					=    self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''do_resize_and_center_crop'''      )      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''size'''      )      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''crop_pct'''      )      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''do_normalize'''      )      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''image_mean'''      )      )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , '''image_std'''      )      )
    def 		a_      (	self      ):
         snake_case					=    self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size      , {'''shortest_edge''': 3_0}      )
         self.assertEqual(image_processor.crop_size      , {'''height''': 3_0, '''width''': 3_0}      )

         snake_case					=    self.image_processing_class.from_dict(self.image_processor_dict      , size=4_2      , crop_size=8_4      )
         self.assertEqual(image_processor.size      , {'''shortest_edge''': 4_2}      )
         self.assertEqual(image_processor.crop_size      , {'''height''': 8_4, '''width''': 8_4}      )
    def 		a_      (	self      ):
         pass
    def 		a_      (	self      ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         snake_case					=    prepare_image_inputs(self.image_processor_tester      , equal_resolution=_SCREAMING_SNAKE_CASE      )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      , Image.Image      )

         # Test not batched input
         snake_case					=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )

         # Test batched
         snake_case					=    image_processing(_SCREAMING_SNAKE_CASE      , 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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )
    def 		a_      (	self      ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         snake_case					=    prepare_image_inputs(self.image_processor_tester      , equal_resolution=_SCREAMING_SNAKE_CASE      , numpify=_SCREAMING_SNAKE_CASE      )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      , np.ndarray      )

         # Test not batched input
         snake_case					=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )

         # Test batched
         snake_case					=    image_processing(_SCREAMING_SNAKE_CASE      , 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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )




    def 		a_      (	self      ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         snake_case					=    prepare_image_inputs(self.image_processor_tester      , equal_resolution=_SCREAMING_SNAKE_CASE      , torchify=_SCREAMING_SNAKE_CASE      )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      , torch.Tensor      )

         # Test not batched input
         snake_case					=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )

         # Test batched
         snake_case					=    image_processing(_SCREAMING_SNAKE_CASE      , 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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )      , )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE      :			int      				=   {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TimesformerModel",
     "TimesformerForVideoClassification",
     "TimesformerPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_timesformer import (
      TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TimesformerForVideoClassification,
      TimesformerModel,
      TimesformerPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			int      				=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




