




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 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=13		,     _a=3		,     _a=224		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Tuple:
           _A							:     Dict			=						size if size is not None else {"""height""": 18, """width""": 18}
           _A							:     Tuple			=						parent
           _A							:     List[Any]			=						batch_size
           _A							:     Any			=						num_channels
           _A							:     Dict			=						image_size
           _A							:     str			=						min_resolution
           _A							:     int			=						max_resolution
           _A							:     Union[str, Any]			=						do_resize
           _A							:     Any			=						size
           _A							:     Tuple			=						do_normalize
           _A							:     int			=						image_mean
           _A							:     Optional[int]			=						image_std



     def 					a__	(  self				)						->							str:
           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 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						ViTImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Optional[int]			=						EfficientFormerImageProcessorTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_proc_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)

     def 					a__	(  self				)						->							Tuple:
           pass

     def 					a__	(  self				)						->							Tuple:
           # Initialize image_processor
           _A							:     List[str]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Optional[Any]			=						prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     List[Any]			=						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							:     List[str]			=						image_processor(_a		,     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 					a__	(  self				)						->							Union[str, Any]:
           # Initialize image_processor
           _A							:     Optional[int]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Dict			=						prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     Union[str, Any]			=						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							:     Dict			=						image_processor(_a		,     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 					a__	(  self				)						->							int:
           # Initialize image_processor
           _A							:     List[str]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     str			=						prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     int			=						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							:     Optional[Any]			=						image_processor(_a		,     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"""],
               )		,     )







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

