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 EfficientNetImageProcessor






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=13							,     lowerCAmelCase_=3							,     lowerCAmelCase_=18							,     lowerCAmelCase_=30							,     lowerCAmelCase_=4_00							,     lowerCAmelCase_=True							,     lowerCAmelCase_=None							,     lowerCAmelCase_=True							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     lowerCAmelCase_=[0.5, 0.5, 0.5]							,     )	->List[str]:
        a  :    Any				=						size if size is not None else {'''height''': 18, '''width''': 18}
        a  :    Optional[Any]				=						parent
        a  :    str				=						batch_size
        a  :    Tuple				=						num_channels
        a  :    Optional[int]				=						image_size
        a  :    str				=						min_resolution
        a  :    Dict				=						max_resolution
        a  :    List[Any]				=						do_resize
        a  :    Optional[int]				=						size
        a  :    List[Any]				=						do_normalize
        a  :    List[str]				=						image_mean
        a  :    List[Any]				=						image_std


   def _lowercase(    self    )	->int:
        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 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						List[Any] 			=   EfficientNetImageProcessor if is_vision_available() else None
   def _lowercase(    self    )	->Union[str, Any]:
        a  :    Dict				=						EfficientNetImageProcessorTester(self    )
   @property
   def _lowercase(    self    )	->Any:
        return self.image_processor_tester.prepare_image_processor_dict()
   def _lowercase(    self    )	->Any:
        a  :    str				=						self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_mean'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''image_std'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_normalize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''do_resize'''    )    )
        self.assertTrue(hasattr(lowerCAmelCase_							,     '''size'''    )    )
   def _lowercase(    self    )	->Union[str, Any]:
        a  :    int				=						self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size							,     {'''height''': 18, '''width''': 18}    )

        a  :    List[str]				=						self.image_processing_class.from_dict(self.image_processor_dict							,     size=42    )
        self.assertEqual(image_processor.size							,     {'''height''': 42, '''width''': 42}    )
   def _lowercase(    self    )	->str:
        # Initialize image_processing
        a  :    Tuple				=						self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        a  :    List[Any]				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     Image.Image    )

        # Test not batched input
        a  :    Tuple				=						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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    Optional[Any]				=						image_processing(lowerCAmelCase_							,     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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
   def _lowercase(    self    )	->Tuple:
        # Initialize image_processing
        a  :    int				=						self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        a  :    Any				=						prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase_							,     numpify=lowerCAmelCase_    )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_							,     np.ndarray    )

        # Test not batched input
        a  :    Dict				=						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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    str				=						image_processing(lowerCAmelCase_							,     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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )


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

        # Test not batched input
        a  :    Any				=						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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )

        # Test batched
        a  :    Any				=						image_processing(lowerCAmelCase_							,     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['''height'''],
                self.image_processor_tester.size['''width'''],
            )							,     )
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/