






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 3_2    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 2_5_5    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,     _lowerCamelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,     _lowerCamelCase = True    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3_0    ,     _lowerCamelCase=4_0_0    ,     _lowerCamelCase=3    ,     ):
         UpperCamelCase_:   str        =							parent
         UpperCamelCase_:   List[str]        =							do_resize
         UpperCamelCase_:   Optional[int]        =							size if size is not None else {'shortest_edge': 2_8_8}
         UpperCamelCase_:   Optional[Any]        =							size_divisor
         UpperCamelCase_:   List[str]        =							do_rescale
         UpperCamelCase_:   List[str]        =							rescale_factor
         UpperCamelCase_:   Optional[int]        =							do_normalize
         UpperCamelCase_:   str        =							do_center_crop
         UpperCamelCase_:   str        =							image_mean
         UpperCamelCase_:   str        =							image_std
         UpperCamelCase_:   Any        =							do_pad
         UpperCamelCase_:   Any        =							batch_size
         UpperCamelCase_:   List[str]        =							num_channels
         UpperCamelCase_:   Tuple        =							min_resolution
         UpperCamelCase_:   Optional[Any]        =							max_resolution





       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,
             "size_divisor": self.size_divisor,
         }





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=False				):
         if not batched:
           UpperCamelCase_:   Tuple        =							self.size['shortest_edge']
           UpperCamelCase_:   Dict        =							image_inputs[0]
           if isinstance(_lowerCamelCase    ,     Image.Image				):
             UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							image.size
           else:
             UpperCamelCase_ ,UpperCamelCase_:   int        =							image.shape[1], image.shape[2]
           UpperCamelCase_:   List[str]        =							size / min(_lowerCamelCase    ,     _lowerCamelCase				)
           if h < w:
             UpperCamelCase_ ,UpperCamelCase_:   str        =							size, scale * w
           else:
             UpperCamelCase_ ,UpperCamelCase_:   str        =							scale * h, size

           UpperCamelCase_:   Dict        =							int((1_3_3_3 / 8_0_0) * size				)
           if max(_lowerCamelCase    ,     _lowerCamelCase				) > max_size:
             UpperCamelCase_:   int        =							max_size / max(_lowerCamelCase    ,     _lowerCamelCase				)
             UpperCamelCase_:   Optional[int]        =							newh * scale
             UpperCamelCase_:   Optional[int]        =							neww * scale

           UpperCamelCase_ ,UpperCamelCase_:   Dict        =							int(newh + 0.5				), int(neww + 0.5				)
           UpperCamelCase_ ,UpperCamelCase_:   int        =							(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

         else:
           UpperCamelCase_:   int        =							[]
           for image in image_inputs:
             UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							self.get_expected_values([image]				)
             expected_values.append((expected_height, expected_width)				)
           UpperCamelCase_:   Optional[int]        =							max(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: item[0]				)[0]
           UpperCamelCase_:   Optional[Any]        =							max(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: item[1]				)[1]

         return expected_height, expected_width







@require_torch
@require_vision
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Dict										=BridgeTowerImageProcessor if is_vision_available() else None





       def      _a       (   self				):
         UpperCamelCase_:   int        =							BridgeTowerImageProcessingTester(self				)





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





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							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'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'size_divisor'				)				)





       def      _a       (   self				):
         pass





       def      _a       (   self				):
         # Initialize image processor
         UpperCamelCase_:   int        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCamelCase_:   List[str]        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     Image.Image				)

         # Test not batched input
         UpperCamelCase_:   List[Any]        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Dict        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_:   Optional[Any]        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   str        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )





       def      _a       (   self				):
         # Initialize image processor
         UpperCamelCase_:   Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCamelCase_:   List[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
         UpperCamelCase_:   int        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_:   Optional[int]        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )





       def      _a       (   self				):
         # Initialize image processor
         UpperCamelCase_:   Dict        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCamelCase_:   str        =							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
         UpperCamelCase_:   List[Any]        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_:   Optional[int]        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)
         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )
"""simple docstring"""



import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			0





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""							)
										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Optional[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Any								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     str								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Union[str, Any]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Union[str, Any]								=			CLIPConfig()

																	# Create a dummy config file with image_proceesor_type
																	A__       :     Dict								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Optional[int]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							).to_dict()

																	config_dict.pop("""image_processor_type"""							)
																	A__       :     str								=			CLIPImageProcessor(**snake_case_							)

																	# save in new folder
																	model_config.save_pretrained(snake_case_							)
																	config.save_pretrained(snake_case_							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)

																	# make sure private variable is not incorrectly saved
																	A__       :     Dict								=			json.loads(config.to_json_string()							)
																	self.assertTrue("""_processor_class""" not in dict_as_saved							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )

																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """clip-base is not a local folder and is not a valid model identifier"""							):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""clip-base"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""							):
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_   ,      revision="""aaaaaa"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""   ,      ):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaises(snake_case_							):
																	A__       :     str								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(snake_case_							):
																	A__       :     int								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)

										A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
										self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)

										# Test image processor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(snake_case_							)
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(snake_case_   ,      trust_remote_code=snake_case_							)
										self.assertEqual(reloaded_image_processor.__class__.__name__   ,      """NewImageProcessor"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(snake_case_							):
																								AutoImageProcessor.register(snake_case_   ,      snake_case_							)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								A__       :     Tuple								=			Path(snake_case_							) / """preprocessor_config.json"""
																								A__       :     List[str]								=			Path(snake_case_							) / """config.json"""
																								json.dump(
																								    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																								json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																								A__       :     Optional[int]								=			CustomImageProcessor.from_pretrained(snake_case_							)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	with tempfile.TemporaryDirectory() as tmp_dir:
																								image_processor.save_pretrained(snake_case_							)
																								A__       :     int								=			AutoImageProcessor.from_pretrained(snake_case_							)
																								self.assertIsInstance(snake_case_   ,      snake_case_							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						Union[str, Any]					     =				True

										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# If remote code is not set, the default is to use local
																	A__       :     int								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote code is disabled, we load the local one.
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote is enabled, we load from the Hub
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(not hasattr(snake_case_   ,      """is_local"""							)							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





