


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


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

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowerCamelCase__							:						List[Any]							     =   get_tests_dir("""fixtures""")
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= mock.Mock()
       snake_case__						= 5_00
       snake_case__						= {}
       snake_case__						= HTTPError
       snake_case__						= {}

       # Download this model to make sure it's in the cache.
       snake_case__						= ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''   )
       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch('''requests.Session.request'''     ,		return_value=_a   ) as mock_head:
         snake_case__						= ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''   )
         # This check we did call the fake head request
         mock_head.assert_called()

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= ViTImageProcessor.from_pretrained(
           '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''   )







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):







       with self.assertRaises(_a   ):
         # config is in subfolder, the following should not work without specifying the subfolder
         snake_case__						= AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''   )

       snake_case__						= AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/stable-diffusion-all-variants'''     ,		subfolder='''feature_extractor'''   )

       self.assertIsNotNone(_a   )




@is_staging_test
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     @classmethod
     def 	SCREAMING_SNAKE_CASE__						(					cls:int   ):
       snake_case__						= TOKEN
       HfFolder.save_token(_a   )

     @classmethod
     def 	SCREAMING_SNAKE_CASE__						(					cls:Union[str, Any]   ):







       try:
         delete_repo(token=cls._token     ,		repo_id='''test-image-processor'''   )
       except HTTPError:
         pass

       try:
         delete_repo(token=cls._token     ,		repo_id='''valid_org/test-image-processor-org'''   )
       except HTTPError:
         pass

       try:
         delete_repo(token=cls._token     ,		repo_id='''test-dynamic-image-processor'''   )
       except HTTPError:
         pass

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= ViTImageProcessor.from_pretrained(_a   )
       image_processor.push_to_hub('''test-image-processor'''     ,		use_auth_token=self._token   )

       snake_case__						= ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""   )
       for k, v in image_processor.__dict__.items():
         self.assertEqual(_a     ,		getattr(_a     ,		_a   )   )

       # Reset repo
       delete_repo(token=self._token     ,		repo_id='''test-image-processor'''   )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(
             _a     ,		repo_id='''test-image-processor'''     ,		push_to_hub=_a     ,		use_auth_token=self._token   )

       snake_case__						= ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor"""   )
       for k, v in image_processor.__dict__.items():
         self.assertEqual(_a     ,		getattr(_a     ,		_a   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= ViTImageProcessor.from_pretrained(_a   )
       image_processor.push_to_hub('''valid_org/test-image-processor'''     ,		use_auth_token=self._token   )

       snake_case__						= ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''   )
       for k, v in image_processor.__dict__.items():
         self.assertEqual(_a     ,		getattr(_a     ,		_a   )   )

       # Reset repo
       delete_repo(token=self._token     ,		repo_id='''valid_org/test-image-processor'''   )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(
             _a     ,		repo_id='''valid_org/test-image-processor-org'''     ,		push_to_hub=_a     ,		use_auth_token=self._token   )

       snake_case__						= ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''   )
       for k, v in image_processor.__dict__.items():
         self.assertEqual(_a     ,		getattr(_a     ,		_a   )   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       CustomImageProcessor.register_for_auto_class()
       snake_case__						= CustomImageProcessor.from_pretrained(_a   )

       image_processor.push_to_hub('''test-dynamic-image-processor'''     ,		use_auth_token=self._token   )

       # This has added the proper auto_map field to the config
       self.assertDictEqual(
           image_processor.auto_map     ,		{'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}     ,		)

       snake_case__						= AutoImageProcessor.from_pretrained(
           F"""{USER}/test-dynamic-image-processor"""     ,		trust_remote_code=_a   )
       # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
       self.assertEqual(new_image_processor.__class__.__name__     ,		'''CustomImageProcessor'''   )







'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







       def        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

             logger.info(F"{len(A  )} controlnets loaded from {pretrained_model_path}."  )

             if len(A  ) == 0:
                   raise ValueError(
                       F"No ControlNets found under {os.path.dirname(A  )}. Expected at least {pretrained_model_path + '_0'}."  )

             return cls(A  )


