



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       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      Any     		=	0



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      List[str]     		=	AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''  )
						self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__	:      Any     		=	Path(_lowerCamelCase  ) / '''preprocessor_config.json'''
										snake_case__	:      Optional[int]     		=	Path(_lowerCamelCase  ) / '''config.json'''
										json.dump(
										    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,open(_lowerCamelCase   ,'''w'''  )   ,)
										json.dump({'''model_type''': '''clip'''}   ,open(_lowerCamelCase   ,'''w'''  )  )

										snake_case__	:      List[str]     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  )
										self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )



		def 	__lowerCamelCase			(     self     :Any  ):
						# Ensure we can load the image processor from the feature extractor config
						with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__	:      Any     		=	Path(_lowerCamelCase  ) / '''preprocessor_config.json'''
										snake_case__	:      List[str]     		=	Path(_lowerCamelCase  ) / '''config.json'''
										json.dump(
										    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}   ,open(_lowerCamelCase   ,'''w'''  )   ,)
										json.dump({'''model_type''': '''clip'''}   ,open(_lowerCamelCase   ,'''w'''  )  )

										snake_case__	:      Union[str, Any]     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  )
										self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )



		def 	__lowerCamelCase			(     self     :Any  ):
						with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__	:      List[Any]     		=	CLIPConfig()

										# Create a dummy config file with image_proceesor_type
										snake_case__	:      Union[str, Any]     		=	Path(_lowerCamelCase  ) / '''preprocessor_config.json'''
										snake_case__	:      Dict     		=	Path(_lowerCamelCase  ) / '''config.json'''
										json.dump(
										    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,open(_lowerCamelCase   ,'''w'''  )   ,)
										json.dump({'''model_type''': '''clip'''}   ,open(_lowerCamelCase   ,'''w'''  )  )

										# remove image_processor_type to make sure config.json alone is enough to load image processor locally
										snake_case__	:      Optional[Any]     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  ).to_dict()

										config_dict.pop('''image_processor_type'''  )
										snake_case__	:      Optional[Any]     		=	CLIPImageProcessor(**_lowerCamelCase  )

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

										snake_case__	:      int     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  )

										# make sure private variable is not incorrectly saved
										snake_case__	:      List[str]     		=	json.loads(config.to_json_string()  )
										self.assertTrue('''_processor_class''' not in dict_as_saved  )

						self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__	:      Any     		=	Path(_lowerCamelCase  ) / '''preprocessor_config.json'''
										json.dump(
										    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,open(_lowerCamelCase   ,'''w'''  )   ,)

										snake_case__	:      Tuple     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  )
										self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						with self.assertRaisesRegex(
						    _lowerCamelCase   ,'''clip-base is not a local folder and is not a valid model identifier'''  ):
										snake_case__	:      Union[str, Any]     		=	AutoImageProcessor.from_pretrained('''clip-base'''  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						with self.assertRaisesRegex(
						    _lowerCamelCase   ,r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''  ):
										snake_case__	:      str     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase   ,revision='''aaaaaa'''  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						with self.assertRaisesRegex(
						    _lowerCamelCase   ,'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''   ,):
										snake_case__	:      List[Any]     		=	AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''  )



		def 	__lowerCamelCase			(     self     :int  ):
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(_lowerCamelCase  ):
										snake_case__	:      Union[str, Any]     		=	AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''  )
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(_lowerCamelCase  ):
										snake_case__	:      Union[str, Any]     		=	AutoImageProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_image_processor'''   ,trust_remote_code=_lowerCamelCase  )

						snake_case__	:      Any     		=	AutoImageProcessor.from_pretrained(
						    '''hf-internal-testing/test_dynamic_image_processor'''   ,trust_remote_code=_lowerCamelCase  )
						self.assertEqual(image_processor.__class__.__name__   ,'''NewImageProcessor'''  )

						# Test image processor can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
										image_processor.save_pretrained(_lowerCamelCase  )
										snake_case__	:      Any     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase   ,trust_remote_code=_lowerCamelCase  )
						self.assertEqual(reloaded_image_processor.__class__.__name__   ,'''NewImageProcessor'''  )



		def 	__lowerCamelCase			(     self     :Any  ):
						try:
										AutoConfig.register('''custom'''   ,_lowerCamelCase  )
										AutoImageProcessor.register(_lowerCamelCase   ,_lowerCamelCase  )
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(_lowerCamelCase  ):
														AutoImageProcessor.register(_lowerCamelCase   ,_lowerCamelCase  )

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

														snake_case__	:      int     		=	CustomImageProcessor.from_pretrained(_lowerCamelCase  )

										# 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(_lowerCamelCase  )
														snake_case__	:      List[Any]     		=	AutoImageProcessor.from_pretrained(_lowerCamelCase  )
														self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase  )

						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     :List[Any]  ):


						class       a							(			a_				):
								__lowerCAmelCase	:    Union[str, Any]											=					True

						try:
										AutoConfig.register('''custom'''   ,_lowerCamelCase  )
										AutoImageProcessor.register(_lowerCamelCase   ,_lowerCamelCase  )
										# If remote code is not set, the default is to use local
										snake_case__	:      Dict     		=	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.
										snake_case__	:      Optional[int]     		=	AutoImageProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_image_processor'''   ,trust_remote_code=_lowerCamelCase  )
										self.assertEqual(image_processor.__class__.__name__   ,'''NewImageProcessor'''  )
										self.assertTrue(image_processor.is_local  )

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









"""simple docstring"""



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_UpperCamelCase		:  Optional[Any]						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase = True       ):
      lowercase							    =       {}  # dictionary of lists
      lowercase							    =       directed







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):

      if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       [source_vertex]
      else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       []

      return self







    def __repr__(			self       ):
      return pformat(self.adj_list       )


