





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							lowerCAmelCase							:       Any					 =	"""platform"""
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class 				a			:
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     PegasusConfig
				SCREAMING_SNAKE_CASE__     :   Dict		      =     {}
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''gelu'''






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=13			, _lowerCAmelCase=7			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, _lowerCAmelCase=99			, _lowerCAmelCase=32			, _lowerCAmelCase=5			, _lowerCAmelCase=4			, _lowerCAmelCase=37			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=20			, _lowerCAmelCase=2			, _lowerCAmelCase=1			, _lowerCAmelCase=0			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   parent
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   batch_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   seq_length
										__SCREAMING_SNAKE_CASE:				List[str]           =   is_training
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   use_labels
										__SCREAMING_SNAKE_CASE:				int           =   vocab_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   intermediate_size

										__SCREAMING_SNAKE_CASE:				List[str]           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				List[Any]           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Any           =   eos_token_id
										__SCREAMING_SNAKE_CASE:				Dict           =   pad_token_id
										__SCREAMING_SNAKE_CASE:				Any           =   bos_token_id






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   ids_tensor([self.batch_size, self.seq_length - 1]			, self.vocab_size					).clip(3			, self.vocab_size					)
										__SCREAMING_SNAKE_CASE:				int           =   np.expand_dims(np.array([self.eos_token_id] * self.batch_size					)			, 1					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.concatenate([input_ids, eos_tensor]			, axis=1					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size					)

										__SCREAMING_SNAKE_CASE:				Any           =   self.config_cls(
										    vocab_size=self.vocab_size			, d_model=self.hidden_size			, encoder_layers=self.num_hidden_layers			, decoder_layers=self.num_hidden_layers			, encoder_attention_heads=self.num_attention_heads			, decoder_attention_heads=self.num_attention_heads			, encoder_ffn_dim=self.intermediate_size			, decoder_ffn_dim=self.intermediate_size			, dropout=self.hidden_dropout_prob			, attention_dropout=self.attention_probs_dropout_prob			, max_position_embeddings=self.max_position_embeddings			, eos_token_ids=[2]			, bos_token_id=self.bos_token_id			, pad_token_id=self.pad_token_id			, decoder_start_token_id=self.pad_token_id			, **self.config_updates			, )
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   prepare_pegasus_inputs_dict(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										return config, inputs_dict






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   20
										__SCREAMING_SNAKE_CASE:				List[Any]           =   model_class_name(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   model.encode(inputs_dict['''input_ids''']					)

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										__SCREAMING_SNAKE_CASE:				int           =   model.init_cache(decoder_input_ids.shape[0]			, _lowerCAmelCase			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			, dtype='''i4'''					)

										__SCREAMING_SNAKE_CASE:				str           =   jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			, )
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model.decode(
										    decoder_input_ids[:, :-1]			, _lowerCAmelCase			, decoder_attention_mask=_lowerCAmelCase			, past_key_values=_lowerCAmelCase			, decoder_position_ids=_lowerCAmelCase			, )

										__SCREAMING_SNAKE_CASE:				List[str]           =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			, dtype='''i4'''					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   model.decode(
										    decoder_input_ids[:, -1:]			, _lowerCAmelCase			, decoder_attention_mask=_lowerCAmelCase			, past_key_values=outputs_cache.past_key_values			, decoder_position_ids=_lowerCAmelCase			, )

										__SCREAMING_SNAKE_CASE:				str           =   model.decode(_lowerCAmelCase			, _lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
										self.parent.assertTrue(diff < 1e-3			, msg=f"""Max diff is {diff}"""					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   20
										__SCREAMING_SNAKE_CASE:				List[str]           =   model_class_name(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Any           =   model.encode(inputs_dict['''input_ids''']					)

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
										    ]			, axis=-1			, )

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   model.init_cache(decoder_input_ids.shape[0]			, _lowerCAmelCase			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]			, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			, )

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.decode(
										    decoder_input_ids[:, :-1]			, _lowerCAmelCase			, decoder_attention_mask=_lowerCAmelCase			, past_key_values=_lowerCAmelCase			, decoder_position_ids=_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			, dtype='''i4'''					)
										__SCREAMING_SNAKE_CASE:				Any           =   model.decode(
										    decoder_input_ids[:, -1:]			, _lowerCAmelCase			, past_key_values=outputs_cache.past_key_values			, decoder_attention_mask=_lowerCAmelCase			, decoder_position_ids=_lowerCAmelCase			, )

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model.decode(_lowerCAmelCase			, _lowerCAmelCase			, decoder_attention_mask=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
										self.parent.assertTrue(diff < 1e-3			, msg=f"""Max diff is {diff}"""					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Any=None       ,			UpperCamelCase__   :   List[Any]=None       ,			)      ->    Tuple:

						"""simple docstring"""





						if attention_mask is None:
												__SCREAMING_SNAKE_CASE:				int           =   np.not_equal(UpperCamelCase__       ,			config.pad_token_id					).astype(np.inta					)
						if decoder_attention_mask is None:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape       ,			dtype=np.inta					),
												        np.not_equal(decoder_input_ids[:, 1:]       ,			config.pad_token_id					).astype(np.inta					),
												    ]       ,			axis=-1       ,			)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}





@require_flax
class 				a			(      __lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__     :   int		      =     (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     True
				SCREAMING_SNAKE_CASE__     :   str		      =     False
				SCREAMING_SNAKE_CASE__     :   Any		      =     False
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     False






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   FlaxPegasusModelTester(self					)
										__SCREAMING_SNAKE_CASE:				int           =   ConfigTester(self			, config_class=_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.config_tester.run_common_tests()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Dict           =   self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																						__SCREAMING_SNAKE_CASE:				Dict           =   self._prepare_for_class(_lowerCAmelCase			, _lowerCAmelCase					)
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   model_class(_lowerCAmelCase					)

																						@jax.jit
																						def encode_jitted(_lowerCAmelCase			, _lowerCAmelCase=None			, **_lowerCAmelCase					):
																												return model.encode(input_ids=_lowerCAmelCase			, attention_mask=_lowerCAmelCase					)

																						with self.subTest('''JIT Enabled'''					):
																												__SCREAMING_SNAKE_CASE:				List[str]           =   encode_jitted(**_lowerCAmelCase					).to_tuple()

																						with self.subTest('''JIT Disabled'''					):
																												with jax.disable_jit():
																																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   encode_jitted(**_lowerCAmelCase					).to_tuple()

																						self.assertEqual(len(_lowerCAmelCase					)			, len(_lowerCAmelCase					)					)
																						for jitted_output, output in zip(_lowerCAmelCase			, _lowerCAmelCase					):
																												self.assertEqual(jitted_output.shape			, output.shape					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   model_class(_lowerCAmelCase					)
																						__SCREAMING_SNAKE_CASE:				Dict           =   model.encode(inputs_dict['''input_ids''']			, inputs_dict['''attention_mask''']					)

																						__SCREAMING_SNAKE_CASE:				List[str]           =   {
																						    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																						    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																						    '''encoder_outputs''': encoder_outputs,
																						}

																						@jax.jit
																						def decode_jitted(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):
																												return model.decode(
																												    decoder_input_ids=_lowerCAmelCase			, decoder_attention_mask=_lowerCAmelCase			, encoder_outputs=_lowerCAmelCase			, )

																						with self.subTest('''JIT Enabled'''					):
																												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   decode_jitted(**_lowerCAmelCase					).to_tuple()

																						with self.subTest('''JIT Disabled'''					):
																												with jax.disable_jit():
																																		__SCREAMING_SNAKE_CASE:				List[Any]           =   decode_jitted(**_lowerCAmelCase					).to_tuple()

																						self.assertEqual(len(_lowerCAmelCase					)			, len(_lowerCAmelCase					)					)
																						for jitted_output, output in zip(_lowerCAmelCase			, _lowerCAmelCase					):
																												self.assertEqual(jitted_output.shape			, output.shape					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										for model_class_name in self.all_model_classes:
																__SCREAMING_SNAKE_CASE:				Dict           =   model_class_name.from_pretrained('''google/pegasus-large'''			, from_pt=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   np.ones((1, 1)					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(_lowerCAmelCase					)
																self.assertIsNotNone(_lowerCAmelCase					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   [
										    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
										    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
										]

										__SCREAMING_SNAKE_CASE:				str           =   [
										    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
										    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
										]

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   tokenizer(_lowerCAmelCase			, return_tensors='''np'''			, truncation=_lowerCAmelCase			, max_length=512			, padding=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   model.generate(**_lowerCAmelCase			, num_beams=2					).sequences
										__SCREAMING_SNAKE_CASE:				Tuple           =   tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase					)
										assert tgt_text == decoded










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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   0






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''					)
										self.assertIsInstance(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   Path(_lowerCAmelCase					) / '''preprocessor_config.json'''
																__SCREAMING_SNAKE_CASE:				List[str]           =   Path(_lowerCAmelCase					) / '''config.json'''
																json.dump(
																    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			, open(_lowerCAmelCase			, '''w'''					)			, )
																json.dump({'''model_type''': '''clip'''}			, open(_lowerCAmelCase			, '''w'''					)					)

																__SCREAMING_SNAKE_CASE:				Dict           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase					)
																self.assertIsInstance(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE:				int           =   Path(_lowerCAmelCase					) / '''preprocessor_config.json'''
																__SCREAMING_SNAKE_CASE:				Tuple           =   Path(_lowerCAmelCase					) / '''config.json'''
																json.dump(
																    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			, open(_lowerCAmelCase			, '''w'''					)			, )
																json.dump({'''model_type''': '''clip'''}			, open(_lowerCAmelCase			, '''w'''					)					)

																__SCREAMING_SNAKE_CASE:				str           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase					)
																self.assertIsInstance(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE:				int           =   CLIPConfig()

																# Create a dummy config file with image_proceesor_type
																__SCREAMING_SNAKE_CASE:				Any           =   Path(_lowerCAmelCase					) / '''preprocessor_config.json'''
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   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
																__SCREAMING_SNAKE_CASE:				Any           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase					).to_dict()

																config_dict.pop('''image_processor_type'''					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   CLIPImageProcessor(**_lowerCAmelCase					)

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

																__SCREAMING_SNAKE_CASE:				Dict           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase					)

																# make sure private variable is not incorrectly saved
																__SCREAMING_SNAKE_CASE:				int           =   json.loads(config.to_json_string()					)
																self.assertTrue('''_processor_class''' not in dict_as_saved					)

										self.assertIsInstance(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   Path(_lowerCAmelCase					) / '''preprocessor_config.json'''
																json.dump(
																    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			, open(_lowerCAmelCase			, '''w'''					)			, )

																__SCREAMING_SNAKE_CASE:				int           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase					)
																self.assertIsInstance(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with self.assertRaisesRegex(
										    _lowerCAmelCase			, '''clip-base is not a local folder and is not a valid model identifier'''					):
																__SCREAMING_SNAKE_CASE:				Tuple           =   AutoImageProcessor.from_pretrained('''clip-base'''					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with self.assertRaisesRegex(
										    _lowerCAmelCase			, R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''					):
																__SCREAMING_SNAKE_CASE:				Dict           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase			, revision='''aaaaaa'''					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with self.assertRaisesRegex(
										    _lowerCAmelCase			, '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			, ):
																__SCREAMING_SNAKE_CASE:				int           =   AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with self.assertRaises(_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Optional[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					):
																__SCREAMING_SNAKE_CASE:				Any           =   AutoImageProcessor.from_pretrained(
																    '''hf-internal-testing/test_dynamic_image_processor'''			, trust_remote_code=_lowerCAmelCase					)

										__SCREAMING_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'''					)

										# Test image processor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
																image_processor.save_pretrained(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   AutoImageProcessor.from_pretrained(_lowerCAmelCase			, trust_remote_code=_lowerCAmelCase					)
										self.assertEqual(reloaded_image_processor.__class__.__name__			, '''NewImageProcessor'''					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										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:
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Path(_lowerCAmelCase					) / '''preprocessor_config.json'''
																						__SCREAMING_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'''					)					)

																						__SCREAMING_SNAKE_CASE:				List[str]           =   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					)
																						__SCREAMING_SNAKE_CASE:				int           =   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 			snake_case_    (  self					):

										"""simple docstring"""




										class 				a			(      __lowercase			):
														SCREAMING_SNAKE_CASE__     :   Tuple		      =     True

										try:
																AutoConfig.register('''custom'''			, _lowerCAmelCase					)
																AutoImageProcessor.register(_lowerCAmelCase			, _lowerCAmelCase					)
																# If remote code is not set, the default is to use local
																__SCREAMING_SNAKE_CASE:				str           =   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.
																__SCREAMING_SNAKE_CASE:				Tuple           =   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
																__SCREAMING_SNAKE_CASE:				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]



