
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
					import torch







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=14				,       UpperCamelCase__=7				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=False				,       UpperCamelCase__=True				,       UpperCamelCase__=99				,       UpperCamelCase__=32				,       UpperCamelCase__=4				,       UpperCamelCase__=4				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=512				,       UpperCamelCase__=0.0_2				,       ):
					A__  :	int         =							parent
					A__  :	List[str]         =							batch_size
					A__  :	Optional[Any]         =							seq_length
					A__  :	Tuple         =							is_training
					A__  :	Optional[Any]         =							use_input_mask
					A__  :	int         =							use_token_type_ids
					A__  :	Optional[int]         =							use_labels
					A__  :	Any         =							vocab_size
					A__  :	Dict         =							hidden_size
					A__  :	Any         =							rotary_dim
					A__  :	Union[str, Any]         =							num_hidden_layers
					A__  :	Union[str, Any]         =							num_attention_heads
					A__  :	List[Any]         =							intermediate_size
					A__  :	List[str]         =							hidden_act
					A__  :	Tuple         =							hidden_dropout_prob
					A__  :	Optional[Any]         =							attention_probs_dropout_prob
					A__  :	List[Any]         =							max_position_embeddings
					A__  :	str         =							initializer_range
					A__  :	List[str]         =							None
					A__  :	str         =							vocab_size - 1
					A__  :	Optional[Any]         =							vocab_size - 1
					A__  :	Optional[Any]         =							vocab_size - 1





	def   __snake_case				(			self		):
					A__  :	int         =							ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size		)

					A__  :	Dict         =							None
					if self.use_input_mask:
									A__  :	Optional[Any]         =							random_attention_mask([self.batch_size, self.seq_length]		)

					A__  :	Optional[int]         =							GPTJConfig(
					    vocab_size=self.vocab_size				,       n_embd=self.hidden_size				,       n_layer=self.num_hidden_layers				,       n_head=self.num_attention_heads				,       n_positions=self.max_position_embeddings				,       use_cache=UpperCAmelCase_				,       bos_token_id=self.bos_token_id				,       eos_token_id=self.eos_token_id				,       pad_token_id=self.pad_token_id				,       rotary_dim=self.rotary_dim				,       )

					return (config, input_ids, input_mask)





	def   __snake_case				(			self		):
					A__  :	int         =							self.prepare_config_and_inputs()
					A__  :	Optional[int]         =							config_and_inputs
					A__  :	Optional[int]         =							{'input_ids': input_ids, 'attention_mask': attention_mask}
					return config, inputs_dict





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							20
					A__  :	int         =							model_class_name(UpperCAmelCase_		)

					A__  :	int         =							model.init_cache(input_ids.shape[0]				,       UpperCAmelCase_		)
					A__  :	int         =							jnp.ones((input_ids.shape[0], max_decoder_length)				,       dtype='''i4'''		)

					A__  :	str         =							jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,       (input_ids.shape[0], input_ids.shape[-1] - 1)		)
					A__  :	Tuple         =							model(
					    input_ids[:, :-1]				,       attention_mask=UpperCAmelCase_				,       past_key_values=UpperCAmelCase_				,       position_ids=UpperCAmelCase_				,       )

					A__  :	List[Any]         =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,       dtype='''i4'''		)
					A__  :	int         =							model(
					    input_ids[:, -1:]				,       attention_mask=UpperCAmelCase_				,       past_key_values=outputs_cache.past_key_values				,       position_ids=UpperCAmelCase_				,       )

					A__  :	Optional[Any]         =							model(UpperCAmelCase_		)

					A__  :	List[Any]         =							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				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	int         =							20
					A__  :	int         =							model_class_name(UpperCAmelCase_		)

					A__  :	Optional[Any]         =							jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]				,       axis=-1				,       )

					A__  :	Any         =							model.init_cache(input_ids.shape[0]				,       UpperCAmelCase_		)
					A__  :	str         =							jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,       (input_ids.shape[0], input_ids.shape[-1] - 1)		)

					A__  :	Optional[int]         =							model(
					    input_ids[:, :-1]				,       attention_mask=UpperCAmelCase_				,       past_key_values=UpperCAmelCase_				,       position_ids=UpperCAmelCase_				,       )
					A__  :	Any         =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,       dtype='''i4'''		)
					A__  :	List[str]         =							model(
					    input_ids[:, -1:]				,       past_key_values=outputs_cache.past_key_values				,       attention_mask=UpperCAmelCase_				,       position_ids=UpperCAmelCase_				,       )

					A__  :	str         =							model(UpperCAmelCase_				,       attention_mask=UpperCAmelCase_		)

					A__  :	Optional[Any]         =							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}"		)







@require_flax
class  UpperCamelCase__		(    __lowerCamelCase,			__lowerCamelCase,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
	_lowerCAmelCase							       =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							FlaxGPTJModelTester(self		)





	def   __snake_case				(			self		):
					for model_class_name in self.all_model_classes:
									A__  :	str         =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_		)





	def   __snake_case				(			self		):
					for model_class_name in self.all_model_classes:
									A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_		)





	@tooslow
	def   __snake_case				(			self		):
					A__  :	Dict         =							GPTaTokenizer.from_pretrained('''gpt2'''				,       pad_token='''<|endoftext|>'''				,       padding_side='''left'''		)
					A__  :	Optional[int]         =							tokenizer(['''Hello this is a long string''', '''Hey''']				,       return_tensors='''np'''				,       padding=UpperCAmelCase_				,       truncation=UpperCAmelCase_		)

					A__  :	Optional[Any]         =							FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''		)
					A__  :	Tuple         =							False
					A__  :	Any         =							model.config.eos_token_id

					A__  :	Optional[Any]         =							jax.jit(model.generate		)

					A__  :	Any         =							jit_generate(
					    inputs['''input_ids''']				,       attention_mask=inputs['''attention_mask''']				,       pad_token_id=tokenizer.pad_token_id		).sequences

					A__  :	int         =							tokenizer.batch_decode(UpperCAmelCase_				,       skip_special_tokens=UpperCAmelCase_		)

					A__  :	Any         =							[
					    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
					    'Hey, I\'m a little late to the party. I\'m going to',
					]

					self.assertListEqual(UpperCAmelCase_				,       UpperCAmelCase_		)





	@is_pt_flax_cross_test
	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__		):
													# prepare inputs
													A__  :	List[str]         =							self._prepare_for_class(UpperCAmelCase_				,       UpperCAmelCase_		)
													A__  :	Optional[int]         =							{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													A__  :	Tuple         =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
													A__  :	Optional[int]         =							getattr(UpperCAmelCase_				,       UpperCAmelCase_		)

													A__  :	str         =							pt_inputs['input_ids'].shape
													A__  :	int         =							np.random.randint(0				,       seq_length - 1				,       size=(batch_size,)		)
													for batch_idx, start_index in enumerate(UpperCAmelCase_		):
																	A__  :	List[str]         =							0
																	A__  :	Dict         =							1
																	A__  :	Optional[int]         =							0
																	A__  :	Optional[int]         =							1
													A__  :	Optional[Any]         =							pt_model_class(UpperCAmelCase_		).eval()
													A__  :	str         =							model_class(UpperCAmelCase_				,       dtype=jnp.floataa		)

													A__  :	Tuple         =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,       UpperCAmelCase_		)
													A__  :	Dict         =							fx_state

													with torch.no_grad():
																	A__  :	str         =							pt_model(**UpperCAmelCase_		).to_tuple()

													A__  :	Optional[Any]         =							fx_model(**UpperCAmelCase_		).to_tuple()
													self.assertEqual(len(UpperCAmelCase_		)				,       len(UpperCAmelCase_		)				,       '''Output lengths differ between Flax and PyTorch'''		)
													for fx_output, pt_output in zip(UpperCAmelCase_				,       UpperCAmelCase_		):
																	self.assert_almost_equals(fx_output[:, -1]				,       pt_output[:, -1].numpy()				,       4e-2		)

													with tempfile.TemporaryDirectory() as tmpdirname:
																	pt_model.save_pretrained(UpperCAmelCase_		)
																	A__  :	List[Any]         =							model_class.from_pretrained(UpperCAmelCase_				,       from_pt=UpperCAmelCase_		)

													A__  :	int         =							fx_model_loaded(**UpperCAmelCase_		).to_tuple()
													self.assertEqual(
													    len(UpperCAmelCase_		)				,       len(UpperCAmelCase_		)				,       '''Output lengths differ between Flax and PyTorch'''		)
													for fx_output_loaded, pt_output in zip(UpperCAmelCase_				,       UpperCAmelCase_		):
																	self.assert_almost_equals(fx_output_loaded[:, -1]				,       pt_output[:, -1].numpy()				,       4e-2		)





	@is_pt_flax_cross_test
	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__		):
													# prepare inputs
													A__  :	int         =							self._prepare_for_class(UpperCAmelCase_				,       UpperCAmelCase_		)
													A__  :	str         =							{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													A__  :	Any         =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
													A__  :	Optional[Any]         =							getattr(UpperCAmelCase_				,       UpperCAmelCase_		)

													A__  :	List[Any]         =							pt_model_class(UpperCAmelCase_		).eval()
													A__  :	str         =							model_class(UpperCAmelCase_				,       dtype=jnp.floataa		)

													A__  :	Optional[int]         =							load_flax_weights_in_pytorch_model(UpperCAmelCase_				,       fx_model.params		)
													A__  :	Union[str, Any]         =							pt_inputs['input_ids'].shape
													A__  :	Tuple         =							np.random.randint(0				,       seq_length - 1				,       size=(batch_size,)		)
													for batch_idx, start_index in enumerate(UpperCAmelCase_		):
																	A__  :	List[str]         =							0
																	A__  :	Optional[int]         =							1
																	A__  :	List[str]         =							0
																	A__  :	int         =							1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
																	A__  :	List[Any]         =							pt_model(**UpperCAmelCase_		).to_tuple()

													A__  :	Optional[Any]         =							fx_model(**UpperCAmelCase_		).to_tuple()
													self.assertEqual(len(UpperCAmelCase_		)				,       len(UpperCAmelCase_		)				,       '''Output lengths differ between Flax and PyTorch'''		)
													for fx_output, pt_output in zip(UpperCAmelCase_				,       UpperCAmelCase_		):
																	self.assert_almost_equals(fx_output[:, -1]				,       pt_output[:, -1].numpy()				,       4e-2		)

													with tempfile.TemporaryDirectory() as tmpdirname:
																	fx_model.save_pretrained(UpperCAmelCase_		)
																	A__  :	int         =							pt_model_class.from_pretrained(UpperCAmelCase_				,       from_flax=UpperCAmelCase_		)

													with torch.no_grad():
																	A__  :	Dict         =							pt_model_loaded(**UpperCAmelCase_		).to_tuple()

													self.assertEqual(
													    len(UpperCAmelCase_		)				,       len(UpperCAmelCase_		)				,       '''Output lengths differ between Flax and PyTorch'''		)
													for fx_output, pt_output in zip(UpperCAmelCase_				,       UpperCAmelCase_		):
																	self.assert_almost_equals(fx_output[:, -1]				,       pt_output[:, -1].numpy()				,       4e-2		)





	@tooslow
	def   __snake_case				(			self		):
					for model_class_name in self.all_model_classes:
									A__  :	Optional[int]         =							model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''		)
									A__  :	Optional[Any]         =							model(np.ones((1, 1)		)		)
									self.assertIsNotNone(UpperCAmelCase_		)

import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


_SCREAMING_SNAKE_CASE    :      List[Any]          =			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_SCREAMING_SNAKE_CASE    :      int          =			get_tests_dir('fixtures/vocab.json')
_SCREAMING_SNAKE_CASE    :      Tuple          =			get_tests_dir('fixtures')







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							0





	def   __snake_case				(			self		):
					A__  :	Dict         =							AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''		)
					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Optional[Any]         =							WavaVecaConfig()
									A__  :	Dict         =							AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''		)

									# save in new folder
									model_config.save_pretrained(UpperCamelCase__		)
									processor.save_pretrained(UpperCamelCase__		)

									A__  :	Any         =							AutoProcessor.from_pretrained(UpperCamelCase__		)

					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					with tempfile.TemporaryDirectory() as tmpdirname:
									# copy relevant files
									copyfile(UpperCamelCase__				,       os.path.join(UpperCamelCase__				,       UpperCamelCase__		)		)
									copyfile(UpperCamelCase__				,       os.path.join(UpperCamelCase__				,       '''vocab.json'''		)		)

									A__  :	List[Any]         =							AutoProcessor.from_pretrained(UpperCamelCase__		)

					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Dict         =							WavaVecaFeatureExtractor()
									A__  :	Union[str, Any]         =							AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''		)

									A__  :	Optional[int]         =							WavaVecaProcessor(UpperCamelCase__				,       UpperCamelCase__		)

									# save in new folder
									processor.save_pretrained(UpperCamelCase__		)

									# drop `processor_class` in tokenizer
									with open(os.path.join(UpperCamelCase__				,       UpperCamelCase__		)				,       '''r'''		) as f:
													A__  :	str         =							json.load(UpperCamelCase__		)
													config_dict.pop('''processor_class'''		)

									with open(os.path.join(UpperCamelCase__				,       UpperCamelCase__		)				,       '''w'''		) as f:
													f.write(json.dumps(UpperCamelCase__		)		)

									A__  :	Optional[int]         =							AutoProcessor.from_pretrained(UpperCamelCase__		)

					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Optional[int]         =							WavaVecaFeatureExtractor()
									A__  :	List[Any]         =							AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''		)

									A__  :	str         =							WavaVecaProcessor(UpperCamelCase__				,       UpperCamelCase__		)

									# save in new folder
									processor.save_pretrained(UpperCamelCase__		)

									# drop `processor_class` in feature extractor
									with open(os.path.join(UpperCamelCase__				,       UpperCamelCase__		)				,       '''r'''		) as f:
													A__  :	List[Any]         =							json.load(UpperCamelCase__		)
													config_dict.pop('''processor_class'''		)

									with open(os.path.join(UpperCamelCase__				,       UpperCamelCase__		)				,       '''w'''		) as f:
													f.write(json.dumps(UpperCamelCase__		)		)

									A__  :	List[Any]         =							AutoProcessor.from_pretrained(UpperCamelCase__		)

					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							WavaVecaConfig(processor_class='''Wav2Vec2Processor'''		)
									model_config.save_pretrained(UpperCamelCase__		)
									# copy relevant files
									copyfile(UpperCamelCase__				,       os.path.join(UpperCamelCase__				,       '''vocab.json'''		)		)
									# create emtpy sample processor
									with open(os.path.join(UpperCamelCase__				,       UpperCamelCase__		)				,       '''w'''		) as f:
													f.write('''{}'''		)

									A__  :	Union[str, Any]         =							AutoProcessor.from_pretrained(UpperCamelCase__		)

					self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)





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

					A__  :	int         =							AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				,       trust_remote_code=UpperCamelCase__		)
					self.assertTrue(processor.special_attribute_present		)
					self.assertEqual(processor.__class__.__name__				,       '''NewProcessor'''		)

					A__  :	List[Any]         =							processor.feature_extractor
					self.assertTrue(feature_extractor.special_attribute_present		)
					self.assertEqual(feature_extractor.__class__.__name__				,       '''NewFeatureExtractor'''		)

					A__  :	List[Any]         =							processor.tokenizer
					self.assertTrue(tokenizer.special_attribute_present		)
					if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__				,       '''NewTokenizerFast'''		)

									# Test we can also load the slow version
									A__  :	Dict         =							AutoProcessor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_processor'''				,       trust_remote_code=UpperCamelCase__				,       use_fast=UpperCamelCase__		)
									A__  :	int         =							new_processor.tokenizer
									self.assertTrue(new_tokenizer.special_attribute_present		)
									self.assertEqual(new_tokenizer.__class__.__name__				,       '''NewTokenizer'''		)
					else:
									self.assertEqual(tokenizer.__class__.__name__				,       '''NewTokenizer'''		)





	def   __snake_case				(			self		):
					try:
									AutoConfig.register('''custom'''				,       UpperCamelCase__		)
									AutoFeatureExtractor.register(UpperCamelCase__				,       UpperCamelCase__		)
									AutoTokenizer.register(UpperCamelCase__				,       slow_tokenizer_class=UpperCamelCase__		)
									AutoProcessor.register(UpperCamelCase__				,       UpperCamelCase__		)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(UpperCamelCase__		):
													AutoProcessor.register(UpperCamelCase__				,       UpperCamelCase__		)

									# Now that the config is registered, it can be used as any other config with the auto-API
									A__  :	Any         =							CustomFeatureExtractor.from_pretrained(UpperCamelCase__		)

									with tempfile.TemporaryDirectory() as tmp_dir:
													A__  :	str         =							os.path.join(UpperCamelCase__				,       '''vocab.txt'''		)
													with open(UpperCamelCase__				,       '''w'''				,       encoding='''utf-8'''		) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]		)		)
													A__  :	str         =							CustomTokenizer(UpperCamelCase__		)

									A__  :	Optional[Any]         =							CustomProcessor(UpperCamelCase__				,       UpperCamelCase__		)

									with tempfile.TemporaryDirectory() as tmp_dir:
													processor.save_pretrained(UpperCamelCase__		)
													A__  :	Union[str, Any]         =							AutoProcessor.from_pretrained(UpperCamelCase__		)
													self.assertIsInstance(UpperCamelCase__				,       UpperCamelCase__		)

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
													del TOKENIZER_MAPPING._extra_content[CustomConfig]
									if CustomConfig in PROCESSOR_MAPPING._extra_content:
													del PROCESSOR_MAPPING._extra_content[CustomConfig]





	def   __snake_case				(			self		):







					class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







						'''simple docstring'''
						_lowerCAmelCase							       =				False







					class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







						'''simple docstring'''
						_lowerCAmelCase							       =				False







					class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







						'''simple docstring'''
						_lowerCAmelCase							       =				"AutoFeatureExtractor"
						_lowerCAmelCase							       =				"AutoTokenizer"
						_lowerCAmelCase							       =				False

					try:
									AutoConfig.register('''custom'''				,       UpperCamelCase__		)
									AutoFeatureExtractor.register(UpperCamelCase__				,       UpperCamelCase__		)
									AutoTokenizer.register(UpperCamelCase__				,       slow_tokenizer_class=UpperCamelCase__		)
									AutoProcessor.register(UpperCamelCase__				,       UpperCamelCase__		)
									# If remote code is not set, the default is to use local classes.
									A__  :	List[Any]         =							AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''		)
									self.assertEqual(processor.__class__.__name__				,       '''NewProcessor'''		)
									self.assertFalse(processor.special_attribute_present		)
									self.assertFalse(processor.feature_extractor.special_attribute_present		)
									self.assertFalse(processor.tokenizer.special_attribute_present		)

									# If remote code is disabled, we load the local ones.
									A__  :	Any         =							AutoProcessor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_processor'''				,       trust_remote_code=UpperCamelCase__		)
									self.assertEqual(processor.__class__.__name__				,       '''NewProcessor'''		)
									self.assertFalse(processor.special_attribute_present		)
									self.assertFalse(processor.feature_extractor.special_attribute_present		)
									self.assertFalse(processor.tokenizer.special_attribute_present		)

									# If remote is enabled, we load from the Hub.
									A__  :	Union[str, Any]         =							AutoProcessor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_processor'''				,       trust_remote_code=UpperCamelCase__		)
									self.assertEqual(processor.__class__.__name__				,       '''NewProcessor'''		)
									self.assertTrue(processor.special_attribute_present		)
									self.assertTrue(processor.feature_extractor.special_attribute_present		)
									self.assertTrue(processor.tokenizer.special_attribute_present		)

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
													del TOKENIZER_MAPPING._extra_content[CustomConfig]
									if CustomConfig in PROCESSOR_MAPPING._extra_content:
													del PROCESSOR_MAPPING._extra_content[CustomConfig]





	def   __snake_case				(			self		):
					A__  :	str         =							AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
					self.assertEqual(processor.__class__.__name__				,       '''BertTokenizerFast'''		)





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''		)
					self.assertEqual(processor.__class__.__name__				,       '''ConvNextImageProcessor'''		)







@is_staging_test
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]





	@classmethod
	def   __snake_case				(			cls		):
					A__  :	List[str]         =							TOKEN
					HfFolder.save_token(UpperCamelCase__		)





	@classmethod
	def   __snake_case				(			cls		):
					try:
									delete_repo(token=cls._token				,       repo_id='''test-processor'''		)
					except HTTPError:
									pass

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

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





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							WavaVecaProcessor.from_pretrained(UpperCamelCase__		)
					with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(
									    os.path.join(UpperCamelCase__				,       '''test-processor'''		)				,       push_to_hub=UpperCamelCase__				,       use_auth_token=self._token		)

									A__  :	List[Any]         =							WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"		)
									for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(UpperCamelCase__				,       getattr(new_processor.feature_extractor				,       UpperCamelCase__		)		)
									self.assertDictEqual(new_processor.tokenizer.get_vocab()				,       processor.tokenizer.get_vocab()		)





	def   __snake_case				(			self		):
					A__  :	int         =							WavaVecaProcessor.from_pretrained(UpperCamelCase__		)

					with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(
									    os.path.join(UpperCamelCase__				,       '''test-processor-org'''		)				,       push_to_hub=UpperCamelCase__				,       use_auth_token=self._token				,       organization='''valid_org'''				,       )

									A__  :	List[str]         =							WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''		)
									for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(UpperCamelCase__				,       getattr(new_processor.feature_extractor				,       UpperCamelCase__		)		)
									self.assertDictEqual(new_processor.tokenizer.get_vocab()				,       processor.tokenizer.get_vocab()		)





	def   __snake_case				(			self		):
					CustomFeatureExtractor.register_for_auto_class()
					CustomTokenizer.register_for_auto_class()
					CustomProcessor.register_for_auto_class()

					A__  :	Optional[Any]         =							CustomFeatureExtractor.from_pretrained(UpperCamelCase__		)

					with tempfile.TemporaryDirectory() as tmp_dir:
									A__  :	List[Any]         =							os.path.join(UpperCamelCase__				,       '''vocab.txt'''		)
									with open(UpperCamelCase__				,       '''w'''				,       encoding='''utf-8'''		) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]		)		)
									A__  :	Union[str, Any]         =							CustomTokenizer(UpperCamelCase__		)

					A__  :	List[Any]         =							CustomProcessor(UpperCamelCase__				,       UpperCamelCase__		)

					with tempfile.TemporaryDirectory() as tmp_dir:
									create_repo(F"{USER}/test-dynamic-processor"				,       token=self._token		)
									A__  :	Union[str, Any]         =							Repository(UpperCamelCase__				,       clone_from=F"{USER}/test-dynamic-processor"				,       token=self._token		)
									processor.save_pretrained(UpperCamelCase__		)

									# This has added the proper auto_map field to the feature extractor config
									self.assertDictEqual(
									    processor.feature_extractor.auto_map				,       {
									        '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
									        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
									    }				,       )

									# This has added the proper auto_map field to the tokenizer config
									with open(os.path.join(UpperCamelCase__				,       '''tokenizer_config.json'''		)		) as f:
													A__  :	Optional[int]         =							json.load(UpperCamelCase__		)
									self.assertDictEqual(
									    tokenizer_config['''auto_map''']				,       {
									        '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
									        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
									    }				,       )

									# The code has been copied from fixtures
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,       '''custom_feature_extraction.py'''		)		)		)
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,       '''custom_tokenization.py'''		)		)		)
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,       '''custom_processing.py'''		)		)		)

									repo.push_to_hub()

					A__  :	Tuple         =							AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor"				,       trust_remote_code=UpperCamelCase__		)
					# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
					self.assertEqual(new_processor.__class__.__name__				,       '''CustomProcessor'''		)