





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@property
							def      A_  (      self				:     Union[str, Any]    )	->str:
													torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					UNetaDModel(
													    block_out_channels=(32, 64)							,	layers_per_block=2							,	sample_size=32							,	in_channels=3							,	out_channels=3							,	down_block_types=("DownBlock2D", "AttnDownBlock2D")							,	up_block_types=("AttnUpBlock2D", "UpBlock2D")							,	)
													return model






							def      A_  (      self				:     Dict    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.dummy_uncond_unet
													SCREAMING_SNAKE_CASE__     :				Tuple   =					PNDMScheduler()

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					PNDMPipeline(unet=a							,	scheduler=a    )
													pndm.to(a    )
													pndm.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Any   =					pndm(generator=a							,	num_inference_steps=20							,	output_type="numpy"    ).images

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pndm(generator=a							,	num_inference_steps=20							,	output_type="numpy"							,	return_dict=a    )[0]

													SCREAMING_SNAKE_CASE__     :				Dict   =					image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :				Tuple   =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2







@slow
@require_torch
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"google/ddpm-cifar10-32"

													SCREAMING_SNAKE_CASE__     :				int   =					UNetaDModel.from_pretrained(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					PNDMScheduler()

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					PNDMPipeline(unet=a							,	scheduler=a    )
													pndm.to(a    )
													pndm.set_progress_bar_config(disable=a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.manual_seed(0    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					pndm(generator=a							,	output_type="numpy"    ).images

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :				int   =					np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]    )

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2






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


__lowercase       :List[str]       =  get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowercase       :str       =  get_tests_dir("fixtures/vocab.json")
__lowercase       :Optional[int]       =  get_tests_dir("fixtures")



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

							def      A_  (      self				:     Optional[Any]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Dict   =					0

							def      A_  (      self				:     Any    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )
													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Union[str, Any]    )	->List[str]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					WavaVecaConfig()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )

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

																			SCREAMING_SNAKE_CASE__     :				str   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     int    )	->List[str]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			# copy relevant files
																			copyfile(a							,	os.path.join(a							,	a    )    )
																			copyfile(a							,	os.path.join(a							,	"vocab.json"    )    )

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     List[Any]    )	->Tuple:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					WavaVecaFeatureExtractor()
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"    )

																			SCREAMING_SNAKE_CASE__     :				Any   =					WavaVecaProcessor(a							,	a    )

																			# save in new folder
																			processor.save_pretrained(a    )

																			# drop `processor_class` in tokenizer
																			with open(os.path.join(a							,	a    )							,	"r"    ) as f:
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					json.load(a    )
																									config_dict.pop("processor_class"    )

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

																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     List[str]    )	->Optional[Any]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					WavaVecaFeatureExtractor()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					WavaVecaProcessor(a							,	a    )

																			# save in new folder
																			processor.save_pretrained(a    )

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

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

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Union[str, Any]    )	->str:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					WavaVecaConfig(processor_class="Wav2Vec2Processor"    )
																			model_config.save_pretrained(a    )
																			# copy relevant files
																			copyfile(a							,	os.path.join(a							,	"vocab.json"    )    )
																			# create emtpy sample processor
																			with open(os.path.join(a							,	a    )							,	"w"    ) as f:
																									f.write("{}"    )

																			SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Optional[Any]    )	->Optional[int]:
													# If remote code is not set, we will time out when asking whether to load the model.
													with self.assertRaises(a    ):
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"    )
													# If remote code is disabled, we can't load this config.
													with self.assertRaises(a    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a    )
													self.assertTrue(processor.special_attribute_present    )
													self.assertEqual(processor.__class__.__name__							,	"NewProcessor"    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					processor.feature_extractor
													self.assertTrue(feature_extractor.special_attribute_present    )
													self.assertEqual(feature_extractor.__class__.__name__							,	"NewFeatureExtractor"    )

													SCREAMING_SNAKE_CASE__     :				Optional[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
																			SCREAMING_SNAKE_CASE__     :				int   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a							,	use_fast=a    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					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      A_  (      self				:     Tuple    )	->List[Any]:
													try:
																			AutoConfig.register("custom"							,	a    )
																			AutoFeatureExtractor.register(a							,	a    )
																			AutoTokenizer.register(a							,	slow_tokenizer_class=a    )
																			AutoProcessor.register(a							,	a    )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(a    ):
																									AutoProcessor.register(a							,	a    )

																			# Now that the config is registered, it can be used as any other config with the auto-API
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					CustomFeatureExtractor.from_pretrained(a    )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																									SCREAMING_SNAKE_CASE__     :				int   =					os.path.join(a							,	"vocab.txt"    )
																									with open(a							,	"w"							,	encoding="utf-8"    ) as vocab_writer:
																															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]    )    )
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					CustomTokenizer(a    )

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					CustomProcessor(a							,	a    )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																									processor.save_pretrained(a    )
																									SCREAMING_SNAKE_CASE__     :				Any   =					AutoProcessor.from_pretrained(a    )
																									self.assertIsInstance(a							,	a    )

													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      A_  (      self				:     Union[str, Any]    )	->int:



													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						False







													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						False







													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						"AutoFeatureExtractor"
																				snake_case_							 =						"AutoTokenizer"
																				snake_case_							 =						False

													try:
																			AutoConfig.register("custom"							,	a    )
																			AutoFeatureExtractor.register(a							,	a    )
																			AutoTokenizer.register(a							,	slow_tokenizer_class=a    )
																			AutoProcessor.register(a							,	a    )
																			# If remote code is not set, the default is to use local classes.
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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.
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a    )
																			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.
																			SCREAMING_SNAKE_CASE__     :				Any   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a    )
																			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      A_  (      self				:     Optional[Any]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"    )
													self.assertEqual(processor.__class__.__name__							,	"BertTokenizerFast"    )






							def      A_  (      self				:     Dict    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"    )
													self.assertEqual(processor.__class__.__name__							,	"ConvNextImageProcessor"    )







@is_staging_test
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

							@classmethod
							def      A_  (      cls				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					TOKEN
													HfFolder.save_token(a    )

							@classmethod
							def      A_  (      cls				:     List[str]    )	->Optional[int]:
													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      A_  (      self				:     Dict    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					WavaVecaProcessor.from_pretrained(a    )
													with tempfile.TemporaryDirectory() as tmp_dir:
																			processor.save_pretrained(
																			    os.path.join(a							,	"test-processor"    )							,	push_to_hub=a							,	use_auth_token=self._token    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""    )
																			for k, v in processor.feature_extractor.__dict__.items():
																									self.assertEqual(a							,	getattr(new_processor.feature_extractor							,	a    )    )
																			self.assertDictEqual(new_processor.tokenizer.get_vocab()							,	processor.tokenizer.get_vocab()    )

							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					WavaVecaProcessor.from_pretrained(a    )

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

																			SCREAMING_SNAKE_CASE__     :				Dict   =					WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"    )
																			for k, v in processor.feature_extractor.__dict__.items():
																									self.assertEqual(a							,	getattr(new_processor.feature_extractor							,	a    )    )
																			self.assertDictEqual(new_processor.tokenizer.get_vocab()							,	processor.tokenizer.get_vocab()    )






							def      A_  (      self				:     Any    )	->int:
													CustomFeatureExtractor.register_for_auto_class()
													CustomTokenizer.register_for_auto_class()
													CustomProcessor.register_for_auto_class()

													SCREAMING_SNAKE_CASE__     :				Any   =					CustomFeatureExtractor.from_pretrained(a    )

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

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					CustomProcessor(a							,	a    )

													with tempfile.TemporaryDirectory() as tmp_dir:
																			create_repo(f"""{USER}/test-dynamic-processor"""							,	token=self._token    )
																			SCREAMING_SNAKE_CASE__     :				str   =					Repository(a							,	clone_from=f"""{USER}/test-dynamic-processor"""							,	token=self._token    )
																			processor.save_pretrained(a    )

																			# 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(a							,	"tokenizer_config.json"    )    ) as f:
																									SCREAMING_SNAKE_CASE__     :				str   =					json.load(a    )
																			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(a							,	"custom_feature_extraction.py"    )    )    )
																			self.assertTrue(os.path.isfile(os.path.join(a							,	"custom_tokenization.py"    )    )    )
																			self.assertTrue(os.path.isfile(os.path.join(a							,	"custom_processing.py"    )    )    )

																			repo.push_to_hub()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""							,	trust_remote_code=a    )
													# 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"    )