'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							_lowerCamelCase						,						unittest.TestCase					):
						A_			:							Optional[int]     		= UnCLIPImageVariationPipeline
						A_			:							Dict     		= IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
						A_			:							int     		= IMAGE_VARIATION_BATCH_PARAMS

						A_			:							Tuple     		= [
						    '''generator''',
						    '''return_dict''',
						    '''decoder_num_inference_steps''',
						    '''super_res_num_inference_steps''',
						]
						A_			:							Union[str, Any]     		= False




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				str:
													return 32




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Union[str, Any]:
													return 32




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													return self.time_input_dim




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.time_input_dim * 4




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				int:
													return 1_00




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
													return tokenizer




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				str:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = CLIPTextConfig(
													    bos_token_id=0							,							eos_token_id=2							,							hidden_size=self.text_embedder_hidden_size							,							projection_dim=self.text_embedder_hidden_size							,							intermediate_size=37							,							layer_norm_eps=1e-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=10_00							,							)
													return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE      )




						@property
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Dict:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size							,							projection_dim=self.text_embedder_hidden_size							,							num_hidden_layers=5							,							num_attention_heads=4							,							image_size=32							,							intermediate_size=37							,							patch_size=1							,							)
													return CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE      )




						@property
						def      __lowerCamelCase       (    self :       str      )     ->				Optional[Any]:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :Dict						    = {
													    'clip_embeddings_dim': self.text_embedder_hidden_size,
													    'time_embed_dim': self.time_embed_dim,
													    'cross_attention_dim': self.cross_attention_dim,
													}

													SCREAMING_SNAKE_CASE__     :List[Any]						    = UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE      )
													return model




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				Any:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = {
													    'sample_size': 32,
													    # RGB in channels
													    'in_channels': 3,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 6,
													    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
													    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
													    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
													    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    'layers_per_block': 1,
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': 'identity',
													}

													SCREAMING_SNAKE_CASE__     :Tuple						    = UNetaDConditionModel(**_SCREAMING_SNAKE_CASE      )
													return model




						@property
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													return {
													    "sample_size": 64,
													    "layers_per_block": 1,
													    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
													    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
													    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    "in_channels": 6,
													    "out_channels": 3,
													}




						@property
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Any:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :str						    = UNetaDModel(**self.dummy_super_res_kwargs      )
													return model




						@property
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				str:
													torch.manual_seed(1      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = UNetaDModel(**self.dummy_super_res_kwargs      )
													return model




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.dummy_decoder
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.dummy_text_proj
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.dummy_text_encoder
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.dummy_tokenizer
													SCREAMING_SNAKE_CASE__     :int						    = self.dummy_super_res_first
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dummy_super_res_last

													SCREAMING_SNAKE_CASE__     :Any						    = UnCLIPScheduler(
													    variance_type='learned_range'							,							prediction_type='epsilon'							,							num_train_timesteps=10_00							,							)

													SCREAMING_SNAKE_CASE__     :List[Any]						    = UnCLIPScheduler(
													    variance_type='fixed_small_log'							,							prediction_type='epsilon'							,							num_train_timesteps=10_00							,							)

													SCREAMING_SNAKE_CASE__     :Tuple						    = CLIPImageProcessor(crop_size=32							,							size=32      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.dummy_image_encoder

													return {
													    "decoder": decoder,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "text_proj": text_proj,
													    "feature_extractor": feature_extractor,
													    "image_encoder": image_encoder,
													    "super_res_first": super_res_first,
													    "super_res_last": super_res_last,
													    "decoder_scheduler": decoder_scheduler,
													    "super_res_scheduler": super_res_scheduler,
													}




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       int=0							,							UpperCamelCase_ :       Union[str, Any]=True      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = floats_tensor((1, 3, 32, 32)							,							rng=random.Random(_SCREAMING_SNAKE_CASE      )      ).to(_SCREAMING_SNAKE_CASE      )
													if str(_SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :int						    = torch.manual_seed(_SCREAMING_SNAKE_CASE      )
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device=_SCREAMING_SNAKE_CASE      ).manual_seed(_SCREAMING_SNAKE_CASE      )

													if pil_image:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = input_image * 0.5 + 0.5
																				SCREAMING_SNAKE_CASE__     :List[str]						    = input_image.clamp(0							,							1      )
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = input_image.cpu().permute(0							,							2							,							3							,							1      ).float().numpy()
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE      )[0]

													return {
													    "image": input_image,
													    "generator": generator,
													    "decoder_num_inference_steps": 2,
													    "super_res_num_inference_steps": 2,
													    "output_type": "np",
													}




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :str						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.pipeline_class(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = pipe.to(_SCREAMING_SNAKE_CASE      )

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = pipe(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = output.images

													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe(
													    **_SCREAMING_SNAKE_CASE							,							return_dict=_SCREAMING_SNAKE_CASE							,							)[0]

													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array(
													    [
													        0.9997,
													        0.0002,
													        0.9997,
													        0.9997,
													        0.9969,
													        0.0023,
													        0.9997,
													        0.9969,
													        0.9970,
													    ]      )

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




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Dict						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.pipeline_class(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe.to(_SCREAMING_SNAKE_CASE      )

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :str						    = pipe(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = output.images

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe(
													    **_SCREAMING_SNAKE_CASE							,							return_dict=_SCREAMING_SNAKE_CASE							,							)[0]

													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]      )

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




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :str						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.pipeline_class(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :str						    = pipe.to(_SCREAMING_SNAKE_CASE      )

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :str						    = [
													    pipeline_inputs['image'],
													    pipeline_inputs['image'],
													]

													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = output.images

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = [
													    tuple_pipeline_inputs['image'],
													    tuple_pipeline_inputs['image'],
													]

													SCREAMING_SNAKE_CASE__     :str						    = pipe(
													    **_SCREAMING_SNAKE_CASE							,							return_dict=_SCREAMING_SNAKE_CASE							,							)[0]

													SCREAMING_SNAKE_CASE__     :str						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :str						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (2, 64, 64, 3)

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array(
													    [
													        0.9997,
													        0.9989,
													        0.0008,
													        0.0021,
													        0.9960,
													        0.0018,
													        0.0014,
													        0.0002,
													        0.9933,
													    ]      )

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




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.device('cpu'      )




													class _SCREAMING_SNAKE_CASE:
																			A_			:							Dict     		= 1
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.pipeline_class(**_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = pipe.to(_SCREAMING_SNAKE_CASE      )

													pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Any						    = torch.Generator(device=_SCREAMING_SNAKE_CASE      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe.decoder.dtype
													SCREAMING_SNAKE_CASE__     :Dict						    = 1

													SCREAMING_SNAKE_CASE__     :List[Any]						    = (
													    batch_size,
													    pipe.decoder.config.in_channels,
													    pipe.decoder.config.sample_size,
													    pipe.decoder.config.sample_size,
													)
													SCREAMING_SNAKE_CASE__     :int						    = pipe.prepare_latents(
													    _SCREAMING_SNAKE_CASE							,							dtype=_SCREAMING_SNAKE_CASE							,							device=_SCREAMING_SNAKE_CASE							,							generator=_SCREAMING_SNAKE_CASE							,							latents=_SCREAMING_SNAKE_CASE							,							scheduler=DummyScheduler()      )

													SCREAMING_SNAKE_CASE__     :Any						    = (
													    batch_size,
													    pipe.super_res_first.config.in_channels // 2,
													    pipe.super_res_first.config.sample_size,
													    pipe.super_res_first.config.sample_size,
													)
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe.prepare_latents(
													    _SCREAMING_SNAKE_CASE							,							dtype=_SCREAMING_SNAKE_CASE							,							device=_SCREAMING_SNAKE_CASE							,							generator=_SCREAMING_SNAKE_CASE							,							latents=_SCREAMING_SNAKE_CASE							,							scheduler=DummyScheduler()      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe(
													    **_SCREAMING_SNAKE_CASE							,							decoder_latents=_SCREAMING_SNAKE_CASE							,							super_res_latents=_SCREAMING_SNAKE_CASE      ).images

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							,							pil_image=_SCREAMING_SNAKE_CASE      )
													# Don't pass image, instead pass embedding
													SCREAMING_SNAKE_CASE__     :str						    = pipeline_inputs.pop('image'      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe.image_encoder(_SCREAMING_SNAKE_CASE      ).image_embeds

													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe(
													    **_SCREAMING_SNAKE_CASE							,							decoder_latents=_SCREAMING_SNAKE_CASE							,							super_res_latents=_SCREAMING_SNAKE_CASE							,							image_embeddings=_SCREAMING_SNAKE_CASE							,							).images

													# make sure passing text embeddings manually is identical
													assert np.abs(img_out_a - img_out_a      ).max() < 1e-4




						@skip_mps
						def      __lowerCamelCase       (    self :       Any      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :str						    = torch_device == 'cpu'

													# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
													SCREAMING_SNAKE_CASE__     :int						    = 1e-2

													self._test_attention_slicing_forward_pass(
													    test_max_difference=_SCREAMING_SNAKE_CASE							,							expected_max_diff=_SCREAMING_SNAKE_CASE      )




						@skip_mps
						def      __lowerCamelCase       (    self :       str      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch_device == 'cpu'
													SCREAMING_SNAKE_CASE__     :int						    = True
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [
													    'decoder_num_inference_steps',
													    'super_res_num_inference_steps',
													]

													self._test_inference_batch_single_identical(
													    test_max_difference=_SCREAMING_SNAKE_CASE							,							relax_max_difference=_SCREAMING_SNAKE_CASE							,							additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE							,							)




						def      __lowerCamelCase       (    self :       Any      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Any						    = [
													    'decoder_num_inference_steps',
													    'super_res_num_inference_steps',
													]

													if torch_device == "mps":
																				# TODO: MPS errors with larger batch sizes
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [2, 3]
																				self._test_inference_batch_consistent(
																				    batch_sizes=_SCREAMING_SNAKE_CASE							,							additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE							,							)
													else:
																				self._test_inference_batch_consistent(
																				    additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE      )




						@skip_mps
						def      __lowerCamelCase       (    self :       str      )     ->				Any:
													return super().test_dict_tuple_outputs_equivalent()




						@skip_mps
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Dict:
													return super().test_save_load_local()




						@skip_mps
						def      __lowerCamelCase       (    self :       int      )     ->				List[str]:
													return super().test_save_load_optional_components()




@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Tuple:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       str      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = UnCLIPImageVariationPipeline.from_pretrained(
													    'kakaobrain/karlo-v1-alpha-image-variations'							,							torch_dtype=torch.floataa      )
													SCREAMING_SNAKE_CASE__     :str						    = pipeline.to(_SCREAMING_SNAKE_CASE      )
													pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.Generator(device='cpu'      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipeline(
													    _SCREAMING_SNAKE_CASE							,							generator=_SCREAMING_SNAKE_CASE							,							output_type='np'							,							)

													SCREAMING_SNAKE_CASE__     :List[Any]						    = output.images[0]

													assert image.shape == (2_56, 2_56, 3)

													assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							15      )











"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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


A_						:    int				=      get_tests_dir('fixtures')
A_						:    int				=      get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A_						:    Dict				=      get_tests_dir('fixtures/dummy-config.json')
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     0





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    ).to_dict()

									config_dict.pop('feature_extractor_type'    )
									SCREAMING_SNAKE_CASE						:							int							=     WavaVecaFeatureExtractor(**_SCREAMING_SNAKE_CASE    )

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

									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )

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

					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       'bert-base is not a local folder and is not a valid model identifier'    ):
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained('bert-base'    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	str:





					"""simple docstring"""


					with self.assertRaisesRegex(
					    _SCREAMING_SNAKE_CASE     ,       R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'    ):
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       revision='aaaaaa'    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	List[Any]:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	str:





					"""simple docstring"""


					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							int							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
					    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )

					# Test feature extractor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(reloaded_feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[int]:





					"""simple docstring"""


					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
													AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

									# Now that the config is registered, it can be used as any other config with the auto-API
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     CustomFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE    )
													self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					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]







	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					class     lowerCAmelCase__						(	_lowerCamelCase ):


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE	:		str  						= True

					try:
									AutoConfig.register('custom'     ,       _SCREAMING_SNAKE_CASE    )
									AutoFeatureExtractor.register(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									# If remote code is not set, the default is to use local
									SCREAMING_SNAKE_CASE						:							str							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote code is disabled, we load the local one.
									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(feature_extractor.is_local    )

									# If remote is enabled, we load from the Hub
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'     ,       trust_remote_code=_SCREAMING_SNAKE_CASE    )
									self.assertEqual(feature_extractor.__class__.__name__     ,       'NewFeatureExtractor'    )
									self.assertTrue(not hasattr(_SCREAMING_SNAKE_CASE     ,       'is_local'    )    )

					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]





