


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase      =   random.Random()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  Dict:




      """simple docstring"""

      if rng is None:
            snake_case_	=				global_rng

      snake_case_	=				[]
      for batch_idx in range(shape[0] ):
            values.append([] )
            for _ in range(shape[1] ):
                  values[-1].append(rng.random() * scale )

      return values







@require_torch
@require_torchaudio
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=20_00		,				_UpperCAmelCase=24		,				_UpperCAmelCase=24		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=1_60_00		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				min_seq_length
       snake_case_	=				max_seq_length
       snake_case_	=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       snake_case_	=				feature_size
       snake_case_	=				num_mel_bins
       snake_case_	=				padding_value
       snake_case_	=				sampling_rate
       snake_case_	=				return_attention_mask
       snake_case_	=				do_normalize



 def 						UpperCamelCase__					(     self ):
       return {
           "feature_size": self.feature_size,
           "num_mel_bins": self.num_mel_bins,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False ):
       def _flatten(_UpperCAmelCase ):
             return list(itertools.chain(*_UpperCAmelCase ) )

       if equal_length:
             snake_case_	=				[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
       else:
             # make sure that inputs increase in size
             snake_case_	=				[
                 floats_list((x, self.feature_size) )
                 for x in range(self.min_seq_length		,				self.max_seq_length		,				self.seq_length_diff )
             ]
       if numpify:
             snake_case_	=				[np.asarray(_UpperCAmelCase ) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  SpeechaTextFeatureExtractor if is_speech_available() else None



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				SpeechaTextFeatureExtractionTester(self )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       self.assertTrue(np.all(np.mean(_UpperCAmelCase		,				axis=0 ) < 1E-3 ) )
       self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase		,				axis=0 ) - 1 ) < 1E-3 ) )



 def 						UpperCamelCase__					(     self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				[np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]

       # Test feature size
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       self.assertTrue(input_features.ndim == 3 )
       self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )

       # Test not batched input
       snake_case_	=				feature_extractor(speech_inputs[0]		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(np_speech_inputs[0]		,				return_tensors='''np''' ).input_features
       self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )

       # Test batched
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )

       # Test 2-D numpy arrays are batched.
       snake_case_	=				[floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
       snake_case_	=				np.asarray(_UpperCAmelCase )
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]

       snake_case_	=				['''longest''', '''max_length''', '''do_not_pad''']
       snake_case_	=				[None, 16, None]
       for max_length, padding in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				feature_extractor(
                 _UpperCAmelCase		,				padding=_UpperCAmelCase		,				max_length=_UpperCAmelCase		,				return_attention_mask=_UpperCAmelCase )
             snake_case_	=				inputs.input_features
             snake_case_	=				inputs.attention_mask
             snake_case_	=				[np.sum(_UpperCAmelCase ) for x in attention_mask]

             self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
             self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
             self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]

       snake_case_	=				['''longest''', '''max_length''', '''do_not_pad''']
       snake_case_	=				[None, 16, None]
       for max_length, padding in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				feature_extractor(
                 _UpperCAmelCase		,				max_length=_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase )
             snake_case_	=				inputs.input_features
             snake_case_	=				inputs.attention_mask
             snake_case_	=				[np.sum(_UpperCAmelCase ) for x in attention_mask]

             self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
             self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
             self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
             self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
             self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=4		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				np.sum(attention_mask == 1		,				axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1] )
       self._check_zero_mean_unit_variance(input_features[2] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''longest'''		,				max_length=4		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				np.sum(attention_mask == 1		,				axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
       self._check_zero_mean_unit_variance(input_features[2] )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape		,				(3, 4, 24) )

       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''longest'''		,				max_length=16		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				np.sum(attention_mask == 1		,				axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
       self._check_zero_mean_unit_variance(input_features[2] )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape		,				(3, 6, 24) )



 def 						UpperCamelCase__					(     self ):
       import torch

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				np.random.rand(1_00		,				32 ).astype(np.floataa )
       snake_case_	=				np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             snake_case_	=				feature_extractor.pad([{'''input_features''': inputs}]		,				return_tensors='''np''' )
             self.assertTrue(np_processed.input_features.dtype == np.floataa )
             snake_case_	=				feature_extractor.pad([{'''input_features''': inputs}]		,				return_tensors='''pt''' )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       from datasets import load_dataset

       snake_case_	=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,				'''clean'''		,				split='''validation''' )
       # automatic decoding with librispeech
       snake_case_	=				ds.sort('''id''' ).select(range(_UpperCAmelCase ) )[:num_samples]['''audio''']

       return [x["array"] for x in speech_samples]





 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				np.array([
           -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
           -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
           -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
       ] )
       # fmt: on

       snake_case_	=				self._load_datasamples(1 )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''pt''' ).input_features
       self.assertEquals(input_features.shape		,				(1, 5_84, 24) )
       self.assertTrue(np.allclose(input_features[0, 0, :30]		,				_UpperCAmelCase		,				atol=1E-4 ) )



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				self.get_dummy_inputs(_UpperCAmelCase )
       snake_case_	=				sd_pipe(**_UpperCAmelCase ).images
       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9