

"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      _A	(						__lowerCAmelCase							, __lowerCAmelCase							, __lowerCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					AltDiffusionPipeline
    UpperCamelCase_	:					Tuple							=					TEXT_TO_IMAGE_PARAMS
    UpperCamelCase_	:					Optional[int]							=					TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCamelCase_	:					Dict							=					TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCamelCase_	:					Any							=					TEXT_TO_IMAGE_IMAGE_PARAMS
    def  lowercase					(		self							:     int    )    ->       Dict:
         torch.manual_seed(0    )
         __snake_case				=							UNetaDConditionModel(
             block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=4	,      out_channels=4	,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,      cross_attention_dim=32	,      )
         __snake_case				=							DDIMScheduler(
             beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule='''scaled_linear'''	,      clip_sample=lowerCamelCase__	,      set_alpha_to_one=lowerCamelCase__	,      )
         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	,      )

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0    )
         __snake_case				=							CLIPTextConfig(
             bos_token_id=0	,      eos_token_id=2	,      hidden_size=32	,      projection_dim=32	,      intermediate_size=37	,      layer_norm_eps=1E-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=5_002	,      )
         __snake_case				=							CLIPTextModel(lowerCamelCase__    )

         __snake_case				=							XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''    )
         __snake_case				=							77

         __snake_case				=							{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
    def  lowercase					(		self							:     Tuple	,      A_							:     Optional[int]	,      A_							:     Tuple=0    )    ->       Union[str, Any]:






         if str(lowerCamelCase__    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(lowerCamelCase__    )
         else:
              __snake_case				=							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
         __snake_case				=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )
    def  lowercase					(		self							:     List[str]    )    ->       Dict:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3    )
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case				=							self.get_dummy_components()
         torch.manual_seed(0    )
         __snake_case				=							RobertaSeriesConfig(
             hidden_size=32	,      project_dim=32	,      intermediate_size=37	,      layer_norm_eps=1E-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      vocab_size=5_002	,      )
         # TODO: remove after fixing the non-deterministic text encoder
         __snake_case				=							RobertaSeriesModelWithTransformation(lowerCamelCase__    )
         __snake_case				=							text_encoder

         __snake_case				=							AltDiffusionPipeline(**lowerCamelCase__    )
         __snake_case				=							alt_pipe.to(lowerCamelCase__    )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

         __snake_case				=							self.get_dummy_inputs(lowerCamelCase__    )
         __snake_case				=							'''A photo of an astronaut'''
         __snake_case				=							alt_pipe(**lowerCamelCase__    )
         __snake_case				=							output.images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __snake_case				=							np.array(
             [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]    )

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







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case				=							self.get_dummy_components()
         __snake_case				=							PNDMScheduler(skip_prk_steps=lowerCamelCase__    )
         torch.manual_seed(0    )
         __snake_case				=							RobertaSeriesConfig(
             hidden_size=32	,      project_dim=32	,      intermediate_size=37	,      layer_norm_eps=1E-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      vocab_size=5_002	,      )
         # TODO: remove after fixing the non-deterministic text encoder
         __snake_case				=							RobertaSeriesModelWithTransformation(lowerCamelCase__    )
         __snake_case				=							text_encoder
         __snake_case				=							AltDiffusionPipeline(**lowerCamelCase__    )
         __snake_case				=							alt_pipe.to(lowerCamelCase__    )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

         __snake_case				=							self.get_dummy_inputs(lowerCamelCase__    )
         __snake_case				=							alt_pipe(**lowerCamelCase__    )
         __snake_case				=							output.images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __snake_case				=							np.array(
             [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]    )

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






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




    """simple docstring"""




    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,      safety_checker=lowerCamelCase__    )
         __snake_case				=							alt_pipe.to(lowerCamelCase__    )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							alt_pipe([prompt]	,      generator=lowerCamelCase__	,      guidance_scale=6.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]    )

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







    def  lowercase					(		self							:     Any    )    ->       List[Any]:
         __snake_case				=							DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''	,      subfolder='''scheduler'''    )

         __snake_case				=							AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,      scheduler=lowerCamelCase__	,      safety_checker=lowerCamelCase__    )
         __snake_case				=							alt_pipe.to(lowerCamelCase__    )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							alt_pipe([prompt]	,      generator=lowerCamelCase__	,      num_inference_steps=2	,      output_type='''numpy'''    )
         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]    )

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



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         with open(self.vocab_file	,      '''w'''	,      encoding='''utf-8'''    ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': {'''height''': 224, '''width''': 224},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 18, '''width''': 18},
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
             '''do_convert_rgb''': True,
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     str	,      **A_							:     str    )    ->       Dict:
         return BertTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     Optional[int]    )    ->       Tuple:
         return BertTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     Any    )    ->       Any:
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       int:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Dict    )    ->       Dict:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     Tuple    )    ->       Any:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         __snake_case				=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(cls_token='''(CLS)'''	,      sep_token='''(SEP)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_    )

         __snake_case				=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname	,      cls_token='''(CLS)'''	,      sep_token='''(SEP)'''	,      do_normalize=A_    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      processor.model_input_names    )