
import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Any	     =				[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]    )

class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Any=    StableDiffusionLatentUpscalePipeline
    _a		:						List[str]=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        "height",
        "width",
        "cross_attention_kwargs",
        "negative_prompt_embeds",
        "prompt_embeds",
    }
    _a		:						Optional[Any]=    PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
    _a		:						Tuple=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _a		:						int=    frozenset(
        []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    _a		:						Optional[int]=    frozenset([]					)

    _a		:						List[str]=    True
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				1
           lowercase							:					Dict	     =				4
           lowercase							:					Union[str, Any]	     =				(16, 16)

           lowercase							:					Optional[Any]	     =				floats_tensor((batch_size, num_channels) + sizes		,rng=random.Random(0				)				).to(snake_case				)
           return image
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           torch.manual_seed(0				)
           lowercase							:					Union[str, Any]	     =				UNetaDConditionModel(
               act_fn="""gelu"""		,attention_head_dim=8		,norm_num_groups=snake_case		,block_out_channels=[32, 32, 64, 64]		,time_cond_proj_dim=160		,conv_in_kernel=1		,conv_out_kernel=1		,cross_attention_dim=32		,down_block_types=(
                   """KDownBlock2D""",
                   """KCrossAttnDownBlock2D""",
                   """KCrossAttnDownBlock2D""",
                   """KCrossAttnDownBlock2D""",
               )		,in_channels=8		,mid_block_type=snake_case		,only_cross_attention=snake_case		,out_channels=5		,resnet_time_scale_shift="""scale_shift"""		,time_embedding_type="""fourier"""		,timestep_post_act="""gelu"""		,up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")		,)
           lowercase							:					Optional[Any]	     =				AutoencoderKL(
               block_out_channels=[32, 32, 64, 64]		,in_channels=3		,out_channels=3		,down_block_types=[
                   """DownEncoderBlock2D""",
                   """DownEncoderBlock2D""",
                   """DownEncoderBlock2D""",
                   """DownEncoderBlock2D""",
               ]		,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,latent_channels=4		,)
           lowercase							:					int	     =				EulerDiscreteScheduler(prediction_type="""sample"""				)
           lowercase							:					Optional[int]	     =				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=1000		,hidden_act="""quick_gelu"""		,projection_dim=512		,)
           lowercase							:					str	     =				CLIPTextModel(snake_case				)
           lowercase							:					List[Any]	     =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

           lowercase							:					Optional[int]	     =				{
               """unet""": model.eval(),
               """vae""": vae.eval(),
               """scheduler""": scheduler,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }

           return components
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=0				):



           '''simple docstring'''







           if str(snake_case				).startswith("""mps"""				):
                  lowercase							:					Tuple	     =				torch.manual_seed(snake_case				)
           else:
                  lowercase							:					Tuple	     =				torch.Generator(device=snake_case				).manual_seed(snake_case				)
           lowercase							:					Optional[int]	     =				{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": self.dummy_image.cpu(),
               """generator""": generator,
               """num_inference_steps""": 2,
               """output_type""": """numpy""",
           }
           return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				"""cpu"""

           lowercase							:					List[Any]	     =				self.get_dummy_components()
           lowercase							:					Dict	     =				self.pipeline_class(**snake_case				)
           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					Optional[Any]	     =				self.get_dummy_inputs(snake_case				)
           lowercase							:					Any	     =				pipe(**snake_case				).images
           lowercase							:					Dict	     =				image[0, -3:, -3:, -1]

           self.assertEqual(image.shape		,(1, 256, 256, 3)				)
           lowercase							:					Optional[int]	     =				np.array(
               [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]				)
           lowercase							:					List[Any]	     =				np.abs(image_slice.flatten() - expected_slice				).max()
           self.assertLessEqual(snake_case		,1e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_attention_slicing_forward_pass(expected_max_diff=7e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_cpu_offload_forward_pass(expected_max_diff=3e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_inference_batch_single_identical(expected_max_diff=7e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_save_load_local(expected_max_difference=3e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().test_save_load_optional_components(expected_max_difference=3e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				[
               """DDIMScheduler""",
               """DDPMScheduler""",
               """PNDMScheduler""",
               """HeunDiscreteScheduler""",
               """EulerAncestralDiscreteScheduler""",
               """KDPM2DiscreteScheduler""",
               """KDPM2AncestralDiscreteScheduler""",
               """DPMSolverSDEScheduler""",
           ]
           lowercase							:					List[Any]	     =				self.get_dummy_components()
           lowercase							:					Tuple	     =				self.pipeline_class(**snake_case				)

           # make sure that PNDM does not need warm-up
           pipe.scheduler.register_to_config(skip_prk_steps=snake_case				)

           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)
           lowercase							:					Tuple	     =				self.get_dummy_inputs(snake_case				)
           lowercase							:					str	     =				2

           lowercase							:					Dict	     =				[]
           for scheduler_enum in KarrasDiffusionSchedulers:
                  if scheduler_enum.name in skip_schedulers:
                         # no sigma schedulers are not supported
                         # no schedulers
                         continue

                  lowercase							:					Tuple	     =				getattr(snake_case		,scheduler_enum.name				)
                  lowercase							:					int	     =				scheduler_cls.from_config(pipe.scheduler.config				)
                  lowercase							:					Dict	     =				pipe(**snake_case				)[0]
                  outputs.append(snake_case				)

           assert check_same_shape(snake_case				)






@require_torch_gpu
@slow
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				torch.manual_seed(33				)

           lowercase							:					Union[str, Any]	     =				StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""		,torch_dtype=torch.floataa				)
           pipe.to("""cuda"""				)

           lowercase							:					List[str]	     =				StableDiffusionLatentUpscalePipeline.from_pretrained(
               """stabilityai/sd-x2-latent-upscaler"""		,torch_dtype=torch.floataa				)
           upscaler.to("""cuda"""				)

           lowercase							:					List[str]	     =				"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

           lowercase							:					Any	     =				pipe(snake_case		,generator=snake_case		,output_type="""latent"""				).images

           lowercase							:					Union[str, Any]	     =				upscaler(
               prompt=snake_case		,image=snake_case		,num_inference_steps=20		,guidance_scale=0		,generator=snake_case		,output_type="""np"""		,).images[0]

           lowercase							:					Optional[int]	     =				load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"""				)
           assert np.abs((expected_image - image).mean()				) < 5e-2
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				torch.manual_seed(33				)

           lowercase							:					Tuple	     =				StableDiffusionLatentUpscalePipeline.from_pretrained(
               """stabilityai/sd-x2-latent-upscaler"""		,torch_dtype=torch.floataa				)
           upscaler.to("""cuda"""				)

           lowercase							:					str	     =				"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

           lowercase							:					Union[str, Any]	     =				load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""				)

           lowercase							:					List[Any]	     =				upscaler(
               prompt=snake_case		,image=snake_case		,num_inference_steps=20		,guidance_scale=0		,generator=snake_case		,output_type="""np"""		,).images[0]

           lowercase							:					Optional[int]	     =				load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"""				)
           assert np.abs((expected_image - image).max()				) < 5e-2


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
