


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							):
        lowercase_       :Dict     =       parent
        lowercase_       :Optional[Any]     =       13
        lowercase_       :Optional[Any]     =       7
        lowercase_       :List[Any]     =       30
        lowercase_       :int     =       self.seq_length + self.mem_len
        lowercase_       :Any     =       15
        lowercase_       :Optional[Any]     =       True
        lowercase_       :List[Any]     =       True
        lowercase_       :Any     =       99
        lowercase_       :Optional[int]     =       [10, 50, 80]
        lowercase_       :Union[str, Any]     =       32
        lowercase_       :List[Any]     =       32
        lowercase_       :Tuple     =       4
        lowercase_       :Tuple     =       8
        lowercase_       :List[Any]     =       128
        lowercase_       :Any     =       2
        lowercase_       :Tuple     =       2
        lowercase_       :Dict     =       None
        lowercase_       :Optional[Any]     =       1
        lowercase_       :Optional[int]     =       0
        lowercase_       :List[str]     =       3
        lowercase_       :Optional[int]     =       self.vocab_size - 1
        lowercase_       :List[Any]     =       0.01
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
        lowercase_       :List[str]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       None
        if self.use_labels:
             lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       TransfoXLConfig(
            vocab_size=self.vocab_size   ,							mem_len=self.mem_len   ,							clamp_len=self.clamp_len   ,							cutoffs=self.cutoffs   ,							d_model=self.hidden_size   ,							d_embed=self.d_embed   ,							n_head=self.num_attention_heads   ,							d_head=self.d_head   ,							d_inner=self.d_inner   ,							div_val=self.div_val   ,							n_layer=self.num_hidden_layers   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.vocab_size - 1   ,							init_range=self.init_range   ,							num_labels=self.num_labels   ,							)

        return (config, input_ids_a, input_ids_a, lm_labels)
   def 		UpperCamelCase  (     self		):
        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLModel(UpperCamelCase_		)

        lowercase_       :List[Any]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Dict     =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

        lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel(UpperCamelCase_		)

        lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Optional[int]     =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        lowercase_       :Optional[int]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Tuple     =       model([input_ids_a, mems_a]		).to_tuple()

        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        lowercase_       :Union[str, Any]     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       TFTransfoXLForSequenceClassification(UpperCamelCase_		)
        lowercase_       :int     =       model(UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        (lowercase_)       :Tuple     =       config_and_inputs
        lowercase_       :Dict     =       {'''input_ids''': input_ids_a}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   lowercase					:	Dict									=() if is_tf_available() else ()
   lowercase					:	List[str]									=(
       {
           """feature-extraction""": TFTransfoXLModel,
           """text-classification""": TFTransfoXLForSequenceClassification,
           """text-generation""": TFTransfoXLLMHeadModel,
           """zero-shot""": TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   lowercase					:	Optional[int]									=False
   lowercase					:	Tuple									=False
   lowercase					:	Dict									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

        return False
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       TFTransfoXLModelTester(self		)
        lowercase_       :str     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							d_embed=37		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :List[str]     =       [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             lowercase_       :Dict     =       model_class(UpperCamelCase_		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                  lowercase_       :str     =       model.get_output_embeddings()
                  assert isinstance(UpperCamelCase_   ,							tf.keras.layers.Layer		)
                  lowercase_       :Optional[int]     =       model.get_bias()
                  assert name is None
             else:
                  lowercase_       :List[Any]     =       model.get_output_embeddings()
                  assert x is None
                  lowercase_       :Dict     =       model.get_bias()
                  assert name is None
   def 		UpperCamelCase  (     self		):
        # TODO JP: Make TransfoXL XLA compliant
        pass
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Dict     =       TFTransfoXLModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




   @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''		)
   def 		UpperCamelCase  (     self		):
        pass






@require_tf
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @unittest.skip('''Skip test until #12651 is resolved.'''		)
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        lowercase_       :List[str]     =       tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,							dtype=tf.intaa		)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        lowercase_       :List[Any]     =       [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        lowercase_       :Any     =       model.generate(UpperCamelCase_   ,							max_length=200   ,							do_sample=UpperCamelCase_		)
        self.assertListEqual(output_ids[0].numpy().tolist()   ,							UpperCamelCase_		)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       1
        lowercase_       :Optional[Any]     =       3
        lowercase_       :Optional[int]     =       (32, 32)

        lowercase_       :Dict     =       floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0		)		).to(UpperCamelCase_		)
        return image
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Optional[int]     =       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   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Dict     =       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   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :str     =       RobertaSeriesConfig(
            hidden_size=32   ,							project_dim=32   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=5006   ,							)
        return RobertaSeriesModelWithTransformation(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        def extract(*UpperCamelCase_   ,							**UpperCamelCase_		):






             class      UpperCamelCase					:


                '''simple docstring'''







                def __init__(     self		):
                     lowercase_       :List[str]     =       torch.ones([0]		)




                def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
                     self.pixel_values.to(UpperCamelCase_		)
                     return self
             return Out()

        return extract
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase_       :Dict     =       self.dummy_cond_unet
        lowercase_       :Optional[Any]     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       self.dummy_vae
        lowercase_       :Any     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :List[str]     =       77

        lowercase_       :int     =       self.dummy_image.to(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        lowercase_       :str     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[str]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :int     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Any     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							)

        lowercase_       :Dict     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							)[0]

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

        assert image.shape == (1, 32, 32, 3)
        lowercase_       :int     =       np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3
   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       self.dummy_cond_unet
        lowercase_       :Dict     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Tuple     =       self.dummy_vae
        lowercase_       :Dict     =       self.dummy_text_encoder
        lowercase_       :Tuple     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :str     =       77

        lowercase_       :str     =       self.dummy_image.to(UpperCamelCase_		)

        # put models in fp16
        lowercase_       :Union[str, Any]     =       unet.half()
        lowercase_       :Union[str, Any]     =       vae.half()
        lowercase_       :List[str]     =       bert.half()

        # make sure here that pndm scheduler skips prk
        lowercase_       :List[Any]     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[Any]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :List[str]     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :str     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Union[str, Any]     =       torch.manual_seed(0		)
        lowercase_       :Any     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							).images

        assert image.shape == (1, 32, 32, 3)




   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        # resize to resolution that is divisible by 8 but not 16 or 32
        lowercase_       :Optional[Any]     =       init_image.resize((760, 504)		)

        lowercase_       :List[str]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Optional[Any]     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :Optional[Any]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[Any]     =       torch.manual_seed(0		)
        lowercase_       :str     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :Optional[Any]     =       output.images[0]

        lowercase_       :Optional[int]     =       image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        lowercase_       :Any     =       np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

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






@slow
@require_torch_gpu
class      UpperCamelCase					(		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		):
        lowercase_       :Union[str, Any]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        lowercase_       :Any     =       init_image.resize((768, 512)		)
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

        lowercase_       :List[Any]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Tuple     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :List[str]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[int]     =       torch.manual_seed(0		)
        lowercase_       :Tuple     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :int     =       output.images[0]

        assert image.shape == (512, 768, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image		).max() < 1E-2

