


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers import AutoModelForCausalLM




@require_torch
class    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
						UpperCamelCase         =					AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__snake_case						)
						UpperCamelCase         =					-1

						UpperCamelCase         =					ids_tensor((1, 5)       ,       vocab_size=model.config.vocab_size						).to(__snake_case						)
						UpperCamelCase         =					model.generate(__snake_case       ,       max_new_tokens=1_0       ,       do_sample=__snake_case						)
						UpperCamelCase         =					tokenizer.decode(greedy_ids[0]						)

						with CaptureStdout() as cs:
									UpperCamelCase         =					TextStreamer(__snake_case						)
									model.generate(__snake_case       ,       max_new_tokens=1_0       ,       do_sample=__snake_case       ,       streamer=__snake_case						)
						# The greedy text should be printed to stdout, except for the final "\n" in the streamer
						UpperCamelCase         =					cs.out[:-1]

						self.assertEqual(__snake_case       ,       __snake_case						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
						UpperCamelCase         =					AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__snake_case						)
						UpperCamelCase         =					-1

						UpperCamelCase         =					ids_tensor((1, 5)       ,       vocab_size=model.config.vocab_size						).to(__snake_case						)
						UpperCamelCase         =					model.generate(__snake_case       ,       max_new_tokens=1_0       ,       do_sample=__snake_case						)
						UpperCamelCase         =					tokenizer.decode(greedy_ids[0]						)

						UpperCamelCase         =					TextIteratorStreamer(__snake_case						)
						UpperCamelCase         =					{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
						UpperCamelCase         =					Thread(target=model.generate       ,       kwargs=__snake_case						)
						thread.start()
						UpperCamelCase         =					''''''
						for new_text in streamer:
									streamer_text += new_text

						self.assertEqual(__snake_case       ,       __snake_case						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
						UpperCamelCase         =					AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__snake_case						)
						UpperCamelCase         =					-1

						UpperCamelCase         =					ids_tensor((1, 5)       ,       vocab_size=model.config.vocab_size						).to(__snake_case						)
						UpperCamelCase         =					model.generate(__snake_case       ,       max_new_tokens=1_0       ,       do_sample=__snake_case						)
						UpperCamelCase         =					greedy_ids[:, input_ids.shape[1] :]
						UpperCamelCase         =					tokenizer.decode(new_greedy_ids[0]						)

						with CaptureStdout() as cs:
									UpperCamelCase         =					TextStreamer(__snake_case       ,       skip_prompt=__snake_case						)
									model.generate(__snake_case       ,       max_new_tokens=1_0       ,       do_sample=__snake_case       ,       streamer=__snake_case						)
						# The greedy text should be printed to stdout, except for the final "\n" in the streamer
						UpperCamelCase         =					cs.out[:-1]

						self.assertEqual(__snake_case       ,       __snake_case						)




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					AutoTokenizer.from_pretrained("""distilgpt2"""						)
						UpperCamelCase         =					AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(__snake_case						)
						UpperCamelCase         =					-1

						UpperCamelCase         =					torch.ones((1, 5)       ,       device=__snake_case						).long() * model.config.bos_token_id
						with CaptureStdout() as cs:
									UpperCamelCase         =					TextStreamer(__snake_case       ,       skip_special_tokens=__snake_case						)
									model.generate(__snake_case       ,       max_new_tokens=1       ,       do_sample=__snake_case       ,       streamer=__snake_case						)

						# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
						# re-tokenized, must only contain one token
						UpperCamelCase         =					cs.out[:-1]  # Remove the final "\n"
						UpperCamelCase         =					tokenizer(__snake_case       ,       return_tensors="""pt"""						)
						self.assertEqual(streamer_text_tokenized.input_ids.shape       ,       (1, 1)						)





			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
						UpperCamelCase         =					AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(__snake_case						)
						UpperCamelCase         =					-1

						UpperCamelCase         =					ids_tensor((1, 5)       ,       vocab_size=model.config.vocab_size						).to(__snake_case						)
						UpperCamelCase         =					TextIteratorStreamer(__snake_case       ,       timeout=0.001						)
						UpperCamelCase         =					{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
						UpperCamelCase         =					Thread(target=model.generate       ,       kwargs=__snake_case						)
						thread.start()

						# The streamer will timeout after 0.001 seconds, so an exception will be raised
						with self.assertRaises(__snake_case						):
									UpperCamelCase         =					''''''
									for new_text in streamer:
												streamer_text += new_text








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= KandinskyInpaintPipeline
			lowercase						= ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowercase						= [
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowercase						= [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowercase						= False




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return self.time_input_dim




			@property
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						return self.time_input_dim * 4




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return 1_0_0




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
						return tokenizer




			@property
			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					MCLIPConfig(
						    numDims=self.cross_attention_dim       ,       transformerDimensions=self.text_embedder_hidden_size       ,       hidden_size=self.text_embedder_hidden_size       ,       intermediate_size=3_7       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       vocab_size=1_0_0_5       ,       )

						UpperCamelCase         =					MultilingualCLIP(__magic_name__						)
						UpperCamelCase         =					text_encoder.eval()

						return text_encoder




			@property
			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						torch.manual_seed(0						)

						UpperCamelCase         =					{
						    """in_channels""": 9,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_image""",
						    """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,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						UpperCamelCase         =					UNetaDConditionModel(**__magic_name__						)
						return model




			@property
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					VQModel(**self.dummy_movq_kwargs						)
						return model




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_text_encoder
						UpperCamelCase         =					self.dummy_tokenizer
						UpperCamelCase         =					self.dummy_unet
						UpperCamelCase         =					self.dummy_movq

						UpperCamelCase         =					DDIMScheduler(
						    num_train_timesteps=1_0_0_0       ,       beta_schedule="""linear"""       ,       beta_start=0.00_085       ,       beta_end=0.012       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       steps_offset=1       ,       prediction_type="""epsilon"""       ,       thresholding=__magic_name__       ,       )

						UpperCamelCase         =					{
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]=0						):







						"""simple docstring"""







						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1						)						).to(__magic_name__						)
						# create init_image
						UpperCamelCase         =					floats_tensor((1, 3, 6_4, 6_4)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						)[0]
						UpperCamelCase         =					Image.fromarray(np.uinta(__magic_name__						)						).convert("""RGB"""						).resize((2_5_6, 2_5_6)						)
						# create mask
						UpperCamelCase         =					np.ones((6_4, 6_4)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """prompt""": """horse""",
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs




			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""

						UpperCamelCase         =					self.get_dummy_components()

						UpperCamelCase         =					self.pipeline_class(**__magic_name__						)
						UpperCamelCase         =					pipe.to(__magic_name__						)

						pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					pipe(**self.get_dummy_inputs(__magic_name__						)						)
						UpperCamelCase         =					output.images

						UpperCamelCase         =					pipe(
						    **self.get_dummy_inputs(__magic_name__						)       ,       return_dict=__magic_name__       ,       )[0]

						UpperCamelCase         =					image[0, -3:, -3:, -1]
						UpperCamelCase         =					image_from_tuple[0, -3:, -3:, -1]

						print(F'image.shape {image.shape}'						)

						assert image.shape == (1, 6_4, 6_4, 3)

						UpperCamelCase         =					np.array(
						    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]						)

						assert (
						    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
						), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
						), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						super().test_inference_batch_single_identical(expected_max_diff=3e-3						)




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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""						)

						UpperCamelCase         =					load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
						UpperCamelCase         =					np.ones((7_6_8, 7_6_8)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						UpperCamelCase         =					"""a hat"""

						UpperCamelCase         =					KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""       ,       torch_dtype=torch.floataa						)
						pipe_prior.to(__magic_name__						)

						UpperCamelCase         =					KandinskyInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-inpaint"""       ,       torch_dtype=torch.floataa						)
						UpperCamelCase         =					pipeline.to(__magic_name__						)
						pipeline.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					torch.Generator(device="""cpu"""						).manual_seed(0						)
						UpperCamelCase  ,  UpperCamelCase         =					pipe_prior(
						    __magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=5       ,       negative_prompt=""""""       ,       ).to_tuple()

						UpperCamelCase         =					pipeline(
						    __magic_name__       ,       image=__magic_name__       ,       mask_image=__magic_name__       ,       image_embeds=__magic_name__       ,       negative_image_embeds=__magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=1_0_0       ,       height=7_6_8       ,       width=7_6_8       ,       output_type="""np"""       ,       )

						UpperCamelCase         =					output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(__magic_name__       ,       __magic_name__						)




