





'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class       UpperCAmelCase (						_lowercase			,       _lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		StableUnCLIPPipeline
		UpperCAmelCase					:	Optional[Any] 			=		TEXT_TO_IMAGE_PARAMS
		UpperCAmelCase					:	List[Any] 			=		TEXT_TO_IMAGE_BATCH_PARAMS
		UpperCAmelCase					:	Union[str, Any] 			=		TEXT_TO_IMAGE_IMAGE_PARAMS
		UpperCAmelCase					:	Any 			=		TEXT_TO_IMAGE_IMAGE_PARAMS

		# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
		UpperCAmelCase					:	Optional[int] 			=		False




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						lowercase         = 3_2
						lowercase         = embedder_hidden_size

						# prior components

						torch.manual_seed(0				)
						lowercase         = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

						torch.manual_seed(0				)
						lowercase         = CLIPTextModelWithProjection(
						    CLIPTextConfig(
						        bos_token_id=0							,			eos_token_id=2							,			hidden_size=UpperCamelCase_							,			projection_dim=UpperCamelCase_							,			intermediate_size=3_7							,			layer_norm_eps=1e-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1_0_0_0							,			)				)

						torch.manual_seed(0				)
						lowercase         = PriorTransformer(
						    num_attention_heads=2							,			attention_head_dim=1_2							,			embedding_dim=UpperCamelCase_							,			num_layers=1							,			)

						torch.manual_seed(0				)
						lowercase         = DDPMScheduler(
						    variance_type="fixed_small_log"							,			prediction_type="sample"							,			num_train_timesteps=1_0_0_0							,			clip_sample=UpperCamelCase_							,			clip_sample_range=5.0							,			beta_schedule="squaredcos_cap_v2"							,			)

						# regular denoising components

						torch.manual_seed(0				)
						lowercase         = StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase_				)
						lowercase         = DDPMScheduler(beta_schedule="squaredcos_cap_v2"				)

						torch.manual_seed(0				)
						lowercase         = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

						torch.manual_seed(0				)
						lowercase         = CLIPTextModel(
						    CLIPTextConfig(
						        bos_token_id=0							,			eos_token_id=2							,			hidden_size=UpperCamelCase_							,			projection_dim=3_2							,			intermediate_size=3_7							,			layer_norm_eps=1e-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1_0_0_0							,			)				)

						torch.manual_seed(0				)
						lowercase         = UNetaDConditionModel(
						    sample_size=3_2							,			in_channels=4							,			out_channels=4							,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")							,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")							,			block_out_channels=(3_2, 6_4)							,			attention_head_dim=(2, 4)							,			class_embed_type="projection"							,			projection_class_embeddings_input_dim=embedder_projection_dim * 2							,			cross_attention_dim=UpperCamelCase_							,			layers_per_block=1							,			upcast_attention=UpperCamelCase_							,			use_linear_projection=UpperCamelCase_							,			)

						torch.manual_seed(0				)
						lowercase         = DDIMScheduler(
						    beta_schedule="scaled_linear"							,			beta_start=0.0_0_0_8_5							,			beta_end=0.0_1_2							,			prediction_type="v_prediction"							,			set_alpha_to_one=UpperCamelCase_							,			steps_offset=1							,			)

						torch.manual_seed(0				)
						lowercase         = AutoencoderKL()

						lowercase         = {
						    # prior components
						    "prior_tokenizer": prior_tokenizer,
						    "prior_text_encoder": prior_text_encoder,
						    "prior": prior,
						    "prior_scheduler": prior_scheduler,
						    # image noising components
						    "image_normalizer": image_normalizer,
						    "image_noising_scheduler": image_noising_scheduler,
						    # regular denoising components
						    "tokenizer": tokenizer,
						    "text_encoder": text_encoder,
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						}

						return components




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	List[str]							,			A__      :	Any=0				)					-> List[str]:
						if str(UpperCamelCase_				).startswith("mps"				):
										lowercase         = torch.manual_seed(UpperCamelCase_				)
						else:
										lowercase         = torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
						lowercase         = {
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "prior_num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Dict:
						lowercase         = torch_device == "cpu"

						self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_				)





		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = torch_device in ["cpu", "mps"]

						self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase_				)




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




		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Dict:
						lowercase         = load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"				)

						lowercase         = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"							,			torch_dtype=torch.floataa				)
						pipe.to(UpperCamelCase_				)
						pipe.set_progress_bar_config(disable=UpperCamelCase_				)
						# stable unclip will oom when integration tests are run on a V100,
						# so turn on memory savings
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						lowercase         = torch.Generator(device="cpu"				).manual_seed(0				)
						lowercase         = pipe("anime turle"							,			generator=UpperCamelCase_							,			output_type="np"				)

						lowercase         = output.images[0]

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

						assert_mean_pixel_difference(UpperCamelCase_							,			UpperCamelCase_				)





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						lowercase         = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"							,			torch_dtype=torch.floataa				)
						lowercase         = pipe.to(UpperCamelCase_				)
						pipe.set_progress_bar_config(disable=UpperCamelCase_				)
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						lowercase         = pipe(
						    "anime turtle"							,			prior_num_inference_steps=2							,			num_inference_steps=2							,			output_type="np"							,			)

						lowercase         = torch.cuda.max_memory_allocated()
						# make sure that less than 7 GB is allocated
						assert mem_bytes < 7 * 1_0**9











'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
			import torch


if is_vision_available():
			from PIL import Image

			from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		ViTImageProcessor if is_vision_available() else None




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = (3, 3_2, 1_2_8)
						lowercase         = tempfile.mkdtemp()

						# fmt: off
						lowercase         = ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
						# fmt: on
						lowercase         = dict(zip(A__							,			range(len(A__				)				)				)				)

						lowercase         = os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]				)
						with open(self.vocab_file							,			"w"							,			encoding="utf-8"				) as fp:
										fp.write(json.dumps(A__				) + "\n"				)

						lowercase         = {
						    "do_normalize": False,
						    "do_resize": True,
						    "image_processor_type": "ViTImageProcessor",
						    "resample": 3,
						    "size": {"height": 3_2, "width": 1_2_8},
						}
						lowercase         = os.path.join(self.tmpdirname							,			A__				)
						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"				) as fp:
										json.dump(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Dict							,			**A__      :	List[Any]				)					-> Union[str, Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Tuple				)					-> Tuple:
						return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						shutil.rmtree(self.tmpdirname				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta				)

						lowercase         = Image.fromarray(np.moveaxis(A__							,			0							,			-1				)				)

						return image_input




		def 				UpperCAmelCase__   (self      :	str				)					-> List[Any]:
						lowercase         = self.get_tokenizer()
						lowercase         = self.get_image_processor()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)
						lowercase         = MgpstrProcessor.from_pretrained(self.tmpdirname							,			use_fast=A__				)

						self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer.get_vocab()				)
						self.assertIsInstance(processor.char_tokenizer							,			A__				)

						self.assertEqual(processor.image_processor.to_json_string()							,			image_processor.to_json_string()				)
						self.assertIsInstance(processor.image_processor							,			A__				)




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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)
						processor.save_pretrained(self.tmpdirname				)

						lowercase         = self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"				)
						lowercase         = self.get_image_processor(do_normalize=A__							,			padding_value=1.0				)

						lowercase         = MgpstrProcessor.from_pretrained(
						    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=A__							,			padding_value=1.0				)

						self.assertEqual(processor.char_tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab()				)
						self.assertIsInstance(processor.char_tokenizer							,			A__				)

						self.assertEqual(processor.image_processor.to_json_string()							,			image_processor_add_kwargs.to_json_string()				)
						self.assertIsInstance(processor.image_processor							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = self.prepare_image_inputs()

						lowercase         = image_processor(A__							,			return_tensors="np"				)
						lowercase         = processor(images=A__							,			return_tensors="np"				)

						for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()							,			input_processor[key].sum()							,			delta=1e-2				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"

						lowercase         = processor(text=A__				)

						lowercase         = tokenizer(A__				)
						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]				)




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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = "test"
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

						self.assertListEqual(list(inputs.keys()				)							,			["pixel_values", "labels"]				)

						# test if it raises when no input is passed
						with pytest.raises(A__				):
										processor()




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.get_image_processor()
						lowercase         = self.get_tokenizer()

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

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

						lowercase         = processor.char_decode(A__				)
						lowercase         = tokenizer.batch_decode(A__				)
						lowercase         = [seq.replace(" "							,			""				) for seq in decoded_tok]

						self.assertListEqual(A__							,			A__				)




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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = None
						lowercase         = self.prepare_image_inputs()

						lowercase         = processor(text=A__							,			images=A__				)

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





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

						lowercase         = MgpstrProcessor(tokenizer=A__							,			image_processor=A__				)

						lowercase         = torch.randn(1							,			2_7							,			3_8				)
						lowercase         = torch.randn(1							,			2_7							,			5_0_2_5_7				)
						lowercase         = torch.randn(1							,			2_7							,			3_0_5_2_2				)

						lowercase         = processor.batch_decode([char_input, bpe_input, wp_input]				)

						self.assertListEqual(list(results.keys()				)							,			["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]				)




