


"""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 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       int     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       tempfile.mkdtemp()

						__SCREAMING_SNAKE_CASE		     =       [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						__SCREAMING_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]     )     )

						__SCREAMING_SNAKE_CASE		     =       {
						    "do_resize": True,
						    "size": {"height": 2_2_4, "width": 2_2_4},
						    "do_center_crop": True,
						    "crop_size": {"height": 1_8, "width": 1_8},
						    "do_normalize": True,
						    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
						    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
						    "do_convert_rgb": True,
						}
						__SCREAMING_SNAKE_CASE		     =       os.path.join(self.tmpdirname    ,    UpperCAmelCase__     )
						with open(self.image_processor_file    ,    "w"    ,    encoding="utf-8"     ) as fp:
											json.dump(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    **UpperCAmelCase__    :       Optional[int]     ) ->			Optional[Any]:
						return BertTokenizer.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    **UpperCAmelCase__    :       Tuple     ) ->			Dict:
						return BertTokenizerFast.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    **UpperCAmelCase__    :       List[Any]     ) ->			Optional[Any]:
						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Dict:
						shutil.rmtree(self.tmpdirname     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       [np.random.randint(2_5_5    ,    size=(3, 3_0, 4_0_0)    ,    dtype=np.uinta     )]

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

						return image_inputs




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizer()
						__SCREAMING_SNAKE_CASE		     =       self.get_rust_tokenizer()
						__SCREAMING_SNAKE_CASE		     =       self.get_image_processor()

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )
						processor_slow.save_pretrained(self.tmpdirname     )
						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,    use_fast=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )
						processor_fast.save_pretrained(self.tmpdirname     )
						__SCREAMING_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    ,    UpperCAmelCase__     )
						self.assertIsInstance(processor_fast.tokenizer    ,    UpperCAmelCase__     )

						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    ,    UpperCAmelCase__     )
						self.assertIsInstance(processor_fast.image_processor    ,    UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()     )
						processor.save_pretrained(self.tmpdirname     )

						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizer(cls_token="(CLS)"    ,    sep_token="(SEP)"     )
						__SCREAMING_SNAKE_CASE		     =       self.get_image_processor(do_normalize=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname    ,    cls_token="(CLS)"    ,    sep_token="(SEP)"    ,    do_normalize=UpperCAmelCase__     )

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

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




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.get_image_processor()
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizer()

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE		     =       image_processor(UpperCAmelCase__    ,    return_tensors="np"     )
						__SCREAMING_SNAKE_CASE		     =       processor(images=UpperCAmelCase__    ,    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 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.get_image_processor()
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizer()

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       "Alexandra，T-shirt的价格是15便士。"

						__SCREAMING_SNAKE_CASE		     =       processor(text=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__     )

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]     )




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

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       "Alexandra，T-shirt的价格是15便士。"
						__SCREAMING_SNAKE_CASE		     =       self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE		     =       processor(text=UpperCAmelCase__    ,    images=UpperCAmelCase__     )

						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(UpperCAmelCase__     ):
											processor()




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

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )

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

						__SCREAMING_SNAKE_CASE		     =       processor.batch_decode(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       tokenizer.batch_decode(UpperCAmelCase__     )

						self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       self.get_image_processor()
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizer()

						__SCREAMING_SNAKE_CASE		     =       ChineseCLIPProcessor(tokenizer=UpperCAmelCase__    ,    image_processor=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       "Alexandra，T-shirt的价格是15便士。"
						__SCREAMING_SNAKE_CASE		     =       self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE		     =       processor(text=UpperCAmelCase__    ,    images=UpperCAmelCase__     )

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




"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    DiTPipeline
	snake_case__       :						Union[str, Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case__       :						Any					      =    PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__       :						Optional[Any]					      =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case__       :						Optional[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       TransformeraDModel(
						    sample_size=1_6    ,    num_layers=2    ,    patch_size=4    ,    attention_head_dim=8    ,    num_attention_heads=2    ,    in_channels=4    ,    out_channels=8    ,    attention_bias=UpperCAmelCase__    ,    activation_fn="gelu-approximate"    ,    num_embeds_ada_norm=1_0_0_0    ,    norm_type="ada_norm_zero"    ,    norm_elementwise_affine=UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       AutoencoderKL()
						__SCREAMING_SNAKE_CASE		     =       DDIMScheduler()
						__SCREAMING_SNAKE_CASE		     =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
						return components




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       str=0     ) ->			Optional[int]:
						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {
						    "class_labels": [1],
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       "cpu"

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()
						__SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_inputs(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       pipe(**UpperCAmelCase__     ).images
						__SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]

						self.assertEqual(image.shape    ,    (1, 1_6, 1_6, 3)     )
						__SCREAMING_SNAKE_CASE		     =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]     )
						__SCREAMING_SNAKE_CASE		     =       np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(UpperCAmelCase__    ,    1E-3     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase__    ,    expected_max_diff=1E-3     )




	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()    ,    reason="XFormers attention is only available with CUDA and `xformers` installed"    ,    )
	def 						UpperCAmelCase_   (				self    :       int     ) ->			Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )




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

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella", "white shark", "white wolf"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=4_0    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""     )
											assert np.abs((expected_image - image).max()     ) < 1E-2




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"     )
						__SCREAMING_SNAKE_CASE		     =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
						pipe.to("cuda"     )

						__SCREAMING_SNAKE_CASE		     =       ["vase", "umbrella"]
						__SCREAMING_SNAKE_CASE		     =       pipe.get_label_ids(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       pipe(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=2_5    ,    output_type="np"     ).images

						for word, image in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    F"""/dit/{word}_512.npy"""     )

											assert np.abs((expected_image - image).max()     ) < 1E-1
