




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    tempfile.mkdtemp()

											# fmt: off
											SCREAMING_SNAKE_CASE_   	=    ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
											# fmt: on
											SCREAMING_SNAKE_CASE_   	=    dict(zip(_lowerCamelCase		,					range(len(_lowerCamelCase		)		)		)		)
											SCREAMING_SNAKE_CASE_   	=    ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
											SCREAMING_SNAKE_CASE_   	=    {'''unk_token''': '''<unk>'''}

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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''do_resize''': True,
											    '''size''': 20,
											    '''do_center_crop''': True,
											    '''crop_size''': 18,
											    '''do_normalize''': True,
											    '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											}
											SCREAMING_SNAKE_CASE_   	=    os.path.join(self.tmpdirname		,					_lowerCamelCase		)
											with open(self.image_processor_file		,					'''w'''		,					encoding='''utf-8'''		) as fp:
																		json.dump(_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					**_lowerCamelCase:   str		):
											return CLIPTokenizer.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   Dict		,					**_lowerCamelCase:   int		):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					**_lowerCamelCase:   Optional[Any]		):
											return ViTImageProcessor.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   int		):
											shutil.rmtree(self.tmpdirname		)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    [np.random.randint(2_55		,					size=(3, 30, 4_00)		,					dtype=np.uinta		)]

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

											return image_inputs


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()
											SCREAMING_SNAKE_CASE_   	=    self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)
											processor_slow.save_pretrained(self.tmpdirname		)
											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor.from_pretrained(self.tmpdirname		,					use_fast=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)
											processor_fast.save_pretrained(self.tmpdirname		)
											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor.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		,					_lowerCamelCase		)
											self.assertIsInstance(processor_fast.tokenizer		,					_lowerCamelCase		)

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


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=self.get_tokenizer()		,					image_processor=self.get_image_processor()		)
											processor.save_pretrained(self.tmpdirname		)

											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer(bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		)
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor(do_normalize=_lowerCamelCase		,					padding_value=1.0		)

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor.from_pretrained(
											    self.tmpdirname		,					bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		,					do_normalize=_lowerCamelCase		,					padding_value=1.0		)

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

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


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_   	=    image_processor(_lowerCamelCase		,					return_tensors='''np'''		)
											SCREAMING_SNAKE_CASE_   	=    processor(images=_lowerCamelCase		,					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     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    '''lower newer'''

											SCREAMING_SNAKE_CASE_   	=    processor(text=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    tokenizer(_lowerCamelCase		)

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


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    '''lower newer'''
											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_   	=    processor(text=_lowerCamelCase		,					images=_lowerCamelCase		)

											self.assertListEqual(list(inputs.keys()		)		,					['''input_ids''', '''attention_mask''', '''pixel_values''']		)

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


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()
											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_   	=    processor(images=_lowerCamelCase		,					visual_prompt=_lowerCamelCase		)

											self.assertListEqual(list(inputs.keys()		)		,					['''pixel_values''', '''conditional_pixel_values''']		)

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





				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

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

											SCREAMING_SNAKE_CASE_   	=    processor.batch_decode(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    tokenizer.batch_decode(_lowerCamelCase		)

											self.assertListEqual(_lowerCamelCase		,					_lowerCamelCase		)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 16,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 32,
											    '''embedding_proj_dim''': self.text_embedder_hidden_size,
											    '''time_embed_dim''': self.time_embed_dim,
											    '''num_layers''': 1,
											    '''clip_embed_dim''': self.time_input_dim * 2,
											    '''additional_embeddings''': 0,
											    '''time_embed_act_fn''': '''gelu''',
											    '''norm_in_type''': '''layer''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''param_shapes''': (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    '''d_latent''': self.time_input_dim,
											    '''d_hidden''': self.renderer_dim,
											    '''n_output''': 12,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


				def     _A							(							self:   str		):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

											for key in inputs.keys():
																		if key in self.batch_params:
																									SCREAMING_SNAKE_CASE_   	=    batch_size * [inputs[key]]

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Any		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


