


'''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 			__lowerCamelCase			(	unittest.TestCase	):
				"""simple docstring"""


				def        A   (  self      :							List[Any]):
											_A					:     int     = tempfile.mkdtemp()

											_A					:     int     = [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											_A					:     Any     = 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]))

											_A					:     List[str]     = {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
											    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
											    'do_convert_rgb': True,
											}
											_A					:     List[str]     = os.path.join(self.tmpdirname				,							SCREAMING_SNAKE_CASE)
											with open(self.image_processor_file				,							'w'				,							encoding='utf-8') as fp:
																		json.dump(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


				def        A   (  self      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
											return BertTokenizer.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


				def        A   (  self      :							Union[str, Any]				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
											return BertTokenizerFast.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


				def        A   (  self      :							List[Any]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE)


				def        A   (  self      :							Optional[int]):
											shutil.rmtree(self.tmpdirname)


				def        A   (  self      :							Optional[Any]):
											_A					:     Optional[int]     = [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta)]

											_A					:     Dict     = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				,							0				,							-1)) for x in image_inputs]

											return image_inputs


				def        A   (  self      :							Optional[int]):
											_A					:     int     = self.get_tokenizer()
											_A					:     Optional[int]     = self.get_rust_tokenizer()
											_A					:     Union[str, Any]     = self.get_image_processor()

											_A					:     Dict     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)
											processor_slow.save_pretrained(self.tmpdirname)
											_A					:     str     = ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,							use_fast=SCREAMING_SNAKE_CASE)

											_A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)
											processor_fast.save_pretrained(self.tmpdirname)
											_A					:     int     = 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				,							SCREAMING_SNAKE_CASE)
											self.assertIsInstance(processor_fast.tokenizer				,							SCREAMING_SNAKE_CASE)

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


				def        A   (  self      :							List[str]):
											_A					:     List[Any]     = ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor())
											processor.save_pretrained(self.tmpdirname)

											_A					:     List[str]     = self.get_tokenizer(cls_token='(CLS)'				,							sep_token='(SEP)')
											_A					:     Tuple     = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE)

											_A					:     Union[str, Any]     = ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname				,							cls_token='(CLS)'				,							sep_token='(SEP)'				,							do_normalize=SCREAMING_SNAKE_CASE)

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

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


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

											_A					:     Dict     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

											_A					:     Any     = self.prepare_image_inputs()

											_A					:     Any     = image_processor(SCREAMING_SNAKE_CASE				,							return_tensors='np')
											_A					:     Any     = processor(images=SCREAMING_SNAKE_CASE				,							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      :							Optional[int]):
											_A					:     List[str]     = self.get_image_processor()
											_A					:     str     = self.get_tokenizer()

											_A					:     Any     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

											_A					:     Optional[Any]     = 'Alexandra，T-shirt的价格是15便士。'

											_A					:     Any     = processor(text=SCREAMING_SNAKE_CASE)

											_A					:     str     = tokenizer(SCREAMING_SNAKE_CASE)

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


				def        A   (  self      :							List[str]):
											_A					:     int     = self.get_image_processor()
											_A					:     List[Any]     = self.get_tokenizer()

											_A					:     List[Any]     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

											_A					:     Dict     = 'Alexandra，T-shirt的价格是15便士。'
											_A					:     Optional[Any]     = self.prepare_image_inputs()

											_A					:     Optional[Any]     = processor(text=SCREAMING_SNAKE_CASE				,							images=SCREAMING_SNAKE_CASE)

											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(SCREAMING_SNAKE_CASE):
																		processor()


				def        A   (  self      :							Any):
											_A					:     Optional[int]     = self.get_image_processor()
											_A					:     List[Any]     = self.get_tokenizer()

											_A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

											_A					:     str     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_A					:     Dict     = processor.batch_decode(SCREAMING_SNAKE_CASE)
											_A					:     Optional[Any]     = tokenizer.batch_decode(SCREAMING_SNAKE_CASE)

											self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


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

											_A					:     Tuple     = ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE				,							image_processor=SCREAMING_SNAKE_CASE)

											_A					:     int     = 'Alexandra，T-shirt的价格是15便士。'
											_A					:     int     = self.prepare_image_inputs()

											_A					:     Tuple     = processor(text=SCREAMING_SNAKE_CASE				,							images=SCREAMING_SNAKE_CASE)

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










'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   KandinskyVaaPriorPipeline
    a													=   ["prompt"]
    a													=   ["prompt", "negative_prompt"]
    a													=   [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a													=   False


    @property
    def        A   (  self      :							List[str]):
           return 32


    @property
    def        A   (  self      :							List[Any]):
           return 32


    @property
    def        A   (  self      :							Dict):
           return self.time_input_dim


    @property
    def        A   (  self      :							Tuple):
           return self.time_input_dim * 4


    @property
    def        A   (  self      :							Optional[int]):
           return 100


    @property
    def        A   (  self      :							Dict):
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
           return tokenizer


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Any     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)


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

           _A					:     Optional[Any]     = {
               'num_attention_heads': 2,
               'attention_head_dim': 12,
               'embedding_dim': self.text_embedder_hidden_size,
               'num_layers': 1,
           }

           _A					:     Any     = PriorTransformer(**SCREAMING_SNAKE_CASE)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           _A					:     str     = nn.Parameter(torch.ones(model.clip_std.shape))
           return model


    @property
    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[Any]     = CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size				,							image_size=224				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_channels=3				,							num_hidden_layers=5				,							patch_size=14				,							)

           _A					:     Union[str, Any]     = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
           return model


    @property
    def        A   (  self      :							int):
           _A					:     Optional[Any]     = CLIPImageProcessor(
               crop_size=224				,							do_center_crop=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_resize=SCREAMING_SNAKE_CASE				,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,							resample=3				,							size=224				,							)

           return image_processor


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.dummy_prior
           _A					:     Dict     = self.dummy_image_encoder
           _A					:     Dict     = self.dummy_text_encoder
           _A					:     str     = self.dummy_tokenizer
           _A					:     Optional[Any]     = self.dummy_image_processor

           _A					:     Optional[Any]     = UnCLIPScheduler(
               variance_type='fixed_small_log'				,							prediction_type='sample'				,							num_train_timesteps=1000				,							clip_sample=SCREAMING_SNAKE_CASE				,							clip_sample_range=10.0				,							)

           _A					:     Dict     = {
               'prior': prior,
               'image_encoder': image_encoder,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'scheduler': scheduler,
               'image_processor': image_processor,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'horse',
               'generator': generator,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


    def        A   (  self      :							List[Any]):
           _A					:     str     = 'cpu'

           _A					:     Tuple     = self.get_dummy_components()

           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe.to(SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
           _A					:     str     = output.image_embeds

           _A					:     Optional[int]     = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     Optional[int]     = image[0, -10:]
           _A					:     int     = image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           _A					:     Dict     = np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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


    @skip_mps
    def        A   (  self      :							Any):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[int]     = True
           _A					:     Tuple     = False

           self._test_inference_batch_single_identical(
               test_max_difference=SCREAMING_SNAKE_CASE				,							relax_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[Any]     = False

           self._test_attention_slicing_forward_pass(
               test_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)






