




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							torch.FloatTensor




class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase     ):
				"""simple docstring"""



				@register_to_config
				def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  int = 6_55_36      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  int = 2      ,   __lowerCamelCase       :  int = 2      ,   __lowerCamelCase       :  int = 0      ,   __lowerCamelCase       :  str = "fourier"      ,   __lowerCamelCase       :  bool = True      ,   __lowerCamelCase       :  bool = False      ,   __lowerCamelCase       :  float = 0.0      ,   __lowerCamelCase       :  Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,   __lowerCamelCase       :  Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,   __lowerCamelCase       :  Tuple[str] = "UNetMidBlock1D"      ,   __lowerCamelCase       :  str = None      ,   __lowerCamelCase       :  Tuple[int] = (32, 32, 64)      ,   __lowerCamelCase       :  str = None      ,   __lowerCamelCase       :  int = 8      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  bool = False      ,   )      ->  List[str]:
							super().__init__()
							a					=    sample_size

							# time
							if time_embedding_type == "fourier":
										a					=    GaussianFourierProjection(
										    embedding_size=8      ,   set_W_to_weight=__lowerCamelCase      ,   log=__lowerCamelCase      ,   flip_sin_to_cos=__lowerCamelCase							)
										a					=    2 * block_out_channels[0]
							elif time_embedding_type == "positional":
										a					=    Timesteps(
										    block_out_channels[0]      ,   flip_sin_to_cos=__lowerCamelCase      ,   downscale_freq_shift=__lowerCamelCase							)
										a					=    block_out_channels[0]

							if use_timestep_embedding:
										a					=    block_out_channels[0] * 4
										a					=    TimestepEmbedding(
										    in_channels=__lowerCamelCase      ,   time_embed_dim=__lowerCamelCase      ,   act_fn=__lowerCamelCase      ,   out_dim=block_out_channels[0]      ,   )

							a					=    nn.ModuleList([]							)
							a					=    None
							a					=    nn.ModuleList([]							)
							a					=    None

							# down
							a					=    in_channels
							for i, down_block_type in enumerate(__lowerCamelCase							):
										a					=    output_channel
										a					=    block_out_channels[i]

										if i == 0:
													input_channel += extra_in_channels

										a					=    i == len(__lowerCamelCase							) - 1

										a					=    get_down_block(
										    __lowerCamelCase      ,   num_layers=__lowerCamelCase      ,   in_channels=__lowerCamelCase      ,   out_channels=__lowerCamelCase      ,   temb_channels=block_out_channels[0]      ,   add_downsample=not is_final_block or downsample_each_block      ,   )
										self.down_blocks.append(__lowerCamelCase							)

							# mid
							a					=    get_mid_block(
							    __lowerCamelCase      ,   in_channels=block_out_channels[-1]      ,   mid_channels=block_out_channels[-1]      ,   out_channels=block_out_channels[-1]      ,   embed_dim=block_out_channels[0]      ,   num_layers=__lowerCamelCase      ,   add_downsample=__lowerCamelCase      ,   )

							# up
							a					=    list(reversed(__lowerCamelCase							)							)
							a					=    reversed_block_out_channels[0]
							if out_block_type is None:
										a					=    out_channels
							else:
										a					=    block_out_channels[0]

							for i, up_block_type in enumerate(__lowerCamelCase							):
										a					=    output_channel
										a					=    (
										    reversed_block_out_channels[i + 1] if i < len(__lowerCamelCase							) - 1 else final_upsample_channels
										)

										a					=    i == len(__lowerCamelCase							) - 1

										a					=    get_up_block(
										    __lowerCamelCase      ,   num_layers=__lowerCamelCase      ,   in_channels=__lowerCamelCase      ,   out_channels=__lowerCamelCase      ,   temb_channels=block_out_channels[0]      ,   add_upsample=not is_final_block      ,   )
										self.up_blocks.append(__lowerCamelCase							)
										a					=    output_channel

							# out
							a					=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,   32							)
							a					=    get_out_block(
							    out_block_type=__lowerCamelCase      ,   num_groups_out=__lowerCamelCase      ,   embed_dim=block_out_channels[0]      ,   out_channels=__lowerCamelCase      ,   act_fn=__lowerCamelCase      ,   fc_dim=block_out_channels[-1] // 4      ,   )





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  torch.FloatTensor      ,   __lowerCamelCase       :  Union[torch.Tensor, float, int]      ,   __lowerCamelCase       :  bool = True      ,   )      ->  Union[UNetaDOutput, Tuple]:
							a					=    timestep
							if not torch.is_tensor(__lowerCamelCase							):
										a					=    torch.tensor([timesteps]      ,   dtype=torch.long      ,   device=sample.device							)
							elif torch.is_tensor(__lowerCamelCase							) and len(timesteps.shape							) == 0:
										a					=    timesteps[None].to(sample.device							)

							a					=    self.time_proj(__lowerCamelCase							)
							if self.config.use_timestep_embedding:
										a					=    self.time_mlp(__lowerCamelCase							)
							else:
										a					=    timestep_embed[..., None]
										a					=    timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
										a					=    timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

							# 2. down
							a					=    ()
							for downsample_block in self.down_blocks:
										a					,	a					=    downsample_block(hidden_states=__lowerCamelCase      ,   temb=__lowerCamelCase							)
										down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
										a					=    self.mid_block(__lowerCamelCase      ,   __lowerCamelCase							)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks							):
										a					=    down_block_res_samples[-1:]
										a					=    down_block_res_samples[:-1]
										a					=    upsample_block(__lowerCamelCase      ,   res_hidden_states_tuple=__lowerCamelCase      ,   temb=__lowerCamelCase							)

							# 5. post-process
							if self.out_block:
										a					=    self.out_block(__lowerCamelCase      ,   __lowerCamelCase							)

							if not return_dict:
										return (sample,)

							return UNetaDOutput(sample=__lowerCamelCase							)












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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict:
							a					=    tempfile.mkdtemp()

							a					=    [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "的",
							    "价",
							    "格",
							    "是",
							    "15",
							    "便",
							    "alex",
							    "##andra",
							    "，",
							    "。",
							    "-",
							    "t",
							    "shirt",
							]
							a					=    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					=    {
							    "do_resize": True,
							    "size": {"height": 2_24, "width": 2_24},
							    "do_center_crop": True,
							    "crop_size": {"height": 18, "width": 18},
							    "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,
							}
							a					=    os.path.join(self.tmpdirname      ,   __lowerCamelCase							)
							with open(self.image_processor_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										json.dump(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:
							return BertTokenizer.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str      ,   **__lowerCamelCase       :  Optional[int]							)      ->  str:
							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Tuple:
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





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





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    [np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta							)]

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

							return image_inputs





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.get_tokenizer()
							a					=    self.get_rust_tokenizer()
							a					=    self.get_image_processor()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_slow.save_pretrained(self.tmpdirname							)
							a					=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_fast.save_pretrained(self.tmpdirname							)
							a					=    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      ,   __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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							a					=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a					=    self.get_tokenizer(cls_token="(CLS)"      ,   sep_token="(SEP)"							)
							a					=    self.get_image_processor(do_normalize=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname      ,   cls_token="(CLS)"      ,   sep_token="(SEP)"      ,   do_normalize=__lowerCamelCase							)

							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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    self.prepare_image_inputs()

							a					=    image_processor(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    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 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase							)

							a					=    tokenizer(__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

							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(__lowerCamelCase							):
										processor()





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor.batch_decode(__lowerCamelCase							)
							a					=    tokenizer.batch_decode(__lowerCamelCase							)

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

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






