

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  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: torch.FloatTensor







class  lowercase				(       lowercase_	,  lowercase_			):




				@register_to_config
				def __init__(					self      ,   snake_case = 6_5536      ,   snake_case = None      ,   snake_case = 2      ,   snake_case = 2      ,   snake_case = 0      ,   snake_case = "fourier"      ,   snake_case = True      ,   snake_case = False      ,   snake_case = 0.0      ,   snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,   snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,   snake_case = "UNetMidBlock1D"      ,   snake_case = None      ,   snake_case = (32, 32, 64)      ,   snake_case = None      ,   snake_case = 8      ,   snake_case = 1      ,   snake_case = False      ,   ):
											super().__init__()
											snake_case_							=    sample_size

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

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

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

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

																		if i == 0:
																									input_channel += extra_in_channels

																		snake_case_							=    i == len(snake_case       ) - 1

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

											# mid
											snake_case_							=    get_mid_block(
											    snake_case      ,   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=snake_case      ,   add_downsample=snake_case      ,   )

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

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

																		snake_case_							=    i == len(snake_case       ) - 1

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

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




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case = True      ,   ):
											snake_case_							=    timestep
											if not torch.is_tensor(snake_case       ):
																		snake_case_							=    torch.tensor([timesteps]      ,   dtype=torch.long      ,   device=sample.device       )
											elif torch.is_tensor(snake_case       ) and len(timesteps.shape       ) == 0:
																		snake_case_							=    timesteps[None].to(sample.device       )

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

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

											# 3. mid
											if self.mid_block:
																		snake_case_							=    self.mid_block(snake_case      ,   snake_case       )

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

											# 5. post-process
											if self.out_block:
																		snake_case_							=    self.out_block(snake_case      ,   snake_case       )

											if not return_dict:
																		return (sample,)

											return UNetaDOutput(sample=snake_case       )







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  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    tempfile.mkdtemp()

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

											snake_case_							=    {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 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],
											    'do_convert_rgb': True,
											}
											snake_case_							=    os.path.join(self.tmpdirname      ,   snake_case       )
											with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'       ) as fp:
																		json.dump(snake_case      ,   snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizer.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta       )]

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

											return image_inputs




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    self.get_image_processor()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=snake_case       )

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											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      ,   snake_case       )
											self.assertIsInstance(processor_fast.tokenizer      ,   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      ,   snake_case       )
											self.assertIsInstance(processor_fast.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(cls_token='(CLS)'      ,   sep_token='(SEP)'       )
											snake_case_							=    self.get_image_processor(do_normalize=snake_case       )

											snake_case_							=    ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname      ,   cls_token='(CLS)'      ,   sep_token='(SEP)'      ,   do_normalize=snake_case       )

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

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




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    image_processor(snake_case      ,   return_tensors='np'       )
											snake_case_							=    processor(images=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       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'

											snake_case_							=    processor(text=snake_case       )

											snake_case_							=    tokenizer(snake_case       )

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




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

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

											snake_case_							=    processor(text=snake_case      ,   images=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(snake_case       ):
																		processor()




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

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

											snake_case_							=    processor.batch_decode(snake_case       )
											snake_case_							=    tokenizer.batch_decode(snake_case       )

											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

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

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

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




