'''simple docstring'''




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       UpperCAmelCase__  (       __SCREAMING_SNAKE_CASE			):
   A							:       str     =    42



class       UpperCAmelCase__  (       __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE			):
   @register_to_config
   def __init__(self       ,				_a = 65_536       ,				_a = None       ,				_a = 2       ,				_a = 2       ,				_a = 0       ,				_a = "fourier"       ,				_a = True       ,				_a = False       ,				_a = 0.0       ,				_a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,				_a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,				_a = "UNetMidBlock1D"       ,				_a = None       ,				_a = (32, 32, 64)       ,				_a = None       ,				_a = 8       ,				_a = 1       ,				_a = False       ,				)    ->     int:
        super().__init__()
        lowercase_					:       int     				=					sample_size

        # time
        if time_embedding_type == "fourier":
             lowercase_					:       str     				=					GaussianFourierProjection(
                 embedding_size=8       ,				set_W_to_weight=_a       ,				log=_a       ,				flip_sin_to_cos=_a				)
             lowercase_					:       Tuple     				=					2 * block_out_channels[0]
        elif time_embedding_type == "positional":
             lowercase_					:       List[Any]     				=					Timesteps(
                 block_out_channels[0]       ,				flip_sin_to_cos=_a       ,				downscale_freq_shift=_a				)
             lowercase_					:       Optional[int]     				=					block_out_channels[0]

        if use_timestep_embedding:
             lowercase_					:       str     				=					block_out_channels[0] * 4
             lowercase_					:       List[str]     				=					TimestepEmbedding(
                 in_channels=_a       ,				time_embed_dim=_a       ,				act_fn=_a       ,				out_dim=block_out_channels[0]       ,				)

        lowercase_					:       List[Any]     				=					nn.ModuleList([]				)
        lowercase_					:       int     				=					None
        lowercase_					:       List[str]     				=					nn.ModuleList([]				)
        lowercase_					:       Optional[Any]     				=					None

        # down
        lowercase_					:       Dict     				=					in_channels
        for i, down_block_type in enumerate(_a				):
             lowercase_					:       Optional[int]     				=					output_channel
             lowercase_					:       Dict     				=					block_out_channels[i]

             if i == 0:
                  input_channel += extra_in_channels

             lowercase_					:       str     				=					i == len(_a				) - 1

             lowercase_					:       List[str]     				=					get_down_block(
                 _a       ,				num_layers=_a       ,				in_channels=_a       ,				out_channels=_a       ,				temb_channels=block_out_channels[0]       ,				add_downsample=not is_final_block or downsample_each_block       ,				)
             self.down_blocks.append(_a				)

        # mid
        lowercase_					:       List[Any]     				=					get_mid_block(
            _a       ,				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=_a       ,				add_downsample=_a       ,				)

        # up
        lowercase_					:       Tuple     				=					list(reversed(_a				)				)
        lowercase_					:       List[Any]     				=					reversed_block_out_channels[0]
        if out_block_type is None:
             lowercase_					:       Union[str, Any]     				=					out_channels
        else:
             lowercase_					:       Tuple     				=					block_out_channels[0]

        for i, up_block_type in enumerate(_a				):
             lowercase_					:       str     				=					output_channel
             lowercase_					:       Any     				=					(
                 reversed_block_out_channels[i + 1] if i < len(_a				) - 1 else final_upsample_channels
             )

             lowercase_					:       Any     				=					i == len(_a				) - 1

             lowercase_					:       Union[str, Any]     				=					get_up_block(
                 _a       ,				num_layers=_a       ,				in_channels=_a       ,				out_channels=_a       ,				temb_channels=block_out_channels[0]       ,				add_upsample=not is_final_block       ,				)
             self.up_blocks.append(_a				)
             lowercase_					:       Any     				=					output_channel

        # out
        lowercase_					:       str     				=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,				32				)
        lowercase_					:       Optional[Any]     				=					get_out_block(
            out_block_type=_a       ,				num_groups_out=_a       ,				embed_dim=block_out_channels[0]       ,				out_channels=_a       ,				act_fn=_a       ,				fc_dim=block_out_channels[-1] // 4       ,				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a = True       ,				)    ->     Optional[int]:
        lowercase_					:       Tuple     				=					timestep
        if not torch.is_tensor(_a				):
             lowercase_					:       Any     				=					torch.tensor([timesteps]       ,				dtype=torch.long       ,				device=sample.device				)
        elif torch.is_tensor(_a				) and len(timesteps.shape				) == 0:
             lowercase_					:       Tuple     				=					timesteps[None].to(sample.device				)

        lowercase_					:       Dict     				=					self.time_proj(_a				)
        if self.config.use_timestep_embedding:
             lowercase_					:       int     				=					self.time_mlp(_a				)
        else:
             lowercase_					:       int     				=					timestep_embed[..., None]
             lowercase_					:       Tuple     				=					timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
             lowercase_					:       Dict     				=					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

        # 2. down
        lowercase_					:       List[str]     				=					()
        for downsample_block in self.down_blocks:
             lowercase_					:       Tuple     				=					downsample_block(hidden_states=_a       ,				temb=_a				)
             down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
             lowercase_					:       int     				=					self.mid_block(_a       ,				_a				)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks				):
             lowercase_					:       List[str]     				=					down_block_res_samples[-1:]
             lowercase_					:       List[Any]     				=					down_block_res_samples[:-1]
             lowercase_					:       Any     				=					upsample_block(_a       ,				res_hidden_states_tuple=_a       ,				temb=_a				)

        # 5. post-process
        if self.out_block:
             lowercase_					:       Union[str, Any]     				=					self.out_block(_a       ,				_a				)

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=_a				)





'''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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, 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]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            '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],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.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       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        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       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

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

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				return_tensors='np'				)

        for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()       ,				input_processor[key].sum()       ,				delta=1e-2				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        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(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

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




