

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 UNetMidBlockaD, get_down_block, get_up_block



@dataclass
class        _UpperCAmelCase (   _SCREAMING_SNAKE_CASE			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				42



class        _UpperCAmelCase (   _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE			):
  '''simple docstring'''







  @register_to_config
  def __init__( self	: int		,						UpperCamelCase__	: int = None		,						UpperCamelCase__	: Optional[int] = 3		,						UpperCamelCase__	: Optional[int] = 3		,						UpperCamelCase__	: str = False		,						UpperCamelCase__	: List[Any] = "positional"		,						UpperCamelCase__	: List[Any] = 0		,						UpperCamelCase__	: Dict = True		,						UpperCamelCase__	: List[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,						UpperCamelCase__	: Dict = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,						UpperCamelCase__	: Optional[Any] = (224, 448, 672, 896)		,						UpperCamelCase__	: Optional[Any] = 2		,						UpperCamelCase__	: List[str] = 1		,						UpperCamelCase__	: Any = 1		,						UpperCamelCase__	: List[str] = "conv"		,						UpperCamelCase__	: str = "conv"		,						UpperCamelCase__	: Union[str, Any] = "silu"		,						UpperCamelCase__	: Optional[int] = 8		,						UpperCamelCase__	: Optional[int] = 32		,						UpperCamelCase__	: List[Any] = 1e-5		,						UpperCamelCase__	: str = "default"		,						UpperCamelCase__	: Any = True		,						UpperCamelCase__	: Tuple = None		,						UpperCamelCase__	: Any = None		,						):
        super().__init__()

        A    =				sample_size
        A    =				block_out_channels[0] * 4

        # Check inputs
        if len(__A ) != len(__A ):
              raise ValueError(
                  f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.''' )

        if len(__A ) != len(__A ):
              raise ValueError(
                  f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.''' )

        # input
        A    =				nn.Convad(__A		,						block_out_channels[0]		,						kernel_size=3		,						padding=(1, 1) )

        # time
        if time_embedding_type == "fourier":
              A    =				GaussianFourierProjection(embedding_size=block_out_channels[0]		,						scale=16 )
              A    =				2 * block_out_channels[0]
        elif time_embedding_type == "positional":
              A    =				Timesteps(block_out_channels[0]		,						__A		,						__A )
              A    =				block_out_channels[0]

        A    =				TimestepEmbedding(__A		,						__A )

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
              A    =				nn.Embedding(__A		,						__A )
        elif class_embed_type == "timestep":
              A    =				TimestepEmbedding(__A		,						__A )
        elif class_embed_type == "identity":
              A    =				nn.Identity(__A		,						__A )
        else:
              A    =				None

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

        # down
        A    =				block_out_channels[0]
        for i, down_block_type in enumerate(__A ):
              A    =				output_channel
              A    =				block_out_channels[i]
              A    =				i == len(__A ) - 1

              A    =				get_down_block(
                  __A		,						num_layers=__A		,						in_channels=__A		,						out_channels=__A		,						temb_channels=__A		,						add_downsample=not is_final_block		,						resnet_eps=__A		,						resnet_act_fn=__A		,						resnet_groups=__A		,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,						downsample_padding=__A		,						resnet_time_scale_shift=__A		,						downsample_type=__A		,						)
              self.down_blocks.append(__A )

        # mid
        A    =				UNetMidBlockaD(
            in_channels=block_out_channels[-1]		,						temb_channels=__A		,						resnet_eps=__A		,						resnet_act_fn=__A		,						output_scale_factor=__A		,						resnet_time_scale_shift=__A		,						attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,						resnet_groups=__A		,						add_attention=__A		,						)

        # up
        A    =				list(reversed(__A ) )
        A    =				reversed_block_out_channels[0]
        for i, up_block_type in enumerate(__A ):
              A    =				output_channel
              A    =				reversed_block_out_channels[i]
              A    =				reversed_block_out_channels[min(i + 1		,						len(__A ) - 1 )]

              A    =				i == len(__A ) - 1

              A    =				get_up_block(
                  __A		,						num_layers=layers_per_block + 1		,						in_channels=__A		,						out_channels=__A		,						prev_output_channel=__A		,						temb_channels=__A		,						add_upsample=not is_final_block		,						resnet_eps=__A		,						resnet_act_fn=__A		,						resnet_groups=__A		,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,						resnet_time_scale_shift=__A		,						upsample_type=__A		,						)
              self.up_blocks.append(__A )
              A    =				output_channel

        # out
        A    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,						32 )
        A    =				nn.GroupNorm(num_channels=block_out_channels[0]		,						num_groups=__A		,						eps=__A )
        A    =				nn.SiLU()
        A    =				nn.Convad(block_out_channels[0]		,						__A		,						kernel_size=3		,						padding=1 )







  def 	UpperCamelCase       ( self	: List[str]		,						UpperCamelCase__	: Any		,						UpperCamelCase__	: Dict		,						UpperCamelCase__	: List[str] = None		,						UpperCamelCase__	: Optional[int] = True		,						):
        # 0. center input if necessary
        if self.config.center_input_sample:
              A    =				2 * sample - 1.0

        # 1. time
        A    =				timestep
        if not torch.is_tensor(__A ):
              A    =				torch.tensor([timesteps]		,						dtype=torch.long		,						device=sample.device )
        elif torch.is_tensor(__A ) and len(timesteps.shape ) == 0:
              A    =				timesteps[None].to(sample.device )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        A    =				timesteps * torch.ones(sample.shape[0]		,						dtype=timesteps.dtype		,						device=timesteps.device )

        A    =				self.time_proj(__A )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        A    =				t_emb.to(dtype=self.dtype )
        A    =				self.time_embedding(__A )

        if self.class_embedding is not None:
              if class_labels is None:
                    raise ValueError('class_labels should be provided when doing class conditioning' )

              if self.config.class_embed_type == "timestep":
                    A    =				self.time_proj(__A )

              A    =				self.class_embedding(__A ).to(dtype=self.dtype )
              A    =				emb + class_emb

        # 2. pre-process
        A    =				sample
        A    =				self.conv_in(__A )

        # 3. down
        A    =				(sample,)
        for downsample_block in self.down_blocks:
              if hasattr(__A		,						'skip_conv' ):
                    A ,	A ,	A    =				downsample_block(
                        hidden_states=__A		,						temb=__A		,						skip_sample=__A )
              else:
                    A ,	A    =				downsample_block(hidden_states=__A		,						temb=__A )

              down_block_res_samples += res_samples

        # 4. mid
        A    =				self.mid_block(__A		,						__A )

        # 5. up
        A    =				None
        for upsample_block in self.up_blocks:
              A    =				down_block_res_samples[-len(upsample_block.resnets ) :]
              A    =				down_block_res_samples[: -len(upsample_block.resnets )]

              if hasattr(__A		,						'skip_conv' ):
                    A ,	A    =				upsample_block(__A		,						__A		,						__A		,						__A )
              else:
                    A    =				upsample_block(__A		,						__A		,						__A )

        # 6. post-process
        A    =				self.conv_norm_out(__A )
        A    =				self.conv_act(__A )
        A    =				self.conv_out(__A )

        if skip_sample is not None:
              sample += skip_sample

        if self.config.time_embedding_type == "fourier":
              A    =				timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:] ))) )
              A    =				sample / timesteps

        if not return_dict:
              return (sample,)

        return UNetaDOutput(sample=__A )






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







  def 	UpperCamelCase       ( self	: str ):
        super().setUp()
        A    =				[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A    =				dict(zip(UpperCamelCase__		,						range(len(UpperCamelCase__ ) ) ) )
        A    =				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        A    =				{'unk_token': '<unk>'}

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







  def 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						max_length=len(UpperCamelCase__ )		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )

              self.assertEqual((2, 9)		,						batch.input_ids.shape )
              self.assertEqual((2, 9)		,						batch.attention_mask.shape )
              A    =				batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase__		,						UpperCamelCase__ )
              # Test that special tokens are reset







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'		,						UpperCamelCase__ )
              self.assertIn('attention_mask'		,						UpperCamelCase__ )
              self.assertNotIn('labels'		,						UpperCamelCase__ )
              self.assertNotIn('decoder_attention_mask'		,						UpperCamelCase__ )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(
                  ['I am a small frog' * 1024, 'I am a small frog']		,						padding=UpperCamelCase__		,						truncation=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )
              self.assertEqual(batch.input_ids.shape		,						(2, 1024) )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )







  def 	UpperCamelCase       ( self	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                    A    =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				tokenizer_p.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['token_type_ids'] )		,						sum(tokens_p['token_type_ids'] ) )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )		,						sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )		,						)

                    A    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
                    self.assertSequenceEqual(
                        UpperCamelCase__		,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )



