



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							@slow
							@require_torch
							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""      ,				"""prajjwal1/bert-tiny"""	)
											__a       =		BertTokenizer.from_pretrained("""bert-base-uncased"""	)

											__a       =		bertabert.config.encoder.vocab_size
											__a       =		tokenizer.sep_token_id
											__a       =		tokenizer.cls_token_id
											__a       =		128

											__a       =		datasets.load_dataset("""cnn_dailymail"""      ,				"""3.0.0"""      ,				split="""train[:1%]"""	)
											__a       =		datasets.load_dataset("""cnn_dailymail"""      ,				"""3.0.0"""      ,				split="""validation[:1%]"""	)

											__a       =		train_dataset.select(range(32	)	)
											__a       =		val_dataset.select(range(16	)	)

											__a       =		4

											def _map_to_encoder_decoder_inputs(__lowercase		:		Optional[Any]	):
															# Tokenizer will automatically set [BOS] <text> [EOS]
															__a       =		tokenizer(batch["""article"""]      ,				padding="""max_length"""      ,				truncation=__lowercase      ,				max_length=512	)
															__a       =		tokenizer(batch["""highlights"""]      ,				padding="""max_length"""      ,				truncation=__lowercase      ,				max_length=128	)
															__a       =		inputs.input_ids
															__a       =		inputs.attention_mask

															__a       =		outputs.input_ids
															__a       =		outputs.input_ids.copy()
															__a       =		[
															    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
															]
															__a       =		outputs.attention_mask

															assert all(len(__lowercase	) == 512 for x in inputs.input_ids	)
															assert all(len(__lowercase	) == 128 for x in outputs.input_ids	)

															return batch

											def _compute_metrics(__lowercase		:		str	):
															__a       =		pred.label_ids
															__a       =		pred.predictions

															# all unnecessary tokens are removed
															__a       =		tokenizer.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase	)
															__a       =		tokenizer.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase	)

															__a       =		sum([int(pred_str[i] == label_str[i]	) for i in range(len(__lowercase	)	)]	) / len(__lowercase	)

															return {"accuracy": accuracy}

											# map train dataset
											__a       =		train_dataset.map(
											    _map_to_encoder_decoder_inputs      ,				batched=__lowercase      ,				batch_size=__lowercase      ,				remove_columns=["""article""", """highlights"""]      ,				)
											train_dataset.set_format(
											    type="""torch"""      ,				columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,				)

											# same for validation dataset
											__a       =		val_dataset.map(
											    _map_to_encoder_decoder_inputs      ,				batched=__lowercase      ,				batch_size=__lowercase      ,				remove_columns=["""article""", """highlights"""]      ,				)
											val_dataset.set_format(
											    type="""torch"""      ,				columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,				)

											__a       =		self.get_auto_remove_tmp_dir()

											__a       =		SeqaSeqTrainingArguments(
											    output_dir=__lowercase      ,				per_device_train_batch_size=__lowercase      ,				per_device_eval_batch_size=__lowercase      ,				predict_with_generate=__lowercase      ,				evaluation_strategy="""steps"""      ,				do_train=__lowercase      ,				do_eval=__lowercase      ,				warmup_steps=0      ,				eval_steps=2      ,				logging_steps=2      ,				)

											# instantiate trainer
											__a       =		SeqaSeqTrainer(
											    model=__lowercase      ,				args=__lowercase      ,				compute_metrics=_compute_metrics      ,				train_dataset=__lowercase      ,				eval_dataset=__lowercase      ,				tokenizer=__lowercase      ,				)

											# start training
											trainer.train()





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
