



"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


snake_case_				     =       logging.getLogger(__name__)







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
						__UpperCamelCase						=							field(
						    default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
						__UpperCamelCase						=							field(
						    default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
						__UpperCamelCase						=							field(
						    default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,   )
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Whether tp freeze the encoder."""}				)
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Whether  to freeze the embeddings."""}				)







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
						__UpperCamelCase						=							field(
						    default="""summarization"""	,   metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}	,   )
						__UpperCamelCase						=							field(
						    default=10_24	,   metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }	,   )
						__UpperCamelCase						=							field(
						    default=1_28	,   metadata={
						        """help""": (
						            """The maximum total sequence length for target text after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }	,   )
						__UpperCamelCase						=							field(
						    default=1_42	,   metadata={
						        """help""": (
						            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded. """
						            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
						            """during ``evaluate`` and ``predict``."""
						        )
						    }	,   )
						__UpperCamelCase						=							field(
						    default=1_42	,   metadata={
						        """help""": (
						            """The maximum total sequence length for test target text after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }	,   )
						__UpperCamelCase						=							field(default=-1	,   metadata={"""help""": """# training examples. -1 means use all."""}				)
						__UpperCamelCase						=							field(default=-1	,   metadata={"""help""": """# validation examples. -1 means use all."""}				)
						__UpperCamelCase						=							field(default=-1	,   metadata={"""help""": """# test examples. -1 means use all."""}				)
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Source language id for translation."""}				)
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """Target language id for translation."""}				)
						__UpperCamelCase						=							field(default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """# num_beams to use for evaluation."""}				)
						__UpperCamelCase						=							field(
						    default=SCREAMING_SNAKE_CASE_	,   metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}	,   )


def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						logger.info(F"""***** {split} metrics *****"""						)
						for key in sorted(metrics.keys()						):
												logger.info(F"""  {key} = {metrics[key]}"""						)
						save_json(lowercase_		,	os.path.join(lowercase_		,	F"""{split}_results.json"""						)						)

def  _lowerCAmelCase      (   ):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						UpperCAmelCase             = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)						)

						if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = parser.parse_args_into_dataclasses()

						check_output_dir(lowercase_						)

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,	datefmt='%m/%d/%Y %H:%M:%S'		,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,	)
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'		,	training_args.local_rank		,	training_args.device		,	training_args.n_gpu		,	bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED						)		,	training_args.fpaa		,	)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
						logger.info('Training/evaluation parameters %s'		,	lowercase_						)

						# Set seed
						set_seed(training_args.seed						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						UpperCAmelCase             = AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,	cache_dir=model_args.cache_dir		,	)

						UpperCAmelCase             = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
												if getattr(lowercase_		,	lowercase_		,	lowercase_						):
																		assert hasattr(lowercase_		,	lowercase_						), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(lowercase_		,	lowercase_		,	getattr(lowercase_		,	lowercase_						)						)

						UpperCAmelCase             = AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,	cache_dir=model_args.cache_dir		,	)
						UpperCAmelCase             = AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path		,	from_tf='.ckpt' in model_args.model_name_or_path		,	config=lowercase_		,	cache_dir=model_args.cache_dir		,	)

						# use task specific params
						use_task_specific_params(lowercase_		,	data_args.task						)

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												UpperCAmelCase             = model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(lowercase_		,	(MBartTokenizer, MBartTokenizerFast)						):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(lowercase_		,	lowercase_						):
																		UpperCAmelCase             = tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		UpperCAmelCase             = tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

						if model_args.freeze_embeds:
												freeze_embeds(lowercase_						)
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()						)
												assert_all_frozen(model.get_encoder()						)

						UpperCAmelCase             = SeqaSeqDataset

						# Get datasets
						UpperCAmelCase             = (
						    dataset_class(
						        lowercase_		,	type_path='train'		,	data_dir=data_args.data_dir		,	n_obs=data_args.n_train		,	max_target_length=data_args.max_target_length		,	max_source_length=data_args.max_source_length		,	prefix=model.config.prefix or ''		,	)
						    if training_args.do_train
						    else None
						)
						UpperCAmelCase             = (
						    dataset_class(
						        lowercase_		,	type_path='val'		,	data_dir=data_args.data_dir		,	n_obs=data_args.n_val		,	max_target_length=data_args.val_max_target_length		,	max_source_length=data_args.max_source_length		,	prefix=model.config.prefix or ''		,	)
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						UpperCAmelCase             = (
						    dataset_class(
						        lowercase_		,	type_path='test'		,	data_dir=data_args.data_dir		,	n_obs=data_args.n_test		,	max_target_length=data_args.test_max_target_length		,	max_source_length=data_args.max_source_length		,	prefix=model.config.prefix or ''		,	)
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						UpperCAmelCase             = (
						    build_compute_metrics_fn(data_args.task		,	lowercase_						) if training_args.predict_with_generate else None
						)
						UpperCAmelCase             = SeqaSeqTrainer(
						    model=lowercase_		,	args=lowercase_		,	data_args=lowercase_		,	train_dataset=lowercase_		,	eval_dataset=lowercase_		,	data_collator=SeqaSeqDataCollator(
						        lowercase_		,	lowercase_		,	model.config.decoder_start_token_id		,	training_args.tpu_num_cores						)		,	compute_metrics=lowercase_		,	tokenizer=lowercase_		,	)

						UpperCAmelCase             = {}
						# Training
						if training_args.do_train:
												logger.info('*** Train ***'						)

												UpperCAmelCase             = trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												UpperCAmelCase             = train_result.metrics
												UpperCAmelCase             = data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics('train'		,	lowercase_		,	training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir		,	'trainer_state.json'						)						)

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						if training_args.do_eval:
												logger.info('*** Evaluate ***'						)

												UpperCAmelCase             = trainer.evaluate(metric_key_prefix='val'						)
												UpperCAmelCase             = data_args.n_val
												UpperCAmelCase             = round(metrics['val_loss']		,	4						)

												if trainer.is_world_process_zero():
																		handle_metrics('val'		,	lowercase_		,	training_args.output_dir						)
																		all_metrics.update(lowercase_						)

						if training_args.do_predict:
												logger.info('*** Predict ***'						)

												UpperCAmelCase             = trainer.predict(test_dataset=lowercase_		,	metric_key_prefix='test'						)
												UpperCAmelCase             = test_output.metrics
												UpperCAmelCase             = data_args.n_test

												if trainer.is_world_process_zero():
																		UpperCAmelCase             = round(metrics['test_loss']		,	4						)
																		handle_metrics('test'		,	lowercase_		,	training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		if training_args.predict_with_generate:
																								UpperCAmelCase             = tokenizer.batch_decode(
																								    test_output.predictions		,	skip_special_tokens=lowercase_		,	clean_up_tokenization_spaces=lowercase_						)
																								UpperCAmelCase             = lmap(str.strip		,	lowercase_						)
																								write_txt_file(lowercase_		,	os.path.join(training_args.output_dir		,	'test_generations.txt'						)						)

						if trainer.is_world_process_zero():
												save_json(lowercase_		,	os.path.join(training_args.output_dir		,	'all_results.json'						)						)

						return all_metrics

def  _lowerCAmelCase      (   lowercase_						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
				main()











'''simple docstring'''

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)






@flax.struct.dataclass
class 						a				(   _lowerCamelCase     ):
				snake_case_      =		42







@flax_register_to_config
class 						a				(   nn.Module   ,     _lowerCamelCase   ,     _lowerCamelCase     ):
				snake_case_      =		32
				snake_case_      =		4
				snake_case_      =		4
				snake_case_      =		(
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "DownBlock2D",
				)
				snake_case_      =		("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
				snake_case_      =		False
				snake_case_      =		(320, 640, 1_280, 1_280)
				snake_case_      =		2
				snake_case_      =		8
				snake_case_      =		None
				snake_case_      =		1_280
				snake_case_      =		0.0
				snake_case_      =		False
				snake_case_      =		jnp.floataa
				snake_case_      =		True
				snake_case_      =		0
				snake_case_      =		False


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					jax.random.KeyArray					):
									# init input tensors
									snake_case_									=				(1, self.in_channels, self.sample_size, self.sample_size)
									snake_case_									=				jnp.zeros(lowercase_       ,				dtype=jnp.floataa					)
									snake_case_									=				jnp.ones((1,)       ,				dtype=jnp.intaa					)
									snake_case_									=				jnp.zeros((1, 1, self.cross_attention_dim)       ,				dtype=jnp.floataa					)

									snake_case_			,snake_case_									=				jax.random.split(lowercase_					)
									snake_case_									=				{'''params''': params_rng, '''dropout''': dropout_rng}

									return self.init(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)["params"]


				def   A_ (		self    :					List[str]					):
									snake_case_									=				self.block_out_channels
									snake_case_									=				block_out_channels[0] * 4

									if self.num_attention_heads is not None:
														raise ValueError(
														    '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''					)

									# If `num_attention_heads` is not defined (which is the case for most models)
									# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
									# The reason for this behavior is to correct for incorrectly named variables that were introduced
									# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
									# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
									# which is why we correct for the naming here.
									snake_case_									=				self.num_attention_heads or self.attention_head_dim

									# input
									snake_case_									=				nn.Conv(
									    block_out_channels[0]       ,				kernel_size=(3, 3)       ,				strides=(1, 1)       ,				padding=((1, 1), (1, 1))       ,				dtype=self.dtype       ,				)

									# time
									snake_case_									=				FlaxTimesteps(
									    block_out_channels[0]       ,				flip_sin_to_cos=self.flip_sin_to_cos       ,				freq_shift=self.config.freq_shift					)
									snake_case_									=				FlaxTimestepEmbedding(lowercase_       ,				dtype=self.dtype					)

									snake_case_									=				self.only_cross_attention
									if isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				(only_cross_attention,) * len(self.down_block_types					)

									if isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				(num_attention_heads,) * len(self.down_block_types					)

									# down
									snake_case_									=				[]
									snake_case_									=				block_out_channels[0]
									for i, down_block_type in enumerate(self.down_block_types					):
														snake_case_									=				output_channel
														snake_case_									=				block_out_channels[i]
														snake_case_									=				i == len(lowercase_					) - 1

														if down_block_type == "CrossAttnDownBlock2D":
																			snake_case_									=				FlaxCrossAttnDownBlockaD(
																			    in_channels=lowercase_       ,				out_channels=lowercase_       ,				dropout=self.dropout       ,				num_layers=self.layers_per_block       ,				num_attention_heads=num_attention_heads[i]       ,				add_downsample=not is_final_block       ,				use_linear_projection=self.use_linear_projection       ,				only_cross_attention=only_cross_attention[i]       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)
														else:
																			snake_case_									=				FlaxDownBlockaD(
																			    in_channels=lowercase_       ,				out_channels=lowercase_       ,				dropout=self.dropout       ,				num_layers=self.layers_per_block       ,				add_downsample=not is_final_block       ,				dtype=self.dtype       ,				)

														down_blocks.append(lowercase_					)
									snake_case_									=				down_blocks

									# mid
									snake_case_									=				FlaxUNetMidBlockaDCrossAttn(
									    in_channels=block_out_channels[-1]       ,				dropout=self.dropout       ,				num_attention_heads=num_attention_heads[-1]       ,				use_linear_projection=self.use_linear_projection       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)

									# up
									snake_case_									=				[]
									snake_case_									=				list(reversed(lowercase_					)					)
									snake_case_									=				list(reversed(lowercase_					)					)
									snake_case_									=				list(reversed(lowercase_					)					)
									snake_case_									=				reversed_block_out_channels[0]
									for i, up_block_type in enumerate(self.up_block_types					):
														snake_case_									=				output_channel
														snake_case_									=				reversed_block_out_channels[i]
														snake_case_									=				reversed_block_out_channels[min(i + 1       ,				len(lowercase_					) - 1					)]

														snake_case_									=				i == len(lowercase_					) - 1

														if up_block_type == "CrossAttnUpBlock2D":
																			snake_case_									=				FlaxCrossAttnUpBlockaD(
																			    in_channels=lowercase_       ,				out_channels=lowercase_       ,				prev_output_channel=lowercase_       ,				num_layers=self.layers_per_block + 1       ,				num_attention_heads=reversed_num_attention_heads[i]       ,				add_upsample=not is_final_block       ,				dropout=self.dropout       ,				use_linear_projection=self.use_linear_projection       ,				only_cross_attention=only_cross_attention[i]       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)
														else:
																			snake_case_									=				FlaxUpBlockaD(
																			    in_channels=lowercase_       ,				out_channels=lowercase_       ,				prev_output_channel=lowercase_       ,				num_layers=self.layers_per_block + 1       ,				add_upsample=not is_final_block       ,				dropout=self.dropout       ,				dtype=self.dtype       ,				)

														up_blocks.append(lowercase_					)
														snake_case_									=				output_channel
									snake_case_									=				up_blocks

									# out
									snake_case_									=				nn.GroupNorm(num_groups=32       ,				epsilon=1e-5					)
									snake_case_									=				nn.Conv(
									    self.out_channels       ,				kernel_size=(3, 3)       ,				strides=(1, 1)       ,				padding=((1, 1), (1, 1))       ,				dtype=self.dtype       ,				)


				def __call__(		self    :					Union[str, Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					Any       ,				lowercase_    :					int=None       ,				lowercase_    :					Any=None       ,				lowercase_    :					bool = True       ,				lowercase_    :					bool = False       ,				):
									# 1. time
									if not isinstance(lowercase_       ,				jnp.ndarray					):
														snake_case_									=				jnp.array([timesteps]       ,				dtype=jnp.intaa					)
									elif isinstance(lowercase_       ,				jnp.ndarray					) and len(timesteps.shape					) == 0:
														snake_case_									=				timesteps.astype(dtype=jnp.floataa					)
														snake_case_									=				jnp.expand_dims(lowercase_       ,				0					)

									snake_case_									=				self.time_proj(lowercase_					)
									snake_case_									=				self.time_embedding(lowercase_					)

									# 2. pre-process
									snake_case_									=				jnp.transpose(lowercase_       ,				(0, 2, 3, 1)					)
									snake_case_									=				self.conv_in(lowercase_					)

									# 3. down
									snake_case_									=				(sample,)
									for down_block in self.down_blocks:
														if isinstance(lowercase_       ,				lowercase_					):
																			snake_case_			,snake_case_									=				down_block(lowercase_       ,				lowercase_       ,				lowercase_       ,				deterministic=not train					)
														else:
																			snake_case_			,snake_case_									=				down_block(lowercase_       ,				lowercase_       ,				deterministic=not train					)
														down_block_res_samples += res_samples

									if down_block_additional_residuals is not None:
														snake_case_									=				()

														for down_block_res_sample, down_block_additional_residual in zip(
														    lowercase_       ,				lowercase_					):
																			down_block_res_sample += down_block_additional_residual
																			new_down_block_res_samples += (down_block_res_sample,)

														snake_case_									=				new_down_block_res_samples

									# 4. mid
									snake_case_									=				self.mid_block(lowercase_       ,				lowercase_       ,				lowercase_       ,				deterministic=not train					)

									if mid_block_additional_residual is not None:
														sample += mid_block_additional_residual

									# 5. up
									for up_block in self.up_blocks:
														snake_case_									=				down_block_res_samples[-(self.layers_per_block + 1) :]
														snake_case_									=				down_block_res_samples[: -(self.layers_per_block + 1)]
														if isinstance(lowercase_       ,				lowercase_					):
																			snake_case_									=				up_block(
																			    lowercase_       ,				temb=lowercase_       ,				encoder_hidden_states=lowercase_       ,				res_hidden_states_tuple=lowercase_       ,				deterministic=not train       ,				)
														else:
																			snake_case_									=				up_block(lowercase_       ,				temb=lowercase_       ,				res_hidden_states_tuple=lowercase_       ,				deterministic=not train					)

        # 6. post-process
									snake_case_									=				self.conv_norm_out(lowercase_					)
									snake_case_									=				nn.silu(lowercase_					)
									snake_case_									=				self.conv_out(lowercase_					)
									snake_case_									=				jnp.transpose(lowercase_       ,				(0, 3, 1, 2)					)

									if not return_dict:
														return (sample,)

									return FlaxUNetaDConditionOutput(sample=lowercase_					)

