
"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


lowerCAmelCase__				=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__				=					'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'



def 							_lowerCamelCase			(			__a,				__a,				__a=8     ):
		SCREAMING_SNAKE_CASE_    			=			h // scale_factor**2
		if h % scale_factor**2 != 0:
				new_h += 1
		SCREAMING_SNAKE_CASE_    			=			w // scale_factor**2
		if w % scale_factor**2 != 0:
				new_w += 1
		return new_h * scale_factor, new_w * scale_factor





class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__()

									self.register_modules(
									    text_encoder=SCREAMING_SNAKE_CASE_							,							tokenizer=SCREAMING_SNAKE_CASE_							,							unet=SCREAMING_SNAKE_CASE_							,							scheduler=SCREAMING_SNAKE_CASE_							,							movq=SCREAMING_SNAKE_CASE_							,							)
									SCREAMING_SNAKE_CASE_    			=			2 ** (len(self.movq.config.block_out_channels     ) - 1)

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if latents is None:
											SCREAMING_SNAKE_CASE_    			=			randn_tensor(SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_							,							device=SCREAMING_SNAKE_CASE_							,							dtype=SCREAMING_SNAKE_CASE_     )
									else:
											if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'     )
											SCREAMING_SNAKE_CASE_    			=			latents.to(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			latents * scheduler.init_noise_sigma
									return latents

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			len(SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) else 1
									# get prompt text embeddings
									SCREAMING_SNAKE_CASE_    			=			self.tokenizer(
									    SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							truncation=SCREAMING_SNAKE_CASE_							,							max_length=77							,							return_attention_mask=SCREAMING_SNAKE_CASE_							,							add_special_tokens=SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''							,							)

									SCREAMING_SNAKE_CASE_    			=			text_inputs.input_ids
									SCREAMING_SNAKE_CASE_    			=			self.tokenizer(SCREAMING_SNAKE_CASE_							,							padding='''longest'''							,							return_tensors='''pt'''     ).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
											logger.warning(
											    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
											    f' {self.tokenizer.model_max_length} tokens: {removed_text}'     )

									SCREAMING_SNAKE_CASE_    			=			text_input_ids.to(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			text_inputs.attention_mask.to(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.text_encoder(
									    input_ids=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )
									SCREAMING_SNAKE_CASE_    			=			text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )
									SCREAMING_SNAKE_CASE_    			=			text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )

									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE_    			=			42
											if negative_prompt is None:
													SCREAMING_SNAKE_CASE_    			=			[''''''] * batch_size
											elif type(SCREAMING_SNAKE_CASE_     ) is not type(SCREAMING_SNAKE_CASE_     ):
													raise TypeError(
													    f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_     )} !='
													    f' {type(SCREAMING_SNAKE_CASE_     )}.'     )
											elif isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			[negative_prompt]
											elif batch_size != len(SCREAMING_SNAKE_CASE_     ):
													raise ValueError(
													    f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_     )}, but `prompt`:'
													    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
													    ''' the batch size of `prompt`.'''     )
											else:
													SCREAMING_SNAKE_CASE_    			=			negative_prompt

											SCREAMING_SNAKE_CASE_    			=			self.tokenizer(
											    SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							max_length=77							,							truncation=SCREAMING_SNAKE_CASE_							,							return_attention_mask=SCREAMING_SNAKE_CASE_							,							add_special_tokens=SCREAMING_SNAKE_CASE_							,							return_tensors='''pt'''							,							)
											SCREAMING_SNAKE_CASE_    			=			uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			uncond_input.attention_mask.to(SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.text_encoder(
											    input_ids=SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_     )

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

											SCREAMING_SNAKE_CASE_    			=			negative_prompt_embeds.shape[1]
											SCREAMING_SNAKE_CASE_    			=			negative_prompt_embeds.repeat(1							,							SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			negative_prompt_embeds.view(batch_size * num_images_per_prompt							,							SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_    			=			uncond_text_encoder_hidden_states.shape[1]
											SCREAMING_SNAKE_CASE_    			=			uncond_text_encoder_hidden_states.repeat(1							,							SCREAMING_SNAKE_CASE_							,							1     )
											SCREAMING_SNAKE_CASE_    			=			uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt							,							SCREAMING_SNAKE_CASE_							,							-1     )
											SCREAMING_SNAKE_CASE_    			=			uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )

											# done duplicates

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											SCREAMING_SNAKE_CASE_    			=			torch.cat([negative_prompt_embeds, prompt_embeds]     )
											SCREAMING_SNAKE_CASE_    			=			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

											SCREAMING_SNAKE_CASE_    			=			torch.cat([uncond_text_mask, text_mask]     )

									return prompt_embeds, text_encoder_hidden_states, text_mask

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=0     ):

									"""simple docstring"""







									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''     )

									SCREAMING_SNAKE_CASE_    			=			torch.device(f'cuda:{gpu_id}'     )

									SCREAMING_SNAKE_CASE_    			=			[
									    self.unet,
									    self.text_encoder,
									    self.movq,
									]
									for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
													cpu_offload(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=0     ):

									"""simple docstring"""







									if is_accelerate_available() and is_accelerate_version('''>='''							,							'''0.17.0.dev0'''     ):
											from accelerate import cpu_offload_with_hook
									else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''     )

									SCREAMING_SNAKE_CASE_    			=			torch.device(f'cuda:{gpu_id}'     )

									if self.device.type != "cpu":
											self.to('''cpu'''							,							silence_dtype_warnings=SCREAMING_SNAKE_CASE_     )
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									SCREAMING_SNAKE_CASE_    			=			None
									for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			cpu_offload_with_hook(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_module_hook=SCREAMING_SNAKE_CASE_     )

									if self.safety_checker is not None:
											SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			cpu_offload_with_hook(self.safety_checker							,							SCREAMING_SNAKE_CASE_							,							prev_module_hook=SCREAMING_SNAKE_CASE_     )

									# We'll offload the last model manually.
									SCREAMING_SNAKE_CASE_    			=			hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def _lowercase       (self     ):

									"""simple docstring"""







									if not hasattr(self.unet							,							'''_hf_hook'''     ):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(SCREAMING_SNAKE_CASE_							,							'''_hf_hook'''     )
											    and hasattr(module._hf_hook							,							'''execution_device'''     )
											    and module._hf_hook.execution_device is not None
											):
													return torch.device(module._hf_hook.execution_device     )
									return self.device







							@torch.no_grad()
							@replace_example_docstring(SCREAMING_SNAKE_CASE_     )
							def __call__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = 5_12							,							SCREAMING_SNAKE_CASE_ = 5_12							,							SCREAMING_SNAKE_CASE_ = 1_00							,							SCREAMING_SNAKE_CASE_ = 4.0							,							SCREAMING_SNAKE_CASE_ = 1							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = "pil"							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			1
									elif isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			len(SCREAMING_SNAKE_CASE_     )
									else:
											raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_     )}'     )

									SCREAMING_SNAKE_CASE_    			=			self._execution_device

									SCREAMING_SNAKE_CASE_    			=			batch_size * num_images_per_prompt
									SCREAMING_SNAKE_CASE_    			=			guidance_scale > 1.0

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self._encode_prompt(
									    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			torch.cat(SCREAMING_SNAKE_CASE_							,							dim=0     )
									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			torch.cat(SCREAMING_SNAKE_CASE_							,							dim=0     )

									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE_    			=			image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )
											SCREAMING_SNAKE_CASE_    			=			negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_							,							dim=0     )

									SCREAMING_SNAKE_CASE_    			=			torch.cat([negative_image_embeds, image_embeds]							,							dim=0     ).to(
									    dtype=prompt_embeds.dtype							,							device=SCREAMING_SNAKE_CASE_     )

									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,							device=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.unet.config.in_channels

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			get_new_h_w(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							self.movq_scale_factor     )

									# create initial latent
									SCREAMING_SNAKE_CASE_    			=			self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)							,							text_encoder_hidden_states.dtype							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							self.scheduler							,							)

									for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_     )     ):
											# expand the latents if we are doing classifier free guidance
											SCREAMING_SNAKE_CASE_    			=			torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

											SCREAMING_SNAKE_CASE_    			=			{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
											SCREAMING_SNAKE_CASE_    			=			self.unet(
											    sample=SCREAMING_SNAKE_CASE_							,							timestep=SCREAMING_SNAKE_CASE_							,							encoder_hidden_states=SCREAMING_SNAKE_CASE_							,							added_cond_kwargs=SCREAMING_SNAKE_CASE_							,							return_dict=SCREAMING_SNAKE_CASE_							,							)[0]

											if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			noise_pred.split(latents.shape[1]							,							dim=1     )
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			noise_pred.chunk(2     )
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			variance_pred.chunk(2     )
													SCREAMING_SNAKE_CASE_    			=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													SCREAMING_SNAKE_CASE_    			=			torch.cat([noise_pred, variance_pred_text]							,							dim=1     )

											if not (
											    hasattr(self.scheduler.config							,							'''variance_type'''     )
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			noise_pred.split(latents.shape[1]							,							dim=1     )

											# compute the previous noisy sample x_t -> x_t-1
											SCREAMING_SNAKE_CASE_    			=			self.scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_							,							).prev_sample
									# post-processing
									SCREAMING_SNAKE_CASE_    			=			self.movq.decode(SCREAMING_SNAKE_CASE_							,							force_not_quantize=SCREAMING_SNAKE_CASE_     )['''sample''']

									if output_type not in ["pt", "np", "pil"]:
											raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'     )

									if output_type in ["np", "pil"]:
											SCREAMING_SNAKE_CASE_    			=			image * 0.5 + 0.5
											SCREAMING_SNAKE_CASE_    			=			image.clamp(0							,							1     )
											SCREAMING_SNAKE_CASE_    			=			image.cpu().permute(0							,							2							,							3							,							1     ).float().numpy()

									if output_type == "pil":
											SCREAMING_SNAKE_CASE_    			=			self.numpy_to_pil(SCREAMING_SNAKE_CASE_     )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_     )

"""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,
)


lowerCAmelCase__				=					logging.getLogger(__name__)





@dataclass
class 			snake_case  :
							UpperCAmelCase__      =   field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,      )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Whether tp freeze the encoder.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Whether  to freeze the embeddings.'''} )







@dataclass
class 			snake_case  :
							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=1_024							,      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=128							,      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=142							,      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=142							,      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=__lowercase							,      metadata={'''help''': '''Source language id for translation.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''Target language id for translation.'''} )
							UpperCAmelCase__      =   field(default=__lowercase							,      metadata={'''help''': '''# num_beams to use for evaluation.'''} )
							UpperCAmelCase__      =   field(
							    default=__lowercase							,      metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}							,      )







def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		logger.info(F'***** {split} metrics *****'     )
		for key in sorted(metrics.keys()     ):
				logger.info(F'  {key} = {metrics[key]}'     )
		save_json(__a,				os.path.join(__a,				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.

		SCREAMING_SNAKE_CASE_    			=			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.
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			parser.parse_args_into_dataclasses()

		check_output_dir(__a     )

		# 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''',				__a     )

		# 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.

		SCREAMING_SNAKE_CASE_    			=			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,				)

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

		SCREAMING_SNAKE_CASE_    			=			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,				)
		SCREAMING_SNAKE_CASE_    			=			AutoModelForSeqaSeqLM.from_pretrained(
		    model_args.model_name_or_path,				from_tf='''.ckpt''' in model_args.model_name_or_path,				config=__a,				cache_dir=model_args.cache_dir,				)

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

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

		# set decoder_start_token_id for MBart
		if model.config.decoder_start_token_id is None and isinstance(__a,				(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(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			tokenizer.lang_code_to_id[data_args.tgt_lang]
				else:
						SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

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

		SCREAMING_SNAKE_CASE_    			=			SeqaSeqDataset

		# Get datasets
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				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
		)
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				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
		)
		SCREAMING_SNAKE_CASE_    			=			(
		    dataset_class(
		        __a,				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
		SCREAMING_SNAKE_CASE_    			=			(
		    build_compute_metrics_fn(data_args.task,				__a     ) if training_args.predict_with_generate else None
		)
		SCREAMING_SNAKE_CASE_    			=			SeqaSeqTrainer(
		    model=__a,				args=__a,				data_args=__a,				train_dataset=__a,				eval_dataset=__a,				data_collator=SeqaSeqDataCollator(
		        __a,				__a,				model.config.decoder_start_token_id,				training_args.tpu_num_cores     ),				compute_metrics=__a,				tokenizer=__a,				)

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

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

				trainer.save_model()  # this also saves the tokenizer

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

						# 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 ***'''     )

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

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

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

				SCREAMING_SNAKE_CASE_    			=			trainer.predict(test_dataset=__a,				metric_key_prefix='''test'''     )
				SCREAMING_SNAKE_CASE_    			=			test_output.metrics
				SCREAMING_SNAKE_CASE_    			=			data_args.n_test

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

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

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

		return all_metrics



def 							_lowerCamelCase			(			__a     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()