



'''simple docstring'''


import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


lowercase_      :				List[str]   					=    logging.get_logger(__name__)

# General docstring
lowercase_      :				str   					=    '''PoolFormerConfig'''

# Base docstring
lowercase_      :				Optional[int]   					=    '''sail/poolformer_s12'''
lowercase_      :				str   					=    [1, 512, 7, 7]

# Image classification docstring
lowercase_      :				int   					=    '''sail/poolformer_s12'''
lowercase_      :				Dict   					=    '''tabby, tabby cat'''

lowercase_      :				int   					=    [
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      float = 0.0 ,  lowercase_    :      bool = False						):
						if drop_prob == 0.0 or not training:
												return input
						lowercase        =							1 - drop_prob
						lowercase        =							(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
						lowercase        =							keep_prob + torch.rand(lowercase_ ,  dtype=input.dtype ,  device=input.device						)
						random_tensor.floor_()  # binarize
						lowercase        =							input.div(lowercase_						) * random_tensor
						return output




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase = None						)    ->    None:
							'''simple docstring'''






							super().__init__()
							lowercase        =							drop_prob







	def   _a   (    self ,				_lowerCAmelCase						)    ->    torch.Tensor:
							'''simple docstring'''






							return drop_path(_lowerCAmelCase ,				self.drop_prob ,				self.training						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							return "p={}".format(self.drop_prob						)




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							super().__init__()
							lowercase        =							patch_size if isinstance(_lowerCAmelCase ,				collections.abc.Iterable						) else (patch_size, patch_size)
							lowercase        =							stride if isinstance(_lowerCAmelCase ,				collections.abc.Iterable						) else (stride, stride)
							lowercase        =							padding if isinstance(_lowerCAmelCase ,				collections.abc.Iterable						) else (padding, padding)

							lowercase        =							nn.Convad(_lowerCAmelCase ,				_lowerCAmelCase ,				kernel_size=_lowerCAmelCase ,				stride=_lowerCAmelCase ,				padding=_lowerCAmelCase						)
							lowercase        =							norm_layer(_lowerCAmelCase						) if norm_layer else nn.Identity()







	def   _a   (    self ,				_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.projection(_lowerCAmelCase						)
							lowercase        =							self.norm(_lowerCAmelCase						)
							return embeddings




class      __UpperCamelCase       (nn.GroupNorm			):







	def __init__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							super().__init__(1 ,				_lowerCAmelCase ,				**_lowerCAmelCase						)




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							super().__init__()
							lowercase        =							nn.AvgPoolad(_lowerCAmelCase ,				stride=1 ,				padding=pool_size // 2 ,				count_include_pad=_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							return self.pool(_lowerCAmelCase						) - hidden_states




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							super().__init__()
							lowercase        =							nn.Convad(_lowerCAmelCase ,				_lowerCAmelCase ,				1						)
							lowercase        =							nn.Convad(_lowerCAmelCase ,				_lowerCAmelCase ,				1						)
							lowercase        =							PoolFormerDropPath(_lowerCAmelCase						)
							if isinstance(config.hidden_act ,				_lowerCAmelCase						):
													lowercase        =							ACTaFN[config.hidden_act]
							else:
													lowercase        =							config.hidden_act







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.conva(_lowerCAmelCase						)
							lowercase        =							self.act_fn(_lowerCAmelCase						)
							lowercase        =							self.drop(_lowerCAmelCase						)
							lowercase        =							self.conva(_lowerCAmelCase						)
							lowercase        =							self.drop(_lowerCAmelCase						)

							return hidden_states




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							lowercase        =							PoolFormerPooling(_lowerCAmelCase						)
							lowercase        =							PoolFormerOutput(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
							lowercase        =							PoolFormerGroupNorm(_lowerCAmelCase						)
							lowercase        =							PoolFormerGroupNorm(_lowerCAmelCase						)

							# Useful for training neural nets
							lowercase        =							PoolFormerDropPath(_lowerCAmelCase						) if drop_path > 0.0 else nn.Identity()
							lowercase        =							config.use_layer_scale
							if config.use_layer_scale:
													lowercase        =							nn.Parameter(
													    config.layer_scale_init_value * torch.ones((_lowerCAmelCase)						) ,				requires_grad=_lowerCAmelCase						)
													lowercase        =							nn.Parameter(
													    config.layer_scale_init_value * torch.ones((_lowerCAmelCase)						) ,				requires_grad=_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							if self.use_layer_scale:
													lowercase        =							self.pooling(self.before_norm(_lowerCAmelCase						)						)
													lowercase        =							self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * pooling_output
													# First residual connection
													lowercase        =							hidden_states + self.drop_path(_lowerCAmelCase						)
													lowercase        =							()

													lowercase        =							self.output(self.after_norm(_lowerCAmelCase						)						)
													lowercase        =							self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * layer_output
													# Second residual connection
													lowercase        =							hidden_states + self.drop_path(_lowerCAmelCase						)

													lowercase        =							(output,) + outputs
													return outputs

							else:
													lowercase        =							self.drop_path(self.pooling(self.before_norm(_lowerCAmelCase						)						)						)
													# First residual connection
													lowercase        =							pooling_output + hidden_states
													lowercase        =							()

													# Second residual connection inside the PoolFormerOutput block
													lowercase        =							self.drop_path(self.output(self.after_norm(_lowerCAmelCase						)						)						)
													lowercase        =							hidden_states + layer_output

													lowercase        =							(output,) + outputs
													return outputs




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							lowercase        =							config
							# stochastic depth decay rule
							lowercase        =							[x.item() for x in torch.linspace(0 ,				config.drop_path_rate ,				sum(config.depths						)						)]

							# patch embeddings
							lowercase        =							[]
							for i in range(config.num_encoder_blocks						):
													embeddings.append(
													    PoolFormerEmbeddings(
													        patch_size=config.patch_sizes[i] ,				stride=config.strides[i] ,				padding=config.padding[i] ,				num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] ,				hidden_size=config.hidden_sizes[i] ,				)						)
							lowercase        =							nn.ModuleList(_lowerCAmelCase						)

							# Transformer blocks
							lowercase        =							[]
							lowercase        =							0
							for i in range(config.num_encoder_blocks						):
													# each block consists of layers
													lowercase        =							[]
													if i != 0:
																			cur += config.depths[i - 1]
													for j in range(config.depths[i]						):
																			layers.append(
																			    PoolFormerLayer(
																			        _lowerCAmelCase ,				num_channels=config.hidden_sizes[i] ,				pool_size=config.pool_size ,				hidden_size=config.hidden_sizes[i] ,				intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio						) ,				drop_path=dpr[cur + j] ,				)						)
													blocks.append(nn.ModuleList(_lowerCAmelCase						)						)

							lowercase        =							nn.ModuleList(_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=False ,				_lowerCAmelCase=True						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							() if output_hidden_states else None

							lowercase        =							pixel_values
							for idx, layers in enumerate(zip(self.patch_embeddings ,				self.block						)						):
													lowercase     ,   lowercase        =							layers
													# Get patch embeddings from hidden_states
													lowercase        =							embedding_layer(_lowerCAmelCase						)
													# Send the embeddings through the blocks
													for _, blk in enumerate(_lowerCAmelCase						):
																			lowercase        =							blk(_lowerCAmelCase						)
																			lowercase        =							layer_outputs[0]

													if output_hidden_states:
																			lowercase        =							all_hidden_states + (hidden_states,)

							if not return_dict:
													return tuple(v for v in [hidden_states, all_hidden_states] if v is not None						)

							return BaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase ,				hidden_states=_lowerCAmelCase						)




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					PoolFormerConfig
	__A			  =					'''poolformer'''
	__A			  =					'''pixel_values'''
	__A			  =					True







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							if isinstance(_lowerCAmelCase ,				(nn.Linear, nn.Convad)						):
													module.weight.data.normal_(mean=0.0 ,				std=self.config.initializer_range						)
													if module.bias is not None:
																			module.bias.data.zero_()
							elif isinstance(_lowerCAmelCase ,				nn.LayerNorm						):
													module.bias.data.zero_()
													module.weight.data.fill_(1.0						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    int:
							'''simple docstring'''






							if isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							value




lowercase_      :				Any   					=    r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowercase_      :				List[str]   					=    r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
'''




@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''			,   _UpperCAmelCase			,   )
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							super().__init__(_lowerCAmelCase						)
							lowercase        =							config

							lowercase        =							PoolFormerEncoder(_lowerCAmelCase						)

							# Initialize weights and apply final processing
							self.post_init()







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							return self.embeddings.patch_embeddings







	@add_start_docstrings_to_model_forward(_lowerCAmelCase						)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC ,				output_type=_lowerCAmelCase ,				config_class=_CONFIG_FOR_DOC ,				modality="""vision""" ,				expected_output=_EXPECTED_OUTPUT_SHAPE ,				)
	def   _a   (    self ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				)    ->    Union[Tuple, BaseModelOutputWithNoAttention]:
							'''simple docstring'''






							lowercase        =							(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase        =							return_dict if return_dict is not None else self.config.use_return_dict

							if pixel_values is None:
													raise ValueError("""You have to specify pixel_values"""						)

							lowercase        =							self.encoder(
							    _lowerCAmelCase ,				output_hidden_states=_lowerCAmelCase ,				return_dict=_lowerCAmelCase ,				)
							lowercase        =							encoder_outputs[0]

							if not return_dict:
													return (sequence_output, None) + encoder_outputs[1:]

							return BaseModelOutputWithNoAttention(
							    last_hidden_state=_lowerCAmelCase ,				hidden_states=encoder_outputs.hidden_states ,				)




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							super().__init__()
							lowercase        =							nn.Linear(config.hidden_size ,				config.hidden_size						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.dense(_lowerCAmelCase						)
							return output




@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''			,   _UpperCAmelCase			,   )
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							super().__init__(_lowerCAmelCase						)
							lowercase        =							config.num_labels
							lowercase        =							PoolFormerModel(_lowerCAmelCase						)

							# Final norm
							lowercase        =							PoolFormerGroupNorm(config.hidden_sizes[-1]						)
							# Classifier head
							lowercase        =							(
							    nn.Linear(config.hidden_sizes[-1] ,				config.num_labels						) if config.num_labels > 0 else nn.Identity()
							)

							# Initialize weights and apply final processing
							self.post_init()







	@add_start_docstrings_to_model_forward(_lowerCAmelCase						)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT ,				output_type=_lowerCAmelCase ,				config_class=_CONFIG_FOR_DOC ,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,				)
	def   _a   (    self ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				)    ->    Union[Tuple, ImageClassifierOutputWithNoAttention]:
							'''simple docstring'''






							lowercase        =							return_dict if return_dict is not None else self.config.use_return_dict

							lowercase        =							self.poolformer(
							    _lowerCAmelCase ,				output_hidden_states=_lowerCAmelCase ,				return_dict=_lowerCAmelCase ,				)

							lowercase        =							outputs[0]

							lowercase        =							self.classifier(self.norm(_lowerCAmelCase						).mean([-2, -1]						)						)

							lowercase        =							None
							if labels is not None:
													if self.config.problem_type is None:
																			if self.num_labels == 1:
																									lowercase        =							"""regression"""
																			elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																									lowercase        =							"""single_label_classification"""
																			else:
																									lowercase        =							"""multi_label_classification"""

													if self.config.problem_type == "regression":
																			lowercase        =							MSELoss()
																			if self.num_labels == 1:
																									lowercase        =							loss_fct(logits.squeeze() ,				labels.squeeze()						)
																			else:
																									lowercase        =							loss_fct(_lowerCAmelCase ,				_lowerCAmelCase						)
													elif self.config.problem_type == "single_label_classification":
																			lowercase        =							CrossEntropyLoss()
																			lowercase        =							loss_fct(logits.view(-1 ,				self.num_labels						) ,				labels.view(-1						)						)
													elif self.config.problem_type == "multi_label_classification":
																			lowercase        =							BCEWithLogitsLoss()
																			lowercase        =							loss_fct(_lowerCAmelCase ,				_lowerCAmelCase						)

							if not return_dict:
													lowercase        =							(logits,) + outputs[2:]
													return ((loss,) + output) if loss is not None else output

							return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase ,				logits=_lowerCAmelCase ,				hidden_states=outputs.hidden_states						)







'''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,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




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




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
	__A			  =					field(
	    default='''summarization'''			,   metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}			,   )
	__A			  =					field(
	    default=1024			,   metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }			,   )
	__A			  =					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.'''
	        )
	    }			,   )
	__A			  =					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``.'''
	        )
	    }			,   )
	__A			  =					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.'''
	        )
	    }			,   )
	__A			  =					field(default=-1			,   metadata={'''help''': '''# training examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# validation examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# test examples. -1 means use all.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Source language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Target language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''# num_beams to use for evaluation.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}			,   )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      int ,  lowercase_    :      List[Any]						):
						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      SCREAMING_SNAKE_CASE				(				):
						# 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.

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

						lowercase        =							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 ,  )

						lowercase        =							("""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_						)						)

						lowercase        =							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 ,  )
						lowercase        =							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:
												lowercase        =							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_						):
																		lowercase        =							tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowercase        =							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()						)

						lowercase        =							SeqaSeqDataset

						# Get datasets
						lowercase        =							(
						    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
						)
						lowercase        =							(
						    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
						)
						lowercase        =							(
						    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
						lowercase        =							(
						    build_compute_metrics_fn(data_args.task ,  lowercase_						) if training_args.predict_with_generate else None
						)
						lowercase        =							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_ ,  )

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

												lowercase        =							trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												lowercase        =							train_result.metrics
												lowercase        =							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 ***"""						)

												lowercase        =							trainer.evaluate(metric_key_prefix="""val"""						)
												lowercase        =							data_args.n_val
												lowercase        =							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 ***"""						)

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  metric_key_prefix="""test"""						)
												lowercase        =							test_output.metrics
												lowercase        =							data_args.n_test

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

																		if training_args.predict_with_generate:
																								lowercase        =							tokenizer.batch_decode(
																								    test_output.predictions ,  skip_special_tokens=lowercase_ ,  clean_up_tokenization_spaces=lowercase_						)
																								lowercase        =							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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


