





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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_		     = """PoolFormerConfig"""

# Base docstring
SCREAMING_SNAKE_CASE_		     = """sail/poolformer_s12"""
SCREAMING_SNAKE_CASE_		     = [1, 5_1_2, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_		     = """sail/poolformer_s12"""
SCREAMING_SNAKE_CASE_		     = """tabby, tabby cat"""

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE = 0.0				,		_SCREAMING_SNAKE_CASE = False			)				->     Union[str, Any]:


      '''simple docstring'''


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

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Optional[float] = None )  ->	Tuple:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		drop_prob






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					torch.Tensor )  ->	Optional[Any]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Union[str, Any]:




           '''simple docstring'''


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

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Optional[Any]=None )  ->	Any:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE	=		nn.Convad(UpperCAmelCase_		,UpperCAmelCase_		,kernel_size=UpperCAmelCase_		,stride=UpperCAmelCase_		,padding=UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		norm_layer(UpperCAmelCase_ ) if norm_layer else nn.Identity()






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					List[str] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.projection(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		self.norm(UpperCAmelCase_ )
           return embeddings

class 	UpperCamelCase__				(   nn.GroupNorm     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[Any]		,**lowerCamelCase__		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


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

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Any )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Optional[int] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.pool(UpperCAmelCase_ ) - hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[Any] )  ->	str:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.conva(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		self.act_fn(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		self.drop(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		self.conva(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		self.drop(UpperCAmelCase_ )

           return hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Any )  ->	Any:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		PoolFormerPooling(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		PoolFormerOutput(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		PoolFormerGroupNorm(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		PoolFormerGroupNorm(UpperCAmelCase_ )

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					str )  ->	Optional[int]:




           '''simple docstring'''


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

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

                 SCREAMING_SNAKE_CASE	=		(output,) + outputs
                 return outputs

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

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

                 SCREAMING_SNAKE_CASE	=		(output,) + outputs
                 return outputs

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Dict		,lowerCamelCase__		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


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

           # patch embeddings
           SCREAMING_SNAKE_CASE	=		[]
           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]		,) )
           SCREAMING_SNAKE_CASE	=		nn.ModuleList(UpperCAmelCase_ )

           # Transformer blocks
           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		0
           for i in range(config.num_encoder_blocks ):
                 # each block consists of layers
                 SCREAMING_SNAKE_CASE	=		[]
                 if i != 0:
                       cur += config.depths[i - 1]
                 for j in range(config.depths[i] ):
                       layers.append(
                           PoolFormerLayer(
                               UpperCAmelCase_		,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(UpperCAmelCase_ ) )

           SCREAMING_SNAKE_CASE	=		nn.ModuleList(UpperCAmelCase_ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Optional[Any]=False		,lowerCamelCase__		:					int=True )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		() if output_hidden_states else None

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

                 if output_hidden_states:
                       SCREAMING_SNAKE_CASE	=		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=UpperCAmelCase_		,hidden_states=UpperCAmelCase_ )

class 	UpperCamelCase__				(   __lowerCamelCase     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    PoolFormerConfig
     __snake_case					:			Any				=    "poolformer"
     __snake_case					:			Tuple				=    "pixel_values"
     __snake_case					:			Union[str, Any]				=    True






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           if isinstance(UpperCAmelCase_		,(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(UpperCAmelCase_		,nn.LayerNorm ):
                 module.bias.data.zero_()
                 module.weight.data.fill_(1.0 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[Any]=False )  ->	Tuple:




           '''simple docstring'''


           if isinstance(UpperCAmelCase_		,UpperCAmelCase_ ):
                 SCREAMING_SNAKE_CASE	=		value
SCREAMING_SNAKE_CASE_		     = r"""\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"""

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

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



     '''simple docstring'''

     def __init__(							self		:					Tuple		,lowerCamelCase__		:					int )  ->	List[str]:




           '''simple docstring'''


           super().__init__(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		config

           SCREAMING_SNAKE_CASE	=		PoolFormerEncoder(UpperCAmelCase_ )

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Any:




           '''simple docstring'''


           return self.embeddings.patch_embeddings






     @add_start_docstrings_to_model_forward(UpperCAmelCase_ )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC		,output_type=UpperCAmelCase_		,config_class=_CONFIG_FOR_DOC		,modality="""vision"""		,expected_output=_EXPECTED_OUTPUT_SHAPE		,)
     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Optional[torch.FloatTensor] = None		,lowerCamelCase__		:					Optional[bool] = None		,lowerCamelCase__		:					Optional[bool] = None		,)  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           SCREAMING_SNAKE_CASE	=		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""" )

           SCREAMING_SNAKE_CASE	=		self.encoder(
               UpperCAmelCase_		,output_hidden_states=UpperCAmelCase_		,return_dict=UpperCAmelCase_		,)
           SCREAMING_SNAKE_CASE	=		encoder_outputs[0]

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

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

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Tuple )  ->	Dict:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					List[Any] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.dense(UpperCAmelCase_ )
           return output

@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "							,   __lowerCamelCase							,   )
class 	UpperCamelCase__				(   __lowerCamelCase     ):



     '''simple docstring'''

     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           super().__init__(UpperCAmelCase_ )
           SCREAMING_SNAKE_CASE	=		config.num_labels
           SCREAMING_SNAKE_CASE	=		PoolFormerModel(UpperCAmelCase_ )

           # Final norm
           SCREAMING_SNAKE_CASE	=		PoolFormerGroupNorm(config.hidden_sizes[-1] )
           # Classifier head
           SCREAMING_SNAKE_CASE	=		(
               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(UpperCAmelCase_ )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT		,output_type=UpperCAmelCase_		,config_class=_CONFIG_FOR_DOC		,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,)
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Optional[torch.FloatTensor] = None		,lowerCamelCase__		:					Optional[torch.LongTensor] = None		,lowerCamelCase__		:					Optional[bool] = None		,lowerCamelCase__		:					Optional[bool] = None		,)  ->	List[str]:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE	=		self.poolformer(
               UpperCAmelCase_		,output_hidden_states=UpperCAmelCase_		,return_dict=UpperCAmelCase_		,)

           SCREAMING_SNAKE_CASE	=		outputs[0]

           SCREAMING_SNAKE_CASE	=		self.classifier(self.norm(UpperCAmelCase_ ).mean([-2, -1] ) )

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

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

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

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









import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


SCREAMING_SNAKE_CASE_		     = logging.getLogger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    "summarization"
     __snake_case					:			List[str]				=    ["loss"]
     __snake_case					:			Optional[int]				=    ROUGE_KEYS
     __snake_case					:			List[Any]				=    "rouge2"






     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[Any]		,**lowerCamelCase__		:					List[Any] )  ->	str:




           '''simple docstring'''


           if hparams.sortish_sampler and hparams.gpus > 1:
                 SCREAMING_SNAKE_CASE	=		False
           elif hparams.max_tokens_per_batch is not None:
                 if hparams.gpus > 1:
                       raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
                 if hparams.sortish_sampler:
                       raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )

           super().__init__(lowerCamelCase__		,num_labels=lowerCamelCase__		,mode=self.mode		,**lowerCamelCase__ )
           use_task_specific_params(self.model		,"""summarization""" )
           save_git_info(self.hparams.output_dir )
           SCREAMING_SNAKE_CASE	=		Path(self.output_dir ) / """metrics.json"""
           SCREAMING_SNAKE_CASE	=		Path(self.output_dir ) / """hparams.pkl"""
           pickle_save(self.hparams		,self.hparams_save_path )
           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		defaultdict(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		self.config.model_type
           SCREAMING_SNAKE_CASE	=		self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

           SCREAMING_SNAKE_CASE	=		{
               "data_dir": self.hparams.data_dir,
               "max_source_length": self.hparams.max_source_length,
               "prefix": self.model.config.prefix or "",
           }
           SCREAMING_SNAKE_CASE	=		{
               """train""": self.hparams.n_train,
               """val""": self.hparams.n_val,
               """test""": self.hparams.n_test,
           }
           SCREAMING_SNAKE_CASE	=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

           SCREAMING_SNAKE_CASE	=		{
               """train""": self.hparams.max_target_length,
               """val""": self.hparams.val_max_target_length,
               """test""": self.hparams.test_max_target_length,
           }
           assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
           assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
           if self.hparams.freeze_embeds:
                 freeze_embeds(self.model )
           if self.hparams.freeze_encoder:
                 freeze_params(self.model.get_encoder() )
                 assert_all_frozen(self.model.get_encoder() )

           SCREAMING_SNAKE_CASE	=		get_git_info()["""repo_sha"""]
           SCREAMING_SNAKE_CASE	=		hparams.num_workers
           SCREAMING_SNAKE_CASE	=		None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                 SCREAMING_SNAKE_CASE	=		self.decoder_start_token_id
           SCREAMING_SNAKE_CASE	=		(
               SeqaSeqDataset if hasattr(self.tokenizer		,"""prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
           )
           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
           if self.hparams.eval_max_gen_length is not None:
                 SCREAMING_SNAKE_CASE	=		self.hparams.eval_max_gen_length
           else:
                 SCREAMING_SNAKE_CASE	=		self.model.config.max_length
           SCREAMING_SNAKE_CASE	=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict[str, torch.Tensor] )  ->	Dict[str, List[str]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{
               k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
           }
           save_json(lowerCamelCase__		,Path(self.output_dir ) / """text_batch.json""" )
           save_json({k: v.tolist() for k, v in batch.items()}		,Path(self.output_dir ) / """tok_batch.json""" )

           SCREAMING_SNAKE_CASE	=		True
           return readable_batch






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					str		,**lowerCamelCase__		:					Union[str, Any] )  ->	int:




           '''simple docstring'''


           return self.model(lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					List[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer.batch_decode(
               lowerCamelCase__		,skip_special_tokens=lowerCamelCase__		,clean_up_tokenization_spaces=lowerCamelCase__ )
           return lmap(str.strip		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					dict )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer.pad_token_id
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		batch["""input_ids"""], batch["""attention_mask"""]
           SCREAMING_SNAKE_CASE	=		batch["""labels"""]
           if isinstance(self.model		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		self.model._shift_right(lowerCamelCase__ )
           else:
                 SCREAMING_SNAKE_CASE	=		shift_tokens_right(lowerCamelCase__		,lowerCamelCase__ )
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                 SCREAMING_SNAKE_CASE	=		decoder_input_ids
                 self.save_readable_batch(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		self(lowerCamelCase__		,attention_mask=lowerCamelCase__		,decoder_input_ids=lowerCamelCase__		,use_cache=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		outputs["""logits"""]
           if self.hparams.label_smoothing == 0:
                 # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                 SCREAMING_SNAKE_CASE	=		nn.CrossEntropyLoss(ignore_index=lowerCamelCase__ )

                 assert lm_logits.shape[-1] == self.vocab_size
                 SCREAMING_SNAKE_CASE	=		ce_loss_fct(lm_logits.view(-1		,lm_logits.shape[-1] )		,tgt_ids.view(-1 ) )
           else:
                 SCREAMING_SNAKE_CASE	=		nn.functional.log_softmax(lowerCamelCase__		,dim=-1 )
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		label_smoothed_nll_loss(
                     lowerCamelCase__		,lowerCamelCase__		,self.hparams.label_smoothing		,ignore_index=lowerCamelCase__ )
           return (loss,)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	int:




           '''simple docstring'''


           return self.tokenizer.pad_token_id






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Any )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self._step(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		dict(zip(self.loss_names		,lowerCamelCase__ ) )
           # tokens per batch
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].shape[0]
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].eq(self.pad ).sum()
           SCREAMING_SNAKE_CASE	=		batch["""input_ids"""].eq(self.pad ).float().mean()
           # TODO(SS): make a wandb summary metric for this
           return {"loss": loss_tensors[0], "log": logs}






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           return self._generative_step(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					List[str]="val" )  ->	Dict:




           '''simple docstring'''


           self.step_count += 1
           SCREAMING_SNAKE_CASE	=		{k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
           SCREAMING_SNAKE_CASE	=		losses["""loss"""]
           SCREAMING_SNAKE_CASE	=		{
               k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
           }
           SCREAMING_SNAKE_CASE	=		(
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           SCREAMING_SNAKE_CASE	=		torch.tensor(lowerCamelCase__ ).type_as(lowerCamelCase__ )
           generative_metrics.update({k: v.item() for k, v in losses.items()} )
           losses.update(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
           SCREAMING_SNAKE_CASE	=		self.step_count
           self.metrics[prefix].append(lowerCamelCase__ )  # callback writes this to self.metrics_save_path
           SCREAMING_SNAKE_CASE	=		flatten_list([x["""preds"""] for x in outputs] )
           return {
               "log": all_metrics,
               "preds": preds,
               F"""{prefix}_loss""": loss,
               F"""{prefix}_{self.val_metric}""": metric_tensor,
           }






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           return calculate_rouge(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					dict )  ->	dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		time.time()

           # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
           SCREAMING_SNAKE_CASE	=		self.model.generate(
               batch["""input_ids"""]		,attention_mask=batch["""attention_mask"""]		,use_cache=lowerCamelCase__		,decoder_start_token_id=self.decoder_start_token_id		,num_beams=self.eval_beams		,max_length=self.eval_max_length		,)
           SCREAMING_SNAKE_CASE	=		(time.time() - ta) / batch["""input_ids"""].shape[0]
           SCREAMING_SNAKE_CASE	=		self.ids_to_clean_text(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		self.ids_to_clean_text(batch["""labels"""] )
           SCREAMING_SNAKE_CASE	=		self._step(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		dict(zip(self.loss_names		,lowerCamelCase__ ) )
           SCREAMING_SNAKE_CASE	=		self.calc_generative_metrics(lowerCamelCase__		,lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		np.mean(lmap(lowerCamelCase__		,lowerCamelCase__ ) )
           base_metrics.update(gen_time=lowerCamelCase__		,gen_len=lowerCamelCase__		,preds=lowerCamelCase__		,target=lowerCamelCase__		,**lowerCamelCase__ )
           return base_metrics






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[Any] )  ->	Any:




           '''simple docstring'''


           return self._generative_step(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Union[str, Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           return self.validation_epoch_end(lowerCamelCase__		,prefix="""test""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Tuple )  ->	SeqaSeqDataset:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.n_obs[type_path]
           SCREAMING_SNAKE_CASE	=		self.target_lens[type_path]
           SCREAMING_SNAKE_CASE	=		self.dataset_class(
               self.tokenizer		,type_path=lowerCamelCase__		,n_obs=lowerCamelCase__		,max_target_length=lowerCamelCase__		,**self.dataset_kwargs		,)
           return dataset






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					bool = False )  ->	DataLoader:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dataset(lowerCamelCase__ )

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                 SCREAMING_SNAKE_CASE	=		dataset.make_sortish_sampler(lowerCamelCase__		,distributed=self.hparams.gpus > 1 )
                 return DataLoader(
                     lowerCamelCase__		,batch_size=lowerCamelCase__		,collate_fn=dataset.collate_fn		,shuffle=lowerCamelCase__		,num_workers=self.num_workers		,sampler=lowerCamelCase__		,)

           elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                 SCREAMING_SNAKE_CASE	=		dataset.make_dynamic_sampler(
                     self.hparams.max_tokens_per_batch		,distributed=self.hparams.gpus > 1 )
                 return DataLoader(
                     lowerCamelCase__		,batch_sampler=lowerCamelCase__		,collate_fn=dataset.collate_fn		,num_workers=self.num_workers		,)
           else:
                 return DataLoader(
                     lowerCamelCase__		,batch_size=lowerCamelCase__		,collate_fn=dataset.collate_fn		,shuffle=lowerCamelCase__		,num_workers=self.num_workers		,sampler=lowerCamelCase__		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	DataLoader:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_dataloader("""train"""		,batch_size=self.hparams.train_batch_size		,shuffle=lowerCamelCase__ )
           return dataloader






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	DataLoader:




           '''simple docstring'''


           return self.get_dataloader("""val"""		,batch_size=self.hparams.eval_batch_size )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	DataLoader:




           '''simple docstring'''


           return self.get_dataloader("""test"""		,batch_size=self.hparams.eval_batch_size )






     @staticmethod
     def        SCREAMING_SNAKE_CASE__						(							lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           BaseTransformer.add_model_specific_args(lowerCamelCase__		,lowerCamelCase__ )
           add_generic_args(lowerCamelCase__		,lowerCamelCase__ )
           parser.add_argument(
               """--max_source_length"""		,default=1024		,type=lowerCamelCase__		,help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )		,)
           parser.add_argument(
               """--max_target_length"""		,default=56		,type=lowerCamelCase__		,help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )		,)
           parser.add_argument(
               """--val_max_target_length"""		,default=142		,type=lowerCamelCase__		,help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )		,)
           parser.add_argument(
               """--test_max_target_length"""		,default=142		,type=lowerCamelCase__		,help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )		,)
           parser.add_argument("""--freeze_encoder"""		,action="""store_true""" )
           parser.add_argument("""--freeze_embeds"""		,action="""store_true""" )
           parser.add_argument("""--sortish_sampler"""		,action="""store_true"""		,default=lowerCamelCase__ )
           parser.add_argument("""--overwrite_output_dir"""		,action="""store_true"""		,default=lowerCamelCase__ )
           parser.add_argument("""--max_tokens_per_batch"""		,type=lowerCamelCase__		,default=lowerCamelCase__ )
           parser.add_argument("""--logger_name"""		,type=lowerCamelCase__		,choices=["""default""", """wandb""", """wandb_shared"""]		,default="""default""" )
           parser.add_argument("""--n_train"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--n_val"""		,type=lowerCamelCase__		,default=500		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--n_test"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument(
               """--task"""		,type=lowerCamelCase__		,default="""summarization"""		,required=lowerCamelCase__		,help="""# examples. -1 means use all.""" )
           parser.add_argument("""--label_smoothing"""		,type=lowerCamelCase__		,default=0.0		,required=lowerCamelCase__ )
           parser.add_argument("""--src_lang"""		,type=lowerCamelCase__		,default=""""""		,required=lowerCamelCase__ )
           parser.add_argument("""--tgt_lang"""		,type=lowerCamelCase__		,default=""""""		,required=lowerCamelCase__ )
           parser.add_argument("""--eval_beams"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,required=lowerCamelCase__ )
           parser.add_argument(
               """--val_metric"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,required=lowerCamelCase__		,choices=["""bleu""", """rouge2""", """loss""", None] )
           parser.add_argument("""--eval_max_gen_length"""		,type=lowerCamelCase__		,default=lowerCamelCase__		,help="""never generate more than n tokens""" )
           parser.add_argument("""--save_top_k"""		,type=lowerCamelCase__		,default=1		,required=lowerCamelCase__		,help="""How many checkpoints to save""" )
           parser.add_argument(
               """--early_stopping_patience"""		,type=lowerCamelCase__		,default=-1		,required=lowerCamelCase__		,help=(
                   """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                   """ val_check_interval will effect it."""
               )		,)
           return parser

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[Any]				=    "translation"
     __snake_case					:			Tuple				=    ["loss"]
     __snake_case					:			Union[str, Any]				=    ["bleu"]
     __snake_case					:			str				=    "bleu"






     def __init__(							self		:					str		,lowerCamelCase__		:					Optional[int]		,**lowerCamelCase__		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           super().__init__(lowerCamelCase__		,**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		hparams.src_lang
           SCREAMING_SNAKE_CASE	=		hparams.tgt_lang






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple )  ->	dict:




           '''simple docstring'''


           return calculate_bleu(lowerCamelCase__		,lowerCamelCase__ )



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None			)				->     SummarizationModule:


      '''simple docstring'''


      Path(args.output_dir			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
      check_output_dir(_SCREAMING_SNAKE_CASE				,		expected_items=3			)

      if model is None:
            if "summarization" in args.task:
                  SCREAMING_SNAKE_CASE	=		SummarizationModule(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		TranslationModule(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		Path(args.data_dir			).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir			).startswith("""/tmp"""			)
          or str(args.output_dir			).startswith("""/var"""			)
      ):
            SCREAMING_SNAKE_CASE	=		True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            SCREAMING_SNAKE_CASE	=		os.environ.get("""WANDB_PROJECT"""				,		_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		WandbLogger(name=model.output_dir.name				,		project=_SCREAMING_SNAKE_CASE			)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            SCREAMING_SNAKE_CASE	=		WandbLogger(name=model.output_dir.name				,		project=F"""hf_{dataset}"""			)

      if args.early_stopping_patience >= 0:
            SCREAMING_SNAKE_CASE	=		get_early_stopping_callback(model.val_metric				,		args.early_stopping_patience			)
      else:
            SCREAMING_SNAKE_CASE	=		False

      SCREAMING_SNAKE_CASE	=		args.val_metric == """loss"""
      SCREAMING_SNAKE_CASE	=		generic_train(
          _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		logging_callback=SeqaSeqLoggingCallback()				,		checkpoint_callback=get_checkpoint_callback(
              args.output_dir				,		model.val_metric				,		args.save_top_k				,		_SCREAMING_SNAKE_CASE			)				,		early_stopping_callback=_SCREAMING_SNAKE_CASE				,		logger=_SCREAMING_SNAKE_CASE				,		)
      pickle_save(model.hparams				,		model.output_dir / """hparams.pkl"""			)
      if not args.do_predict:
            return model

      SCREAMING_SNAKE_CASE	=		""""""
      SCREAMING_SNAKE_CASE	=		sorted(glob.glob(os.path.join(args.output_dir				,		"""*.ckpt"""			)				,		recursive=_SCREAMING_SNAKE_CASE			)			)
      if checkpoints:
            SCREAMING_SNAKE_CASE	=		checkpoints[-1]
            SCREAMING_SNAKE_CASE	=		checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams			)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = argparse.ArgumentParser()
      SCREAMING_SNAKE_CASE_		     = pl.Trainer.add_argparse_args(parser)
      SCREAMING_SNAKE_CASE_		     = SummarizationModule.add_model_specific_args(parser, os.getcwd())

      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      main(args)


