
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


UpperCamelCase_   	=	logging.get_logger(__name__)

# General docstring
UpperCamelCase_   	=	"PoolFormerConfig"

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

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

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






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     float = 0.0			,							UpperCamelCase:     bool = False  ):






	"""simple docstring"""


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

class       a		(   nn.Module					):







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[float] = None ):
								"""simple docstring"""







								super().__init__()
								__lowerCAmelCase         =							drop_prob







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  torch.Tensor ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







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

class       a		(   nn.Module					):







							def __init__(						self  :  Tuple      ,				snake_case__  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  List[str]=None ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							nn.Convad(snake_case__      ,				snake_case__      ,				kernel_size=snake_case__      ,				stride=snake_case__      ,				padding=snake_case__ )
								__lowerCAmelCase         =							norm_layer(snake_case__ ) if norm_layer else nn.Identity()







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.projection(snake_case__ )
								__lowerCAmelCase         =							self.norm(snake_case__ )
								return embeddings

class       a		(   nn.GroupNorm					):







							def __init__(						self  :  str      ,				snake_case__  :  str      ,				**snake_case__  :  List[str] ):
								"""simple docstring"""







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

class       a		(   nn.Module					):







							def __init__(						self  :  Any      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								return self.pool(snake_case__ ) - hidden_states

class       a		(   nn.Module					):







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Any ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.conva(snake_case__ )
								__lowerCAmelCase         =							self.act_fn(snake_case__ )
								__lowerCAmelCase         =							self.drop(snake_case__ )
								__lowerCAmelCase         =							self.conva(snake_case__ )
								__lowerCAmelCase         =							self.drop(snake_case__ )

								return hidden_states

class       a		(   nn.Module					):







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  str      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







								super().__init__()
								__lowerCAmelCase         =							PoolFormerPooling(snake_case__ )
								__lowerCAmelCase         =							PoolFormerOutput(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							PoolFormerGroupNorm(snake_case__ )
								__lowerCAmelCase         =							PoolFormerGroupNorm(snake_case__ )

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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







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

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

									__lowerCAmelCase         =							(output,) + outputs
									return outputs

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

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

									__lowerCAmelCase         =							(output,) + outputs
									return outputs

class       a		(   nn.Module					):







							def __init__(						self  :  Any      ,				snake_case__  :  int ):
								"""simple docstring"""







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

								# patch embeddings
								__lowerCAmelCase         =							[]
								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]      ,				) )
								__lowerCAmelCase         =							nn.ModuleList(snake_case__ )

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

								__lowerCAmelCase         =							nn.ModuleList(snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Dict      ,				snake_case__  :  List[Any]=False      ,				snake_case__  :  Dict=True ):
								"""simple docstring"""







								__lowerCAmelCase         =							() if output_hidden_states else None

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

									if output_hidden_states:
										__lowerCAmelCase         =							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=snake_case__      ,				hidden_states=snake_case__ )

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  PoolFormerConfig
							lowercase_		:						int   							=  'poolformer'
							lowercase_		:						Optional[Any]   							=  'pixel_values'
							lowercase_		:						Dict   							=  True







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Any ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  List[str]      ,				snake_case__  :  str=False ):
								"""simple docstring"""







								if isinstance(snake_case__      ,				snake_case__ ):
									__lowerCAmelCase         =							value
UpperCamelCase_   	=	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"

UpperCamelCase_   	=	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.'      ,	__UpperCAmelCase      ,	)
class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  str      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								super().__init__(snake_case__ )
								__lowerCAmelCase         =							config

								__lowerCAmelCase         =							PoolFormerEncoder(snake_case__ )

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







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.embeddings.patch_embeddings







							@add_start_docstrings_to_model_forward(snake_case__ )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC      ,				output_type=snake_case__      ,				config_class=_CONFIG_FOR_DOC      ,				modality="vision"      ,				expected_output=_EXPECTED_OUTPUT_SHAPE      ,				)
							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Optional[torch.FloatTensor] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[bool] = None      ,				):
								"""simple docstring"""







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

								__lowerCAmelCase         =							self.encoder(
								    snake_case__      ,				output_hidden_states=snake_case__      ,				return_dict=snake_case__      ,				)
								__lowerCAmelCase         =							encoder_outputs[0]

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

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

class       a		(   nn.Module					):







							def __init__(						self  :  List[Any]      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.dense(snake_case__ )
								return output

@add_start_docstrings(
    '\n    PoolFormer Model transformer with an image classification head on top\n    '      ,	__UpperCAmelCase      ,	)
class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								super().__init__(snake_case__ )
								__lowerCAmelCase         =							config.num_labels
								__lowerCAmelCase         =							PoolFormerModel(snake_case__ )

								# Final norm
								__lowerCAmelCase         =							PoolFormerGroupNorm(config.hidden_sizes[-1] )
								# Classifier head
								__lowerCAmelCase         =							(
								    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(snake_case__ )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,				output_type=snake_case__      ,				config_class=_CONFIG_FOR_DOC      ,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,				)
							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Optional[torch.FloatTensor] = None      ,				snake_case__  :  Optional[torch.LongTensor] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[bool] = None      ,				):
								"""simple docstring"""







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

								__lowerCAmelCase         =							self.poolformer(
								    snake_case__      ,				output_hidden_states=snake_case__      ,				return_dict=snake_case__      ,				)

								__lowerCAmelCase         =							outputs[0]

								__lowerCAmelCase         =							self.classifier(self.norm(snake_case__ ).mean([-2, -1] ) )

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

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

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

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








import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class       a		(   __UpperCAmelCase					):
							lowercase_		:						BigBirdConfig
							lowercase_		:						jnp.dtype   							=  jnp.floataa
							lowercase_		:						bool   							=  True







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								super().setup()
								__lowerCAmelCase         =							nn.Dense(5      ,				dtype=self.dtype )







							def __call__(						self  :  Optional[Any]      ,				*snake_case__  :  List[str]      ,				**snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							super().__call__(*snake_case__      ,				**snake_case__ )
								__lowerCAmelCase         =							self.cls(outputs[2] )
								return outputs[:2] + (cls_out,)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  FlaxBigBirdForNaturalQuestionsModule






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int  ):






	"""simple docstring"""


	def cross_entropy(UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]=None  ):
		__lowerCAmelCase         =							logits.shape[-1]
		__lowerCAmelCase         =							(labels[..., None] == jnp.arange(UpperCamelCase  )[None]).astype("f4"  )
		__lowerCAmelCase         =							jax.nn.log_softmax(UpperCamelCase			,							axis=-1  )
		__lowerCAmelCase         =							-jnp.sum(labels * logits			,							axis=-1  )
		if reduction is not None:
			__lowerCAmelCase         =							reduction(UpperCamelCase  )
		return loss

	__lowerCAmelCase         =							partial(UpperCamelCase			,							reduction=jnp.mean  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							cross_entropy(UpperCamelCase			,							UpperCamelCase  )
	return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class       a		:
							lowercase_		:						str   							=  "google/bigbird-roberta-base"
							lowercase_		:						int   							=  3_000
							lowercase_		:						int   							=  10_500

							lowercase_		:						int   							=  128
							lowercase_		:						int   							=  3

							lowercase_		:						int   							=  1
							lowercase_		:						int   							=  5

							# tx_args
							lowercase_		:						float   							=  3e-5
							lowercase_		:						float   							=  0.0
							lowercase_		:						int   							=  20_000
							lowercase_		:						float   							=  0.0095

							lowercase_		:						str   							=  "bigbird-roberta-natural-questions"
							lowercase_		:						str   							=  "training-expt"
							lowercase_		:						str   							=  "data/nq-training.jsonl"
							lowercase_		:						str   							=  "data/nq-validation.jsonl"







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								os.makedirs(self.base_dir      ,				exist_ok=snake_case__ )
								__lowerCAmelCase         =							os.path.join(self.base_dir      ,				self.save_dir )
								__lowerCAmelCase         =							self.batch_size_per_device * jax.device_count()

@dataclass
class       a		:
							lowercase_		:						int
							lowercase_		:						int   							=  4_096  # no dynamic padding on TPUs







							def __call__(						self  :  List[Any]      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.collate_fn(snake_case__ )
								__lowerCAmelCase         =							jax.tree_util.tree_map(snake_case__      ,				snake_case__ )
								return batch







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.fetch_inputs(features["input_ids"] )
								__lowerCAmelCase         =							{
								    "input_ids": jnp.array(snake_case__      ,				dtype=jnp.intaa ),
								    "attention_mask": jnp.array(snake_case__      ,				dtype=jnp.intaa ),
								    "start_labels": jnp.array(features["start_token"]      ,				dtype=jnp.intaa ),
								    "end_labels": jnp.array(features["end_token"]      ,				dtype=jnp.intaa ),
								    "pooled_labels": jnp.array(features["category"]      ,				dtype=jnp.intaa ),
								}
								return batch







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  list ):
								"""simple docstring"""







								__lowerCAmelCase         =							[self._fetch_inputs(snake_case__ ) for ids in input_ids]
								return zip(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  list ):
								"""simple docstring"""







								__lowerCAmelCase         =							[1 for _ in range(len(snake_case__ ) )]
								while len(snake_case__ ) < self.max_length:
									input_ids.append(self.pad_id )
									attention_mask.append(0 )
								return input_ids, attention_mask






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[Any]=None  ):






	"""simple docstring"""


	if seed is not None:
		__lowerCAmelCase         =							dataset.shuffle(seed=UpperCamelCase  )
	for i in range(len(UpperCamelCase  ) // batch_size  ):
		__lowerCAmelCase         =							dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(UpperCamelCase  )






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	def loss_fn(UpperCamelCase:     Dict  ):
		__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

		__lowerCAmelCase         =							state.apply_fn(**UpperCamelCase			,							params=UpperCamelCase			,							dropout_rng=UpperCamelCase			,							train=UpperCamelCase  )
		__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							outputs

		return state.loss_fn(
		    UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							)

	__lowerCAmelCase   ,						__lowerCAmelCase         =							jax.random.split(UpperCamelCase  )
	__lowerCAmelCase         =							jax.value_and_grad(UpperCamelCase  )
	__lowerCAmelCase   ,						__lowerCAmelCase         =							grad_fn(state.params  )
	__lowerCAmelCase         =							jax.lax.pmean({"loss": loss}			,							axis_name="batch"  )
	__lowerCAmelCase         =							jax.lax.pmean(UpperCamelCase			,							"batch"  )

	__lowerCAmelCase         =							state.apply_gradients(grads=UpperCamelCase  )
	return state, metrics, new_drp_rng






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

	__lowerCAmelCase         =							state.apply_fn(**UpperCamelCase			,							params=state.params			,							train=UpperCamelCase  )
	__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							outputs

	__lowerCAmelCase         =							state.loss_fn(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							jax.lax.pmean({"loss": loss}			,							axis_name="batch"  )
	return metrics

class       a		(   train_state.TrainState					):
							lowercase_		:						Callable   							=  struct.field(pytree_node=__UpperCAmelCase					)

@dataclass
class       a		:
							lowercase_		:						Args
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						wandb
							lowercase_		:						Callable   							=  None







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  int      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  str=None ):
								"""simple docstring"""







								__lowerCAmelCase         =							model.params
								__lowerCAmelCase         =							TrainState.create(
								    apply_fn=model.__call__      ,				params=snake_case__      ,				tx=snake_case__      ,				loss_fn=snake_case__      ,				)
								if ckpt_dir is not None:
									__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							restore_checkpoint(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							{
									    "lr": args.lr,
									    "init_lr": args.init_lr,
									    "warmup_steps": args.warmup_steps,
									    "num_train_steps": num_train_steps,
									    "weight_decay": args.weight_decay,
									}
									__lowerCAmelCase   ,						__lowerCAmelCase         =							build_tx(**snake_case__ )
									__lowerCAmelCase         =							train_state.TrainState(
									    step=snake_case__      ,				apply_fn=model.__call__      ,				params=snake_case__      ,				tx=snake_case__      ,				opt_state=snake_case__      ,				)
									__lowerCAmelCase         =							args
									__lowerCAmelCase         =							data_collator
									__lowerCAmelCase         =							lr
									__lowerCAmelCase         =							params
								__lowerCAmelCase         =							jax_utils.replicate(snake_case__ )
								return state







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.args
								__lowerCAmelCase         =							len(snake_case__ ) // args.batch_size

								__lowerCAmelCase         =							jax.random.PRNGKey(0 )
								__lowerCAmelCase         =							jax.random.split(snake_case__      ,				jax.device_count() )
								for epoch in range(args.max_epochs ):
									__lowerCAmelCase         =							jnp.array(0      ,				dtype=jnp.floataa )
									__lowerCAmelCase         =							get_batched_dataset(snake_case__      ,				args.batch_size      ,				seed=snake_case__ )
									__lowerCAmelCase         =							0
									for batch in tqdm(snake_case__      ,				total=snake_case__      ,				desc=F"Running EPOCH-{epoch}" ):
										__lowerCAmelCase         =							self.data_collator(snake_case__ )
										__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							self.train_step_fn(snake_case__      ,				snake_case__      ,				**snake_case__ )
										running_loss += jax_utils.unreplicate(metrics["loss"] )
										i += 1
										if i % args.logging_steps == 0:
											__lowerCAmelCase         =							jax_utils.unreplicate(state.step )
											__lowerCAmelCase         =							running_loss.item() / i
											__lowerCAmelCase         =							self.scheduler_fn(state_step - 1 )

											__lowerCAmelCase         =							self.evaluate(snake_case__      ,				snake_case__ )
											__lowerCAmelCase         =							{
											    "step": state_step.item(),
											    "eval_loss": eval_loss.item(),
											    "tr_loss": tr_loss,
											    "lr": lr.item(),
											}
											tqdm.write(str(snake_case__ ) )
											self.logger.log(snake_case__      ,				commit=snake_case__ )

										if i % args.save_steps == 0:
											self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"      ,				state=snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							get_batched_dataset(snake_case__      ,				self.args.batch_size )
								__lowerCAmelCase         =							len(snake_case__ ) // self.args.batch_size
								__lowerCAmelCase         =							jnp.array(0      ,				dtype=jnp.floataa )
								__lowerCAmelCase         =							0
								for batch in tqdm(snake_case__      ,				total=snake_case__      ,				desc="Evaluating ... " ):
									__lowerCAmelCase         =							self.data_collator(snake_case__ )
									__lowerCAmelCase         =							self.val_step_fn(snake_case__      ,				**snake_case__ )
									running_loss += jax_utils.unreplicate(metrics["loss"] )
									i += 1
								return running_loss / i







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							jax_utils.unreplicate(snake_case__ )
								print(F"SAVING CHECKPOINT IN {save_dir}"      ,				end=" ... " )
								self.model_save_fn(snake_case__      ,				params=state.params )
								with open(os.path.join(snake_case__      ,				"opt_state.msgpack" )      ,				"wb" ) as f:
									f.write(to_bytes(state.opt_state ) )
								joblib.dump(self.args      ,				os.path.join(snake_case__      ,				"args.joblib" ) )
								joblib.dump(self.data_collator      ,				os.path.join(snake_case__      ,				"data_collator.joblib" ) )
								with open(os.path.join(snake_case__      ,				"training_state.json" )      ,				"w" ) as f:
									json.dump({"step": state.step.item()}      ,				snake_case__ )
								print("DONE" )






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	print(F"RESTORING CHECKPOINT FROM {save_dir}"			,							end=" ... "  )
	with open(os.path.join(UpperCamelCase			,							"flax_model.msgpack"  )			,							"rb"  ) as f:
		__lowerCAmelCase         =							from_bytes(state.params			,							f.read()  )

	with open(os.path.join(UpperCamelCase			,							"opt_state.msgpack"  )			,							"rb"  ) as f:
		__lowerCAmelCase         =							from_bytes(state.opt_state			,							f.read()  )

	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"args.joblib"  )  )
	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"data_collator.joblib"  )  )

	with open(os.path.join(UpperCamelCase			,							"training_state.json"  )			,							"r"  ) as f:
		__lowerCAmelCase         =							json.load(UpperCamelCase  )
	__lowerCAmelCase         =							training_state["step"]

	print("DONE"  )
	return params, opt_state, step, args, data_collator






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	__lowerCAmelCase         =							num_train_steps - warmup_steps
	__lowerCAmelCase         =							optax.linear_schedule(init_value=UpperCamelCase			,							end_value=UpperCamelCase			,							transition_steps=UpperCamelCase  )
	__lowerCAmelCase         =							optax.linear_schedule(init_value=UpperCamelCase			,							end_value=1e-7			,							transition_steps=UpperCamelCase  )
	__lowerCAmelCase         =							optax.join_schedules(schedules=[warmup_fn, decay_fn]			,							boundaries=[warmup_steps]  )
	return lr







def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     str			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


	def weight_decay_mask(UpperCamelCase:     int  ):
		__lowerCAmelCase         =							traverse_util.flatten_dict(UpperCamelCase  )
		__lowerCAmelCase         =							{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
		return traverse_util.unflatten_dict(UpperCamelCase  )

	__lowerCAmelCase         =							scheduler_fn(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	__lowerCAmelCase         =							optax.adamw(learning_rate=UpperCamelCase			,							weight_decay=UpperCamelCase			,							mask=UpperCamelCase  )
	return tx, lr






