




'''simple docstring'''



from typing import Optional, Tuple

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

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


UpperCAmelCase_    :					Optional[int]          =						logging.get_logger(__name__)





class       lowerCAmelCase				(	nn.Module):
       __lowercase					: HybridCLIPConfig
       __lowercase					: jnp.dtype	    =     jnp.floataa


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					self.config.text_config
            __snake_case         =					self.config.vision_config

            __snake_case         =					self.config.projection_dim
            __snake_case         =					text_config.hidden_size
            __snake_case         =					vision_config.hidden_size

            __snake_case         =					FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
            __snake_case         =					FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__      ,					__SCREAMING_SNAKE_CASE					).module_class

            __snake_case         =					text_module(__SCREAMING_SNAKE_CASE      ,					dtype=self.dtype					)
            __snake_case         =					vision_module(__SCREAMING_SNAKE_CASE      ,					dtype=self.dtype					)

            __snake_case         =					nn.Dense(
                self.projection_dim      ,					dtype=self.dtype      ,					kernel_init=jax.nn.initializers.normal(0.02					)      ,					use_bias=__SCREAMING_SNAKE_CASE      ,					)
            __snake_case         =					nn.Dense(
                self.projection_dim      ,					dtype=self.dtype      ,					kernel_init=jax.nn.initializers.normal(0.02					)      ,					use_bias=__SCREAMING_SNAKE_CASE      ,					)
            __snake_case         =					self.param('''logit_scale'''      ,					jax.nn.initializers.ones      ,					[]					)




       def __call__(				self      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					return_dict if return_dict is not None else self.config.return_dict

            __snake_case         =					self.vision_model(
                pixel_values=__SCREAMING_SNAKE_CASE      ,					deterministic=__SCREAMING_SNAKE_CASE      ,					output_attentions=__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE      ,					return_dict=__SCREAMING_SNAKE_CASE      ,					)

            __snake_case         =					self.text_model(
                input_ids=__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					position_ids=__SCREAMING_SNAKE_CASE      ,					deterministic=__SCREAMING_SNAKE_CASE      ,					output_attentions=__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE      ,					return_dict=__SCREAMING_SNAKE_CASE      ,					)

            __snake_case         =					vision_outputs[1]
            __snake_case         =					self.visual_projection(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					text_outputs[1]
            __snake_case         =					self.text_projection(__SCREAMING_SNAKE_CASE					)

            # normalized features
            __snake_case         =					image_embeds / jnp.linalg.norm(__SCREAMING_SNAKE_CASE      ,					axis=-1      ,					keepdims=__SCREAMING_SNAKE_CASE					)
            __snake_case         =					text_embeds / jnp.linalg.norm(__SCREAMING_SNAKE_CASE      ,					axis=-1      ,					keepdims=__SCREAMING_SNAKE_CASE					)

            # cosine similarity as logits
            __snake_case         =					jnp.exp(self.logit_scale					)
            __snake_case         =					jnp.matmul(__SCREAMING_SNAKE_CASE      ,					image_embeds.T					) * logit_scale
            __snake_case         =					logits_per_text.T

            if not return_dict:
                 return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

            return FlaxCLIPOutput(
                logits_per_image=__SCREAMING_SNAKE_CASE      ,					logits_per_text=__SCREAMING_SNAKE_CASE      ,					text_embeds=__SCREAMING_SNAKE_CASE      ,					image_embeds=__SCREAMING_SNAKE_CASE      ,					text_model_output=__SCREAMING_SNAKE_CASE      ,					vision_model_output=__SCREAMING_SNAKE_CASE      ,					)





class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Dict	    =     HybridCLIPConfig
       __lowercase					: str	    =     FlaxHybridCLIPModule


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 0      ,					__SCREAMING_SNAKE_CASE = jnp.floataa      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Dict:



            '''simple docstring'''




            if input_shape is None:
                 __snake_case         =					((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

            __snake_case         =					self.module_class(config=__SCREAMING_SNAKE_CASE      ,					dtype=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)
            super().__init__(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					input_shape=__SCREAMING_SNAKE_CASE      ,					seed=__SCREAMING_SNAKE_CASE      ,					dtype=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = None					) ->					FrozenDict:



            '''simple docstring'''




            __snake_case         =					jnp.zeros(input_shape[0]      ,					dtype='''i4'''					)
            __snake_case         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE					).shape[-1]					)      ,					input_shape[0]					)
            __snake_case         =					jnp.ones_like(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					jnp.ones_like(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					jax.random.normal(__SCREAMING_SNAKE_CASE      ,					input_shape[1]					)

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

            return self.module.init(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)["params"]


       def __call__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = False      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					output_attentions if output_attentions is not None else self.config.output_attentions
            __snake_case         =					(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __snake_case         =					return_dict if return_dict is not None else self.config.return_dict

            if position_ids is None:
                 __snake_case         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE					).shape[-1]					)      ,					input_ids.shape					)

            if token_type_ids is None:
                 __snake_case         =					jnp.zeros_like(__SCREAMING_SNAKE_CASE					)

            if attention_mask is None:
                 __snake_case         =					jnp.ones_like(__SCREAMING_SNAKE_CASE					)

            # Handle any PRNG if needed
            __snake_case         =					{}
            if dropout_rng is not None:
                 __snake_case         =					dropout_rng

            return self.module.apply(
                {'''params''': params or self.params}      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype=jnp.floataa					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					not train      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					rngs=__SCREAMING_SNAKE_CASE      ,					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE=False      ,					) ->					Any:



            '''simple docstring'''




            if position_ids is None:
                 __snake_case         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE					).shape[-1]					)      ,					input_ids.shape					)

            if token_type_ids is None:
                 __snake_case         =					jnp.zeros_like(__SCREAMING_SNAKE_CASE					)

            if attention_mask is None:
                 __snake_case         =					jnp.ones_like(__SCREAMING_SNAKE_CASE					)

            # Handle any PRNG if needed
            __snake_case         =					{}
            if dropout_rng is not None:
                 __snake_case         =					dropout_rng

            def _get_features(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					module.text_model(
                     input_ids=__SCREAMING_SNAKE_CASE      ,					attention_mask=__SCREAMING_SNAKE_CASE      ,					position_ids=__SCREAMING_SNAKE_CASE      ,					token_type_ids=__SCREAMING_SNAKE_CASE      ,					deterministic=__SCREAMING_SNAKE_CASE      ,					)
                 __snake_case         =					text_outputs[1]
                 __snake_case         =					module.text_projection(__SCREAMING_SNAKE_CASE					)
                 return text_features

            return self.module.apply(
                {'''params''': params or self.params}      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype='''i4'''					)      ,					not train      ,					method=_get_features      ,					rngs=__SCREAMING_SNAKE_CASE      ,					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE=False					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					{}
            if dropout_rng is not None:
                 __snake_case         =					dropout_rng

            def _get_features(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					module.vision_model(pixel_values=__SCREAMING_SNAKE_CASE      ,					deterministic=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					vision_outputs[1]  # pooled_output
                 __snake_case         =					module.visual_projection(__SCREAMING_SNAKE_CASE					)
                 return image_features

            return self.module.apply(
                {'''params''': params or self.params}      ,					jnp.array(__SCREAMING_SNAKE_CASE      ,					dtype=jnp.floataa					)      ,					not train      ,					method=_get_features      ,					rngs=__SCREAMING_SNAKE_CASE      ,					)




       @classmethod
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					FlaxPreTrainedModel:



            '''simple docstring'''




            __snake_case         =					{
                argument[len('''text_'''					) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''					)
            }

            __snake_case         =					{
                argument[len('''vision_'''					) :]: value for argument, value in kwargs.items() if argument.startswith('''vision_'''					)
            }

            # remove text, vision kwargs from kwargs
            for key in kwargs_text.keys():
                 del kwargs["text_" + key]
            for key in kwargs_vision.keys():
                 del kwargs["vision_" + key]

            # Load and initialize the text and vision model
            __snake_case         =					kwargs_text.pop('''model'''      ,					__SCREAMING_SNAKE_CASE					)
            if text_model is None:
                 assert (
                     text_model_name_or_path is not None
                 ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
                 from transformers import FlaxAutoModel

                 if "config" not in kwargs_text:
                      from transformers import AutoConfig

                      __snake_case         =					AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE					)
                      __snake_case         =					text_config

                 __snake_case         =					FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            __snake_case         =					kwargs_vision.pop('''model'''      ,					__SCREAMING_SNAKE_CASE					)
            if vision_model is None:
                 assert (
                     vision_model_name_or_path is not None
                 ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
                 from transformers import FlaxAutoModel

                 if "config" not in kwargs_vision:
                      from transformers import AutoConfig

                      __snake_case         =					AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE					)
                      __snake_case         =					vision_config

                 __snake_case         =					FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            # instantiate config with corresponding kwargs
            __snake_case         =					kwargs.pop('''dtype'''      ,					jnp.floataa					)
            __snake_case         =					HybridCLIPConfig.from_text_vision_configs(text_model.config      ,					vision_model.config      ,					**__SCREAMING_SNAKE_CASE					)

            # init model
            __snake_case         =					cls(__SCREAMING_SNAKE_CASE      ,					*__SCREAMING_SNAKE_CASE      ,					dtype=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            if vision_config.model_type == "clip":
                 __snake_case         =					vision_model.params['''vision_model''']
                 __snake_case         =					vision_model.params['''visual_projection''']['''kernel''']
            else:
                 __snake_case         =					vision_model.params

            __snake_case         =					text_model.params

            return model












'''simple docstring'''



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase_    :					Tuple					      =						logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase_    :					List[str]					      =						{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase_    :					Any					      =						{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase_    :					Dict					      =						sorted(arg_to_scheduler.keys())
UpperCAmelCase_    :					List[Any]					      =						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class       lowerCAmelCase				(	pl.LightningModule):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE="base"      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Optional[int]:



            '''simple docstring'''




            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					0
            __snake_case         =					Path(self.hparams.output_dir					)
            __snake_case         =					self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __snake_case         =					AutoConfig.from_pretrained(
                     self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path      ,					**({'''num_labels''': num_labels} if num_labels is not None else {})      ,					cache_dir=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					config

            __snake_case         =					('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
            for p in extra_model_params:
                 if getattr(self.hparams      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      assert hasattr(self.config      ,					__SCREAMING_SNAKE_CASE					), F'''model config doesn\'t have a `{p}` attribute'''
                      setattr(self.config      ,					__SCREAMING_SNAKE_CASE      ,					getattr(self.hparams      ,					__SCREAMING_SNAKE_CASE					)					)

            if tokenizer is None:
                 __snake_case         =					AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,					cache_dir=__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					tokenizer
            __snake_case         =					MODEL_MODES[mode]
            if model is None:
                 __snake_case         =					self.model_type.from_pretrained(
                     self.hparams.model_name_or_path      ,					from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path					)      ,					config=self.config      ,					cache_dir=__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					model


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					arg_to_scheduler[self.hparams.lr_scheduler]
            __snake_case         =					get_schedule_func(
                self.opt      ,					num_warmup_steps=self.hparams.warmup_steps      ,					num_training_steps=self.total_steps()					)
            __snake_case         =					{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
            return scheduler


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.model
            __snake_case         =					['''bias''', '''LayerNorm.weight''']
            __snake_case         =					[
                {
                    '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                    '''weight_decay''': self.hparams.weight_decay,
                },
                {
                    '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
                    '''weight_decay''': 0.0,
                },
            ]
            if self.hparams.adafactor:
                 __snake_case         =					Adafactor(
                     __SCREAMING_SNAKE_CASE      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__SCREAMING_SNAKE_CASE      ,					relative_step=__SCREAMING_SNAKE_CASE					)

            else:
                 __snake_case         =					AdamW(
                     __SCREAMING_SNAKE_CASE      ,					lr=self.hparams.learning_rate      ,					eps=self.hparams.adam_epsilon					)
            __snake_case         =					optimizer

            __snake_case         =					self.get_lr_scheduler()

            return [optimizer], [scheduler]


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            return self.validation_step(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            return self.validation_end(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					max(1      ,					self.hparams.gpus					)  # TODO: consider num_tpu_cores
            __snake_case         =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            if mode == "test":
                 __snake_case         =					len(self.test_dataloader().dataset					)
            else:
                 __snake_case         =					self.get_dataloader('''train'''      ,					self.hparams.train_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					len(self.train_dataloader().dataset					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = False					) ->					str:



            '''simple docstring'''




            raise NotImplementedError('''You must implement this for your task'''					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            return self.train_loader


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            return self.get_dataloader('''dev'''      ,					self.hparams.eval_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            return self.get_dataloader('''test'''      ,					self.hparams.eval_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            return os.path.join(
                self.hparams.data_dir      ,					'''cached_{}_{}_{}'''.format(
                    __SCREAMING_SNAKE_CASE      ,					list(filter(__SCREAMING_SNAKE_CASE      ,					self.hparams.model_name_or_path.split('''/'''					)					)					).pop()      ,					str(self.hparams.max_seq_length					)      ,					)      ,					)


       @pl.utilities.rank_zero_only
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					self.output_dir.joinpath('''best_tfmr'''					)
            __snake_case         =					self.step_count
            self.model.save_pretrained(__SCREAMING_SNAKE_CASE					)
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)




       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            parser.add_argument(
                '''--model_name_or_path'''      ,					default=__SCREAMING_SNAKE_CASE      ,					type=__SCREAMING_SNAKE_CASE      ,					required=__SCREAMING_SNAKE_CASE      ,					help='''Path to pretrained model or model identifier from huggingface.co/models'''      ,					)
            parser.add_argument(
                '''--config_name'''      ,					default=''''''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Pretrained config name or path if not the same as model_name'''					)
            parser.add_argument(
                '''--tokenizer_name'''      ,					default=__SCREAMING_SNAKE_CASE      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Pretrained tokenizer name or path if not the same as model_name'''      ,					)
            parser.add_argument(
                '''--cache_dir'''      ,					default=''''''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''      ,					)
            parser.add_argument(
                '''--encoder_layerdrop'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Encoder layer dropout probability (Optional). Goes into model.config'''      ,					)
            parser.add_argument(
                '''--decoder_layerdrop'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Decoder layer dropout probability (Optional). Goes into model.config'''      ,					)
            parser.add_argument(
                '''--dropout'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Dropout probability (Optional). Goes into model.config'''      ,					)
            parser.add_argument(
                '''--attention_dropout'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Attention dropout probability (Optional). Goes into model.config'''      ,					)
            parser.add_argument('''--learning_rate'''      ,					default=5E-5      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''The initial learning rate for Adam.'''					)
            parser.add_argument(
                '''--lr_scheduler'''      ,					default='''linear'''      ,					choices=__SCREAMING_SNAKE_CASE      ,					metavar=__SCREAMING_SNAKE_CASE      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Learning rate scheduler'''      ,					)
            parser.add_argument('''--weight_decay'''      ,					default=0.0      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Weight decay if we apply some.'''					)
            parser.add_argument('''--adam_epsilon'''      ,					default=1E-8      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Epsilon for Adam optimizer.'''					)
            parser.add_argument('''--warmup_steps'''      ,					default=0      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Linear warmup over warmup_steps.'''					)
            parser.add_argument('''--num_workers'''      ,					default=4      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''kwarg passed to DataLoader'''					)
            parser.add_argument('''--num_train_epochs'''      ,					dest='''max_epochs'''      ,					default=3      ,					type=__SCREAMING_SNAKE_CASE					)
            parser.add_argument('''--train_batch_size'''      ,					default=32      ,					type=__SCREAMING_SNAKE_CASE					)
            parser.add_argument('''--eval_batch_size'''      ,					default=32      ,					type=__SCREAMING_SNAKE_CASE					)
            parser.add_argument('''--adafactor'''      ,					action='''store_true'''					)





class       lowerCAmelCase				(	pl.Callback):


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					trainer.lr_schedulers[0]['''scheduler''']
            __snake_case         =					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
            pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            rank_zero_info('''***** Validation results *****'''					)
            __snake_case         =					trainer.callback_metrics
            # Log results
            for key in sorted(__SCREAMING_SNAKE_CASE					):
                 if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            rank_zero_info('''***** Test results *****'''					)
            __snake_case         =					trainer.callback_metrics
            # Log and save results to file
            __snake_case         =					os.path.join(pl_module.hparams.output_dir      ,					'''test_results.txt'''					)
            with open(__SCREAMING_SNAKE_CASE      ,					'''w'''					) as writer:
                 for key in sorted(__SCREAMING_SNAKE_CASE					):
                      if key not in ["log", "progress_bar"]:
                           rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)
                           writer.write('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[Any]		)->				None:




     '''simple docstring'''





     parser.add_argument(
         '''--output_dir'''				,		default=_lowerCamelCase				,		type=_lowerCamelCase				,		required=_lowerCamelCase				,		help='''The output directory where the model predictions and checkpoints will be written.'''				,		)
     parser.add_argument(
         '''--fp16'''				,		action='''store_true'''				,		help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,		)

     parser.add_argument(
         '''--fp16_opt_level'''				,		type=_lowerCamelCase				,		default='''O2'''				,		help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )				,		)
     parser.add_argument('''--n_tpu_cores'''				,		dest='''tpu_cores'''				,		type=_lowerCamelCase		)
     parser.add_argument('''--max_grad_norm'''				,		dest='''gradient_clip_val'''				,		default=1.0				,		type=_lowerCamelCase				,		help='''Max gradient norm'''		)
     parser.add_argument('''--do_train'''				,		action='''store_true'''				,		help='''Whether to run training.'''		)
     parser.add_argument('''--do_predict'''				,		action='''store_true'''				,		help='''Whether to run predictions on the test set.'''		)
     parser.add_argument(
         '''--gradient_accumulation_steps'''				,		dest='''accumulate_grad_batches'''				,		type=_lowerCamelCase				,		default=1				,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,		)
     parser.add_argument('''--seed'''				,		type=_lowerCamelCase				,		default=42				,		help='''random seed for initialization'''		)
     parser.add_argument(
         '''--data_dir'''				,		default=_lowerCamelCase				,		type=_lowerCamelCase				,		required=_lowerCamelCase				,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,		)







def      _UpperCamelCase	(_lowerCamelCase							:       BaseTransformer				,		_lowerCamelCase							:       argparse.Namespace				,		_lowerCamelCase							:       Any=None				,		_lowerCamelCase							:       Union[str, Any]=True				,		_lowerCamelCase							:       Union[str, Any]=[]				,		_lowerCamelCase							:       int=None				,		_lowerCamelCase							:       Any=None				,		**_lowerCamelCase							:       Tuple				,		)->				Optional[Any]:




     '''simple docstring'''





     pl.seed_everything(args.seed		)

     # init model
     __snake_case         =					Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=_lowerCamelCase		)

     # add custom checkpoints
     if checkpoint_callback is None:
          __snake_case         =					pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir				,		prefix='''checkpoint'''				,		monitor='''val_loss'''				,		mode='''min'''				,		save_top_k=1		)
     if early_stopping_callback:
          extra_callbacks.append(_lowerCamelCase		)
     if logging_callback is None:
          __snake_case         =					LoggingCallback()

     __snake_case         =					{}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          __snake_case         =					16
          __snake_case         =					args.fpaa_opt_level

     if args.gpus > 1:
          __snake_case         =					'''ddp'''

     __snake_case         =					args.accumulate_grad_batches
     __snake_case         =					extra_train_kwargs.get('''accelerator'''				,		_lowerCamelCase		)
     __snake_case         =					extra_train_kwargs.get('''profiler'''				,		_lowerCamelCase		)

     __snake_case         =					pl.Trainer.from_argparse_args(
         _lowerCamelCase				,		weights_summary=_lowerCamelCase				,		callbacks=[logging_callback] + extra_callbacks				,		logger=_lowerCamelCase				,		checkpoint_callback=_lowerCamelCase				,		**_lowerCamelCase				,		)

     if args.do_train:
          trainer.fit(_lowerCamelCase		)

     return trainer






