






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       a		:	List[str]      					=			"platform"

       import jax
       import jax.numpy as jnp

       from transformers import AutoTokenizer
       from transformers.models.mbart.modeling_flax_mbart import (
           FlaxMBartForConditionalGeneration,
           FlaxMBartForQuestionAnswering,
           FlaxMBartForSequenceClassification,
           FlaxMBartModel,
           shift_tokens_right,
       )






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[str]=None  ,		__lowerCamelCase      :					Union[str, Any]=None  ,		__lowerCamelCase      :					Union[str, Any]=None  ,		__lowerCamelCase      :					Any=None  ,		__lowerCamelCase      :					Optional[Any]=None  ,		):
  if attention_mask is None:
    __UpperCAmelCase							:     str									=  np.where(input_ids != config.pad_token_id  ,		1  ,		0		)
  if decoder_attention_mask is None:
    __UpperCAmelCase							:     int									=  np.where(decoder_input_ids != config.pad_token_id  ,		1  ,		0		)
  if head_mask is None:
    __UpperCAmelCase							:     int									=  np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    __UpperCAmelCase							:     List[Any]									=  np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    __UpperCAmelCase							:     Dict									=  np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def __init__(				self :      Dict		,				__lowercase :      Optional[int]		,				__lowercase :      Optional[int]=13		,				__lowercase :      Optional[Any]=7		,				__lowercase :      Union[str, Any]=True		,				__lowercase :      List[Any]=False		,				__lowercase :      Dict=99		,				__lowercase :      Union[str, Any]=16		,				__lowercase :      Any=2		,				__lowercase :      List[str]=4		,				__lowercase :      int=4		,				__lowercase :      str="gelu"		,				__lowercase :      Optional[int]=0.1		,				__lowercase :      List[Any]=0.1		,				__lowercase :      str=32		,				__lowercase :      Optional[int]=2		,				__lowercase :      Tuple=1		,				__lowercase :      Dict=0		,				__lowercase :      List[str]=2		,				__lowercase :      Optional[int]=0.02		,				)							->				List[str]:
        __UpperCAmelCase							:     Optional[Any]									=  parent
        __UpperCAmelCase							:     str									=  batch_size
        __UpperCAmelCase							:     List[Any]									=  seq_length
        __UpperCAmelCase							:     int									=  is_training
        __UpperCAmelCase							:     Tuple									=  use_labels
        __UpperCAmelCase							:     str									=  vocab_size
        __UpperCAmelCase							:     Union[str, Any]									=  hidden_size
        __UpperCAmelCase							:     Union[str, Any]									=  num_hidden_layers
        __UpperCAmelCase							:     int									=  num_attention_heads
        __UpperCAmelCase							:     Dict									=  intermediate_size
        __UpperCAmelCase							:     List[str]									=  hidden_act
        __UpperCAmelCase							:     str									=  hidden_dropout_prob
        __UpperCAmelCase							:     int									=  attention_probs_dropout_prob
        __UpperCAmelCase							:     Optional[int]									=  max_position_embeddings
        __UpperCAmelCase							:     int									=  eos_token_id
        __UpperCAmelCase							:     Optional[Any]									=  pad_token_id
        __UpperCAmelCase							:     List[Any]									=  bos_token_id
        __UpperCAmelCase							:     List[Any]									=  decoder_start_token_id
        __UpperCAmelCase							:     Union[str, Any]									=  initializer_range
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Union[str, Any]:
        __UpperCAmelCase							:     int									=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size )		,				3		,				self.vocab_size )
        __UpperCAmelCase							:     Optional[int]									=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,				dtype=np.intaa ))		,				-1 )

        __UpperCAmelCase							:     Tuple									=  shift_tokens_right(__lowercase		,				1 )

        __UpperCAmelCase							:     Optional[Any]									=  MBartConfig(
            vocab_size=self.vocab_size		,				d_model=self.hidden_size		,				encoder_layers=self.num_hidden_layers		,				decoder_layers=self.num_hidden_layers		,				encoder_attention_heads=self.num_attention_heads		,				decoder_attention_heads=self.num_attention_heads		,				encoder_ffn_dim=self.intermediate_size		,				decoder_ffn_dim=self.intermediate_size		,				dropout=self.hidden_dropout_prob		,				attention_dropout=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				eos_token_id=self.eos_token_id		,				bos_token_id=self.bos_token_id		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.decoder_start_token_id		,				initializer_range=self.initializer_range		,				use_cache=__lowercase		,				)
        __UpperCAmelCase							:     Optional[Any]									=  prepare_mbart_inputs_dict(__lowercase		,				__lowercase		,				__lowercase )
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      List[Any] )							->				List[str]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  self.prepare_config_and_inputs()
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Tuple		,				__lowercase :      Optional[int]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        __UpperCAmelCase							:     int									=  20
        __UpperCAmelCase							:     List[Any]									=  model_class_name(__lowercase )

        __UpperCAmelCase							:     Dict									=  model.encode(inputs_dict["""input_ids"""] )

        __UpperCAmelCase				,			__UpperCAmelCase							:     Tuple									=  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        __UpperCAmelCase							:     Optional[Any]									=  model.init_cache(decoder_input_ids.shape[0]		,				__lowercase		,				__lowercase )
        __UpperCAmelCase							:     Dict									=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype="""i4""" )

        __UpperCAmelCase							:     List[Any]									=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)
        __UpperCAmelCase							:     List[str]									=  model.decode(
            decoder_input_ids[:, :-1]		,				__lowercase		,				decoder_attention_mask=__lowercase		,				past_key_values=__lowercase		,				decoder_position_ids=__lowercase		,				)

        __UpperCAmelCase							:     Optional[int]									=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     Dict									=  model.decode(
            decoder_input_ids[:, -1:]		,				__lowercase		,				decoder_attention_mask=__lowercase		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=__lowercase		,				)

        __UpperCAmelCase							:     List[Any]									=  model.decode(__lowercase		,				__lowercase )

        __UpperCAmelCase							:     Union[str, Any]									=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3		,				msg=f"""Max diff is {diff}""" )




      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Any		,				__lowercase :      str		,				__lowercase :      List[Any] )							->				Optional[int]:
        __UpperCAmelCase							:     int									=  20
        __UpperCAmelCase							:     List[Any]									=  model_class_name(__lowercase )

        __UpperCAmelCase							:     Tuple									=  model.encode(inputs_dict["""input_ids"""] )

        __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        __UpperCAmelCase							:     Tuple									=  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
            ]		,				axis=-1		,				)

        __UpperCAmelCase							:     Optional[int]									=  model.init_cache(decoder_input_ids.shape[0]		,				__lowercase		,				__lowercase )
        __UpperCAmelCase							:     List[str]									=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)

        __UpperCAmelCase							:     Union[str, Any]									=  model.decode(
            decoder_input_ids[:, :-1]		,				__lowercase		,				decoder_attention_mask=__lowercase		,				past_key_values=__lowercase		,				decoder_position_ids=__lowercase		,				)
        __UpperCAmelCase							:     List[Any]									=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     str									=  model.decode(
            decoder_input_ids[:, -1:]		,				__lowercase		,				past_key_values=outputs_cache.past_key_values		,				decoder_attention_mask=__lowercase		,				decoder_position_ids=__lowercase		,				)

        __UpperCAmelCase							:     int									=  model.decode(__lowercase		,				__lowercase		,				decoder_attention_mask=__lowercase )

        __UpperCAmelCase							:     Dict									=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3		,				msg=f"""Max diff is {diff}""" )


@require_flax
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      a  :			Tuple								=     99
      def      UpperCAmelCase				(				self :      Any )							->				Optional[int]:
        __UpperCAmelCase							:     List[str]									=  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]		,				dtype=np.intaa		,				)

        __UpperCAmelCase							:     Optional[Any]									=  input_ids.shape[0]
        __UpperCAmelCase							:     str									=  MBartConfig(
            vocab_size=self.vocab_size		,				d_model=24		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=32		,				decoder_ffn_dim=32		,				max_position_embeddings=48		,				eos_token_id=2		,				pad_token_id=1		,				bos_token_id=0		,				)
        return config, input_ids, batch_size
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Optional[int]:
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  self._get_config_and_data()
        __UpperCAmelCase							:     Tuple									=  FlaxMBartForSequenceClassification(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  model(input_ids=__lowercase		,				decoder_input_ids=__lowercase )
        __UpperCAmelCase							:     List[str]									=  (batch_size, config.num_labels)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  self._get_config_and_data()
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForQuestionAnswering(__lowercase )
        __UpperCAmelCase							:     Any									=  model(input_ids=__lowercase )

        self.assertEqual(outputs["""start_logits"""].shape		,				input_ids.shape )
        self.assertEqual(outputs["""end_logits"""].shape		,				input_ids.shape )
      def      UpperCAmelCase				(				self :      Any )							->				Tuple:
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  self._get_config_and_data()
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration(__lowercase )
        __UpperCAmelCase							:     int									=  lm_model(input_ids=__lowercase )
        __UpperCAmelCase							:     List[Any]									=  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )
      def      UpperCAmelCase				(				self :      List[Any] )							->				Optional[Any]:
        __UpperCAmelCase							:     str									=  MBartConfig(
            vocab_size=self.vocab_size		,				d_model=14		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=8		,				decoder_ffn_dim=8		,				max_position_embeddings=48		,				)
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration(__lowercase )
        __UpperCAmelCase							:     str									=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     Optional[int]									=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     Optional[int]									=  lm_model(input_ids=__lowercase		,				decoder_input_ids=__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				__lowercase )




      def      UpperCAmelCase				(				self :      Any )							->				str:
        __UpperCAmelCase							:     List[Any]									=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,				dtype=np.intaa )
        __UpperCAmelCase							:     str									=  shift_tokens_right(__lowercase		,				1 )
        __UpperCAmelCase							:     Optional[int]									=  np.equal(__lowercase		,				1 ).astype(np.floataa ).sum()
        __UpperCAmelCase							:     Tuple									=  np.equal(__lowercase		,				1 ).astype(np.floataa ).sum()
        self.assertEqual(shifted.shape		,				input_ids.shape )
        self.assertEqual(__lowercase		,				n_pad_before - 1 )
        self.assertTrue(np.equal(shifted[:, 0]		,				2 ).all() )


@require_flax
class    a   (			lowercase__							,	unittest.TestCase							,	lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     True
      a  :			Optional[int]								=     (
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      a  :			str								=     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
      def      UpperCAmelCase				(				self :      List[Any] )							->				int:
        __UpperCAmelCase							:     Any									=  FlaxMBartModelTester(self )
      def      UpperCAmelCase				(				self :      Dict )							->				Any:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__lowercase		,				__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      int )							->				Union[str, Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(__lowercase		,				__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      List[str] )							->				Tuple:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __UpperCAmelCase							:     Dict									=  self._prepare_for_class(__lowercase		,				__lowercase )
            __UpperCAmelCase							:     List[str]									=  model_class(__lowercase )

            @jax.jit
            def encode_jitted(__lowercase :      Optional[Any]		,				__lowercase :      List[Any]=None		,				**__lowercase :      List[Any] ):
              return model.encode(input_ids=__lowercase		,				attention_mask=__lowercase )

            with self.subTest("""JIT Enabled""" ):
              __UpperCAmelCase							:     Tuple									=  encode_jitted(**__lowercase ).to_tuple()

            with self.subTest("""JIT Disabled""" ):
              with jax.disable_jit():
                __UpperCAmelCase							:     Tuple									=  encode_jitted(**__lowercase ).to_tuple()

            self.assertEqual(len(__lowercase )		,				len(__lowercase ) )
            for jitted_output, output in zip(__lowercase		,				__lowercase ):
              self.assertEqual(jitted_output.shape		,				output.shape )
      def      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
            __UpperCAmelCase							:     List[str]									=  model_class(__lowercase )
            __UpperCAmelCase							:     Optional[int]									=  model.encode(inputs_dict["""input_ids"""]		,				inputs_dict["""attention_mask"""] )

            __UpperCAmelCase							:     str									=  {
                """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                """encoder_outputs""": encoder_outputs,
            }

            @jax.jit
            def decode_jitted(__lowercase :      int		,				__lowercase :      Dict		,				__lowercase :      int ):
              return model.decode(
                  decoder_input_ids=__lowercase		,				decoder_attention_mask=__lowercase		,				encoder_outputs=__lowercase		,				)

            with self.subTest("""JIT Enabled""" ):
              __UpperCAmelCase							:     str									=  decode_jitted(**__lowercase ).to_tuple()

            with self.subTest("""JIT Disabled""" ):
              with jax.disable_jit():
                __UpperCAmelCase							:     str									=  decode_jitted(**__lowercase ).to_tuple()

            self.assertEqual(len(__lowercase )		,				len(__lowercase ) )
            for jitted_output, output in zip(__lowercase		,				__lowercase ):
              self.assertEqual(jitted_output.shape		,				output.shape )




      @slow
      def      UpperCAmelCase				(				self :      Any )							->				List[str]:
        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Optional[int]									=  model_class_name.from_pretrained("""facebook/mbart-large-cc25"""		,				from_pt=__lowercase )
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          __UpperCAmelCase							:     List[Any]									=  np.ones((1, 1) ) * model.config.eos_token_id
          __UpperCAmelCase							:     Optional[Any]									=  model(__lowercase )
          self.assertIsNotNone(__lowercase )


@require_flax
@require_sentencepiece
@require_tokenizers
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      a  :			Union[str, Any]								=     [
          ' UN Chief Says There Is No Military Solution in Syria',
      ]
      a  :			Tuple								=     [
          'Şeful ONU declară că nu există o soluţie militară în Siria',
      ]
      a  :			List[Any]								=     'facebook/mbart-large-en-ro'
      @cached_property
      def      UpperCAmelCase				(				self :      int )							->				Dict:
        return AutoTokenizer.from_pretrained(self.model_name )
      @cached_property
      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     Optional[Any]									=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		,				from_pt=__lowercase )
        return model
      def      UpperCAmelCase				(				self :      Optional[int]		,				**__lowercase :      List[Any] )							->				str:
        __UpperCAmelCase							:     Any									=  self.translate_src_text(**__lowercase )
        self.assertListEqual(self.expected_text		,				__lowercase )
      def      UpperCAmelCase				(				self :      int		,				**__lowercase :      Optional[Any] )							->				Any:
        __UpperCAmelCase							:     Any									=  self.tokenizer(self.src_text		,				**__lowercase		,				return_tensors="""np""" )
        __UpperCAmelCase							:     Union[str, Any]									=  self.model.generate(
            model_inputs.input_ids		,				attention_mask=model_inputs.attention_mask		,				decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]		,				early_stopping=__lowercase		,				num_beams=2		,				).sequences
        __UpperCAmelCase							:     int									=  self.tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase )
        return generated_words




      @slow
      def      UpperCAmelCase				(				self :      Tuple )							->				List[Any]:
        self._assert_generated_batch_equal_expected()










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


a		:	str      					=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

a		:	Any      					=			{
    "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
a		:	List[str]      					=			{
    "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
}
a		:	int      					=			sorted(arg_to_scheduler.keys())
a		:	Dict      					=			"{" + ", ".join(arg_to_scheduler_choices) + "}"
class    a   (			pl.LightningModule       ):



      """simple docstring"""


      def __init__(				self :      Union[str, Any]		,				__lowercase :      argparse.Namespace		,				__lowercase :      int=None		,				__lowercase :      Any="base"		,				__lowercase :      str=None		,				__lowercase :      Dict=None		,				__lowercase :      Optional[Any]=None		,				**__lowercase :      Optional[Any]		,				)							->				List[str]:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(__lowercase )
        __UpperCAmelCase							:     Any									=  0
        __UpperCAmelCase							:     List[Any]									=  Path(self.hparams.output_dir )
        __UpperCAmelCase							:     int									=  self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
          __UpperCAmelCase							:     str									=  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=__lowercase		,				**__lowercase		,				)
        else:
          __UpperCAmelCase							:     PretrainedConfig									=  config

        __UpperCAmelCase							:     Optional[int]									=  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
        for p in extra_model_params:
          if getattr(self.hparams		,				__lowercase		,				__lowercase ):
            assert hasattr(self.config		,				__lowercase ), f"""model config doesn't have a `{p}` attribute"""
            setattr(self.config		,				__lowercase		,				getattr(self.hparams		,				__lowercase ) )

        if tokenizer is None:
          __UpperCAmelCase							:     Tuple									=  AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,				cache_dir=__lowercase		,				)
        else:
          __UpperCAmelCase							:     PreTrainedTokenizer									=  tokenizer
        __UpperCAmelCase							:     Dict									=  MODEL_MODES[mode]
        if model is None:
          __UpperCAmelCase							:     Dict									=  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=__lowercase		,				)
        else:
          __UpperCAmelCase							:     Any									=  model
      def      UpperCAmelCase				(				self :      Optional[Any]		,				*__lowercase :      Optional[Any]		,				**__lowercase :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase							:     List[Any]									=  self.model_type.from_pretrained(*__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Dict:
        __UpperCAmelCase							:     Union[str, Any]									=  arg_to_scheduler[self.hparams.lr_scheduler]
        __UpperCAmelCase							:     Optional[int]									=  get_schedule_func(
            self.opt		,				num_warmup_steps=self.hparams.warmup_steps		,				num_training_steps=self.total_steps() )
        __UpperCAmelCase							:     int									=  {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler
      def      UpperCAmelCase				(				self :      Optional[int] )							->				int:
        __UpperCAmelCase							:     List[str]									=  self.model
        __UpperCAmelCase							:     List[str]									=  ["""bias""", """LayerNorm.weight"""]
        __UpperCAmelCase							:     List[str]									=  [
            {
                """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:
          __UpperCAmelCase							:     List[Any]									=  Adafactor(
              __lowercase		,				lr=self.hparams.learning_rate		,				scale_parameter=__lowercase		,				relative_step=__lowercase )

        else:
          __UpperCAmelCase							:     Optional[int]									=  AdamW(
              __lowercase		,				lr=self.hparams.learning_rate		,				eps=self.hparams.adam_epsilon )
        __UpperCAmelCase							:     Dict									=  optimizer

        __UpperCAmelCase							:     Dict									=  self.get_lr_scheduler()

        return [optimizer], [scheduler]
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      List[Any]		,				__lowercase :      Tuple )							->				List[Any]:
        return self.validation_step(__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        return self.validation_end(__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  max(1		,				self.hparams.gpus )  # TODO: consider num_tpu_cores
        __UpperCAmelCase							:     int									=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Tuple )							->				Dict:
        if stage == "test":
          __UpperCAmelCase							:     str									=  len(self.test_dataloader().dataset )
        else:
          __UpperCAmelCase							:     Any									=  self.get_dataloader("""train"""		,				self.hparams.train_batch_size		,				shuffle=__lowercase )
          __UpperCAmelCase							:     List[Any]									=  len(self.train_dataloader().dataset )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      str		,				__lowercase :      int		,				__lowercase :      bool = False )							->				Optional[int]:
        raise NotImplementedError("""You must implement this for your task""" )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Dict:
        return self.train_loader
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Tuple:
        return self.get_dataloader("""dev"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
        return self.get_dataloader("""test"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      Optional[int] )							->				List[Any]:
        return os.path.join(
            self.hparams.data_dir		,				"""cached_{}_{}_{}""".format(
                __lowercase		,				list(filter(__lowercase		,				self.hparams.model_name_or_path.split("""/""" ) ) ).pop()		,				str(self.hparams.max_seq_length )		,				)		,				)
      @pl.utilities.rank_zero_only
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Dict[str, Any] )							->				None:
        __UpperCAmelCase							:     List[str]									=  self.output_dir.joinpath("""best_tfmr""" )
        __UpperCAmelCase							:     str									=  self.step_count
        self.model.save_pretrained(__lowercase )
        self.tokenizer.save_pretrained(__lowercase )




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


class    a   (			pl.Callback       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      int		,				__lowercase :      Tuple		,				__lowercase :      str )							->				Optional[Any]:
        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
          pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class    a   (			pl.Callback       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      int		,				__lowercase :      Union[str, Any] )							->				Optional[int]:
        __UpperCAmelCase							:     Union[str, Any]									=  trainer.lr_schedulers[0]["""scheduler"""]
        __UpperCAmelCase							:     Optional[Any]									=  {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
        pl_module.logger.log_metrics(__lowercase )
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Dict:
        rank_zero_info("""***** Validation results *****""" )
        __UpperCAmelCase							:     int									=  trainer.callback_metrics
        # Log results
        for key in sorted(__lowercase ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )




      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Any:
        rank_zero_info("""***** Test results *****""" )
        __UpperCAmelCase							:     List[str]									=  trainer.callback_metrics
        # Log and save results to file
        __UpperCAmelCase							:     Dict									=  os.path.join(pl_module.hparams.output_dir		,				"""test_results.txt""" )
        with open(__lowercase		,				"""w""" ) as writer:
          for key in sorted(__lowercase ):
            if key not in ["log", "progress_bar"]:
              rank_zero_info("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )
              writer.write("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					List[Any]		):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  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     lowerCamelCase__				(   __lowerCamelCase      :					BaseTransformer  ,		__lowerCamelCase      :					argparse.Namespace  ,		__lowerCamelCase      :					Any=None  ,		__lowerCamelCase      :					Optional[Any]=True  ,		__lowerCamelCase      :					Tuple=None  ,		__lowerCamelCase      :					Optional[int]=[]  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[str]=None  ,		**__lowerCamelCase      :					List[Any]  ,		):
  pl.seed_everything(args.seed		)

  # init model
  __UpperCAmelCase							:     Optional[Any]									=  Path(model.hparams.output_dir		)
  odir.mkdir(exist_ok=__lowerCamelCase		)

  # add custom checkpoints
  if checkpoint_callback is None:
    __UpperCAmelCase							:     Tuple									=  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:
    __UpperCAmelCase							:     int									=  LoggingCallback()

  __UpperCAmelCase							:     int									=  {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    __UpperCAmelCase							:     Dict									=  16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    __UpperCAmelCase							:     List[str]									=  """auto"""  # "ddp"
    __UpperCAmelCase							:     Union[str, Any]									=  """ddp"""

  __UpperCAmelCase							:     Optional[int]									=  args.accumulate_grad_batches
  __UpperCAmelCase							:     Dict									=  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  __UpperCAmelCase							:     Optional[int]									=  """auto"""

  __UpperCAmelCase							:     Optional[Any]									=  pl.Trainer.from_argparse_args(
      __lowerCamelCase  ,		weights_summary=__lowerCamelCase  ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,		logger=__lowerCamelCase  ,		**__lowerCamelCase  ,		)

  if args.do_train:
    trainer.fit(__lowerCamelCase		)

  return trainer


