






"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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		:	Any      					=			"""platform"""

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def     lowerCamelCase__				(   __lowerCamelCase      :					Any  ,		__lowerCamelCase      :					Any  ,		__lowerCamelCase      :					List[str]=None  ,		__lowerCamelCase      :					List[Any]=None		):



  if attention_mask is None:
    __UpperCAmelCase							:     Any									=  np.where(input_ids != config.pad_token_id  ,		1  ,		0		)
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }
@require_flax
class    a   :



      """simple docstring"""


      def __init__(				self :      Tuple		,				__lowercase :      Tuple		,				__lowercase :      Optional[int]=13		,				__lowercase :      Optional[int]=7		,				__lowercase :      List[Any]=True		,				__lowercase :      List[Any]=False		,				__lowercase :      Optional[int]=99		,				__lowercase :      Union[str, Any]=16		,				__lowercase :      Union[str, Any]=2		,				__lowercase :      Tuple=4		,				__lowercase :      Optional[int]=4		,				__lowercase :      Union[str, Any]="gelu"		,				__lowercase :      Dict=0.1		,				__lowercase :      Union[str, Any]=0.1		,				__lowercase :      Union[str, Any]=20		,				__lowercase :      Dict=2		,				__lowercase :      int=1		,				__lowercase :      Optional[int]=0		,				__lowercase :      int=16		,				__lowercase :      List[str]=16		,				__lowercase :      Tuple=0.02		,				)							->				Any:
        __UpperCAmelCase							:     List[Any]									=  parent
        __UpperCAmelCase							:     str									=  batch_size
        __UpperCAmelCase							:     Union[str, Any]									=  seq_length
        __UpperCAmelCase							:     Dict									=  is_training
        __UpperCAmelCase							:     int									=  use_labels
        __UpperCAmelCase							:     Dict									=  vocab_size
        __UpperCAmelCase							:     Tuple									=  hidden_size
        __UpperCAmelCase							:     List[str]									=  num_hidden_layers
        __UpperCAmelCase							:     Any									=  num_attention_heads
        __UpperCAmelCase							:     Union[str, Any]									=  intermediate_size
        __UpperCAmelCase							:     int									=  hidden_act
        __UpperCAmelCase							:     str									=  hidden_dropout_prob
        __UpperCAmelCase							:     int									=  attention_probs_dropout_prob
        __UpperCAmelCase							:     Tuple									=  max_position_embeddings
        __UpperCAmelCase							:     Any									=  eos_token_id
        __UpperCAmelCase							:     Optional[int]									=  pad_token_id
        __UpperCAmelCase							:     Tuple									=  bos_token_id
        __UpperCAmelCase							:     Dict									=  embed_dim
        __UpperCAmelCase							:     List[str]									=  word_embed_proj_dim
        __UpperCAmelCase							:     Any									=  initializer_range
        __UpperCAmelCase							:     Dict									=  False
      def      UpperCAmelCase				(				self :      List[Any] )							->				List[Any]:
        __UpperCAmelCase							:     List[str]									=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size )		,				3		,				self.vocab_size )
        __UpperCAmelCase							:     Any									=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,				dtype=np.intaa ))		,				-1 )

        __UpperCAmelCase							:     Tuple									=  OPTConfig(
            vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				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		,				embed_dim=self.embed_dim		,				is_encoder_decoder=lowerCAmelCase__		,				word_embed_proj_dim=self.word_embed_proj_dim		,				initializer_range=self.initializer_range		,				use_cache=lowerCAmelCase__		,				)
        __UpperCAmelCase							:     Tuple									=  prepare_opt_inputs_dict(lowerCAmelCase__		,				lowerCAmelCase__ )
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Optional[int]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  self.prepare_config_and_inputs()
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      Optional[Any]		,				__lowercase :      Dict		,				__lowercase :      Optional[Any] )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  20
        __UpperCAmelCase							:     Optional[Any]									=  model_class_name(lowerCAmelCase__ )

        __UpperCAmelCase							:     Any									=  inputs_dict["""input_ids"""]
        __UpperCAmelCase							:     List[Any]									=  inputs_dict["""attention_mask"""]

        __UpperCAmelCase							:     Dict									=  model.init_cache(input_ids.shape[0]		,				lowerCAmelCase__ )
        __UpperCAmelCase							:     int									=  jnp.ones((input_ids.shape[0], max_length)		,				dtype="""i4""" )

        __UpperCAmelCase							:     Any									=  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1)		,				)
        __UpperCAmelCase							:     int									=  model(
            input_ids[:, :-1]		,				attention_mask=lowerCAmelCase__		,				past_key_values=lowerCAmelCase__		,				position_ids=lowerCAmelCase__		,				)

        __UpperCAmelCase							:     Dict									=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     int									=  model(
            input_ids[:, -1:]		,				attention_mask=lowerCAmelCase__		,				past_key_values=outputs_cache.past_key_values		,				position_ids=lowerCAmelCase__		,				)

        __UpperCAmelCase							:     Tuple									=  model(lowerCAmelCase__ )

        __UpperCAmelCase							:     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 :      Dict		,				__lowercase :      List[str]		,				__lowercase :      List[str]		,				__lowercase :      Any )							->				Union[str, Any]:
        __UpperCAmelCase							:     Union[str, Any]									=  20
        __UpperCAmelCase							:     str									=  model_class_name(lowerCAmelCase__ )

        __UpperCAmelCase				,			__UpperCAmelCase							:     Any									=  (
            inputs_dict["""input_ids"""],
            inputs_dict["""attention_mask"""],
        )

        __UpperCAmelCase							:     Any									=  jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
            ]		,				axis=-1		,				)

        __UpperCAmelCase							:     Dict									=  model.init_cache(input_ids.shape[0]		,				lowerCAmelCase__ )
        __UpperCAmelCase							:     Optional[int]									=  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1)		,				)

        __UpperCAmelCase							:     str									=  model(
            input_ids[:, :-1]		,				attention_mask=lowerCAmelCase__		,				past_key_values=lowerCAmelCase__		,				position_ids=lowerCAmelCase__		,				)
        __UpperCAmelCase							:     Dict									=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,				dtype="""i4""" )
        __UpperCAmelCase							:     Tuple									=  model(
            input_ids[:, -1:]		,				past_key_values=outputs_cache.past_key_values		,				attention_mask=lowerCAmelCase__		,				position_ids=lowerCAmelCase__		,				)

        __UpperCAmelCase							:     List[str]									=  model(lowerCAmelCase__		,				attention_mask=lowerCAmelCase__ )

        __UpperCAmelCase							:     str									=  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   (			_UpperCAmelCase							,	unittest.TestCase							,	_UpperCAmelCase       ):



      """simple docstring"""


      a  :			int								=     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      a  :			Dict								=     () if is_flax_available() else ()
      def      UpperCAmelCase				(				self :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     Optional[Any]									=  FlaxOPTModelTester(self )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ )
      def      UpperCAmelCase				(				self :      Tuple )							->				Optional[Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     str									=  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(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ )




      @slow
      def      UpperCAmelCase				(				self :      List[Any] )							->				str:


        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Optional[int]									=  model_class_name.from_pretrained("""facebook/opt-125m""" )
          __UpperCAmelCase							:     Optional[Any]									=  np.ones((1, 1) ) * model.config.eos_token_id
          __UpperCAmelCase							:     str									=  model(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )


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



      """simple docstring"""


      @slow
      def      UpperCAmelCase				(				self :      Any )							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  FlaxOPTModel.from_pretrained("""facebook/opt-350m""" )
        __UpperCAmelCase							:     Dict									=  jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
        __UpperCAmelCase							:     Tuple									=  model(input_ids=lowerCAmelCase__ ).last_hidden_state
        __UpperCAmelCase							:     List[str]									=  (1, 11, 512)
        self.assertEqual(output.shape		,				lowerCAmelCase__ )
        __UpperCAmelCase							:     Union[str, Any]									=  jnp.array(
            [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]] )
        self.assertTrue(jnp.allclose(output[:, :3, :3]		,				lowerCAmelCase__		,				atol=4e-2 ) )


@require_flax
@slow
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      int )							->				Dict:
        super().setUp()
        __UpperCAmelCase							:     List[str]									=  """facebook/opt-350m"""




      def      UpperCAmelCase				(				self :      List[str] )							->				List[Any]:
        __UpperCAmelCase							:     str									=  FlaxOPTForCausalLM.from_pretrained(self.path_model )
        __UpperCAmelCase							:     List[Any]									=  GPTaTokenizer.from_pretrained(self.path_model )

        __UpperCAmelCase							:     Any									=  [
            """Today is a beautiful day and I want to""",
            """In the city of""",
            """Paris is the capital of France and""",
            """Computers and mobile phones have taken""",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        __UpperCAmelCase							:     List[Any]									=  tokenizer(lowerCAmelCase__		,				return_tensors="""jax"""		,				padding=lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__ )
        __UpperCAmelCase							:     str									=  model(inputs.input_ids		,				attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
        __UpperCAmelCase							:     Tuple									=  jnp.array(
            [
                [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
            ] )
        self.assertTrue(jnp.allclose(lowerCAmelCase__		,				lowerCAmelCase__		,				atol=4e-2 ) )

        __UpperCAmelCase							:     Union[str, Any]									=  jax.jit(lowerCAmelCase__ )
        __UpperCAmelCase							:     Optional[int]									=  model(inputs.input_ids		,				attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
        self.assertTrue(jnp.allclose(lowerCAmelCase__		,				lowerCAmelCase__		,				atol=4e-2 ) )


@require_flax
@slow
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      @property
      def      UpperCAmelCase				(				self :      Any )							->				List[Any]:
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
      def      UpperCAmelCase				(				self :      List[Any] )							->				Optional[int]:
        __UpperCAmelCase							:     List[Any]									=  """facebook/opt-125m"""

        __UpperCAmelCase							:     Optional[int]									=  [
            """Today is a beautiful day and I want to""",
            """In the city of New York, the city""",
            """Paris is the capital of France and the capital""",
            """Computers and mobile phones have taken over the""",
        ]

        __UpperCAmelCase							:     Union[str, Any]									=  []

        __UpperCAmelCase							:     Any									=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__ )
        __UpperCAmelCase							:     Optional[int]									=  GPTaTokenizer.from_pretrained(lowerCAmelCase__ )

        for prompt in self.prompts:
          __UpperCAmelCase							:     Optional[Any]									=  tokenizer(lowerCAmelCase__		,				return_tensors="""jax""" ).input_ids

          __UpperCAmelCase							:     str									=  model.generate(lowerCAmelCase__		,				max_length=10 )
          __UpperCAmelCase							:     Union[str, Any]									=  generated_ids[0]

          __UpperCAmelCase							:     str									=  tokenizer.batch_decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__ )
          predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__ )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				int:
        __UpperCAmelCase							:     Dict									=  """facebook/opt-350m"""

        __UpperCAmelCase							:     List[Any]									=  [
            """Today is a beautiful day and I want to""",
            """In the city of San Francisco, the city""",
            """Paris is the capital of France and the capital""",
            """Computers and mobile phones have taken over the""",
        ]

        __UpperCAmelCase							:     str									=  []
        __UpperCAmelCase							:     Union[str, Any]									=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__ )
        __UpperCAmelCase							:     str									=  GPTaTokenizer.from_pretrained(lowerCAmelCase__ )

        for prompt in self.prompts:
          __UpperCAmelCase							:     List[str]									=  tokenizer(lowerCAmelCase__		,				return_tensors="""jax""" ).input_ids

          __UpperCAmelCase							:     int									=  model.generate(lowerCAmelCase__		,				max_length=10 )
          __UpperCAmelCase							:     Dict									=  generated_ids[0]

          __UpperCAmelCase							:     Union[str, Any]									=  tokenizer.batch_decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__ )
          predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__ )
      def      UpperCAmelCase				(				self :      int )							->				List[str]:
        __UpperCAmelCase							:     List[Any]									=  """facebook/opt-125m"""
        __UpperCAmelCase							:     List[str]									=  [
            """Today is a beautiful day and I want to thank""",
            """In the city of Rome Canaver Canaver Canaver Canaver""",
        ]
        __UpperCAmelCase							:     Union[str, Any]									=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__ )
        __UpperCAmelCase							:     Tuple									=  GPTaTokenizer.from_pretrained(lowerCAmelCase__ )
        __UpperCAmelCase							:     Tuple									=  tokenizer(
            [
                """Today is a beautiful day and I want to""",
                """In the city of""",
            ]		,				return_tensors="""jax"""		,				padding=lowerCAmelCase__		,				)

        __UpperCAmelCase							:     Optional[Any]									=  jax.jit(model.generate )

        __UpperCAmelCase							:     Optional[int]									=  jit_generate(inputs["""input_ids"""]		,				attention_mask=inputs["""attention_mask"""] ).sequences

        __UpperCAmelCase							:     List[str]									=  tokenizer.batch_decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__ )

        self.assertIsNotNone(lowerCAmelCase__		,				lowerCAmelCase__ )




      def      UpperCAmelCase				(				self :      Any )							->				Dict:
        __UpperCAmelCase							:     List[str]									=  """facebook/opt-350m"""

        __UpperCAmelCase							:     Union[str, Any]									=  GPTaTokenizer.from_pretrained(lowerCAmelCase__ )
        __UpperCAmelCase							:     Dict									=  FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__ )

        __UpperCAmelCase							:     Tuple									=  """left"""

        # use different length sentences to test batching
        __UpperCAmelCase							:     Optional[int]									=  [
            """Hello, my dog is a little""",
            """Today, I""",
        ]

        __UpperCAmelCase							:     Optional[Any]									=  tokenizer(lowerCAmelCase__		,				return_tensors="""jax"""		,				padding=lowerCAmelCase__ )
        __UpperCAmelCase							:     int									=  inputs["""input_ids"""]

        __UpperCAmelCase							:     int									=  model.generate(input_ids=lowerCAmelCase__		,				attention_mask=inputs["""attention_mask"""]		,				trace=lowerCAmelCase__ )

        __UpperCAmelCase							:     List[str]									=  tokenizer(sentences[0]		,				return_tensors="""jax""" ).input_ids
        __UpperCAmelCase							:     Optional[Any]									=  model.generate(input_ids=lowerCAmelCase__ )

        __UpperCAmelCase							:     int									=  inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
        __UpperCAmelCase							:     Tuple									=  tokenizer(sentences[1]		,				return_tensors="""jax""" ).input_ids
        __UpperCAmelCase							:     Tuple									=  model.generate(input_ids=lowerCAmelCase__		,				max_length=model.config.max_length - num_paddings )

        __UpperCAmelCase							:     Union[str, Any]									=  tokenizer.batch_decode(outputs[0]		,				skip_special_tokens=lowerCAmelCase__ )
        __UpperCAmelCase							:     Any									=  tokenizer.decode(output_non_padded[0][0]		,				skip_special_tokens=lowerCAmelCase__ )
        __UpperCAmelCase							:     Any									=  tokenizer.decode(output_padded[0][0]		,				skip_special_tokens=lowerCAmelCase__ )

        __UpperCAmelCase							:     Optional[Any]									=  [
            """Hello, my dog is a little bit of a dork.\nI\'m a little bit""",
            """Today, I was in the middle of a conversation with a friend about the""",
        ]
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__ )
        self.assertListEqual(lowerCAmelCase__		,				[non_padded_sentence, padded_sentence] )










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


