

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

  def    __lowercase       (  self		:			Dict					,A		:			List[str]=0    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					np.random.RandomState(__UpperCamelCase    )
   UpperCAmelCase__					:  List[str]					   =					{
       """prompt""": """A painting of a squirrel eating a burger""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 7.5,
       """output_type""": """numpy""",
   }
   return inputs

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  List[str]					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Optional[Any]					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  str					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  Optional[int]					   =					np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Optional[int]					   =					PNDMScheduler.from_config(pipe.scheduler.config					,skip_prk_steps=__UpperCamelCase    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  List[str]					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  List[Any]					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  Optional[int]					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  List[Any]					   =					np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  List[Any]					   =					LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Optional[int]					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Optional[Any]					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  str					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  Optional[Any]					   =					np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Tuple					   =					EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  List[Any]					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  str					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  List[str]					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  int					   =					np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Any					   =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Dict					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Tuple					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  Optional[Any]					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  Dict					   =					np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  List[Any]					   =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Dict					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  List[str]					   =					pipe(**__UpperCamelCase    ).images
   UpperCAmelCase__					:  Any					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   UpperCAmelCase__					:  Optional[Any]					   =					np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Any					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Dict					   =					3 * [inputs["""prompt"""]]

   # forward
   UpperCAmelCase__					:  Tuple					   =					pipe(**__UpperCamelCase    )
   UpperCAmelCase__					:  List[str]					   =					output.images[0, -3:, -3:, -1]

   UpperCAmelCase__					:  int					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Any					   =					3 * [inputs.pop("""prompt"""    )]

   UpperCAmelCase__					:  Dict					   =					pipe.tokenizer(
       __UpperCamelCase					,padding="""max_length"""					,max_length=pipe.tokenizer.model_max_length					,truncation=__UpperCamelCase					,return_tensors="""np"""					,)
   UpperCAmelCase__					:  Any					   =					text_inputs["""input_ids"""]

   UpperCAmelCase__					:  int					   =					pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

   UpperCAmelCase__					:  Union[str, Any]					   =					prompt_embeds

   # forward
   UpperCAmelCase__					:  str					   =					pipe(**__UpperCamelCase    )
   UpperCAmelCase__					:  int					   =					output.images[0, -3:, -3:, -1]

   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4




  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  str					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Union[str, Any]					   =					3 * ["""this is a negative prompt"""]
   UpperCAmelCase__					:  Dict					   =					negative_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					3 * [inputs["""prompt"""]]

   # forward
   UpperCAmelCase__					:  Tuple					   =					pipe(**__UpperCamelCase    )
   UpperCAmelCase__					:  Any					   =					output.images[0, -3:, -3:, -1]

   UpperCAmelCase__					:  int					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  Union[str, Any]					   =					3 * [inputs.pop("""prompt"""    )]

   UpperCAmelCase__					:  Tuple					   =					[]
   for p in [prompt, negative_prompt]:
    UpperCAmelCase__					:  List[Any]					   =					pipe.tokenizer(
        __UpperCamelCase					,padding="""max_length"""					,max_length=pipe.tokenizer.model_max_length					,truncation=__UpperCamelCase					,return_tensors="""np"""					,)
    UpperCAmelCase__					:  int					   =					text_inputs["""input_ids"""]

    embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					embeds

   # forward
   UpperCAmelCase__					:  str					   =					pipe(**__UpperCamelCase    )
   UpperCAmelCase__					:  List[str]					   =					output.images[0, -3:, -3:, -1]

   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4







@nightly
@require_onnxruntime
@require_torch_gpu
class      __lowercase			(    unittest.TestCase					):

  @property
  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )

  @property
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					ort.SessionOptions()
   UpperCAmelCase__					:  Tuple					   =					False
   return options

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   # using the PNDM scheduler by default
   UpperCAmelCase__					:  int					   =					OnnxStableDiffusionPipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4"""					,revision="""onnx"""					,safety_checker=__UpperCamelCase					,feature_extractor=__UpperCamelCase					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Tuple					   =					"""A painting of a squirrel eating a burger"""
   np.random.seed(0    )
   UpperCAmelCase__					:  str					   =					sd_pipe([prompt]					,guidance_scale=6.0					,num_inference_steps=10					,output_type="""np"""    )
   UpperCAmelCase__					:  str					   =					output.images

   UpperCAmelCase__					:  Union[str, Any]					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  List[Any]					   =					np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					DDIMScheduler.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,subfolder="""scheduler"""					,revision="""onnx"""    )
   UpperCAmelCase__					:  Optional[int]					   =					OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,revision="""onnx"""					,scheduler=__UpperCamelCase					,safety_checker=__UpperCamelCase					,feature_extractor=__UpperCamelCase					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Tuple					   =					"""open neural network exchange"""
   UpperCAmelCase__					:  Any					   =					np.random.RandomState(0    )
   UpperCAmelCase__					:  Dict					   =					sd_pipe([prompt]					,guidance_scale=7.5					,num_inference_steps=10					,generator=__UpperCamelCase					,output_type="""np"""    )
   UpperCAmelCase__					:  List[Any]					   =					output.images
   UpperCAmelCase__					:  Any					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  List[Any]					   =					np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					LMSDiscreteScheduler.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,subfolder="""scheduler"""					,revision="""onnx"""    )
   UpperCAmelCase__					:  List[str]					   =					OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,revision="""onnx"""					,scheduler=__UpperCamelCase					,safety_checker=__UpperCamelCase					,feature_extractor=__UpperCamelCase					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   sd_pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  int					   =					"""open neural network exchange"""
   UpperCAmelCase__					:  Optional[Any]					   =					np.random.RandomState(0    )
   UpperCAmelCase__					:  Any					   =					sd_pipe([prompt]					,guidance_scale=7.5					,num_inference_steps=10					,generator=__UpperCamelCase					,output_type="""np"""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					output.images
   UpperCAmelCase__					:  Optional[Any]					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Optional[int]					   =					np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]    )

   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					0

   def test_callback_fn(A		:			int					,A		:			int					,A		:			np.ndarray    ) -> None:
    UpperCAmelCase__					:  Tuple					   =					True
    nonlocal number_of_steps
    number_of_steps += 1
    if step == 0:
     assert latents.shape == (1, 4, 64, 64)
     UpperCAmelCase__					:  List[Any]					   =					latents[0, -3:, -3:, -1]
     UpperCAmelCase__					:  Dict					   =					np.array(
         [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]    )

     assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1e-3
    elif step == 5:
     assert latents.shape == (1, 4, 64, 64)
     UpperCAmelCase__					:  List[Any]					   =					latents[0, -3:, -3:, -1]
     UpperCAmelCase__					:  List[Any]					   =					np.array(
         [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]    )

     assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1e-3

   UpperCAmelCase__					:  int					   =					False

   UpperCAmelCase__					:  Union[str, Any]					   =					OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,revision="""onnx"""					,safety_checker=__UpperCamelCase					,feature_extractor=__UpperCamelCase					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   pipe.set_progress_bar_config(disable=__UpperCamelCase    )

   UpperCAmelCase__					:  Optional[Any]					   =					"""Andromeda galaxy in a bottle"""

   UpperCAmelCase__					:  str					   =					np.random.RandomState(0    )
   pipe(
       prompt=__UpperCamelCase					,num_inference_steps=5					,guidance_scale=7.5					,generator=__UpperCamelCase					,callback=__UpperCamelCase					,callback_steps=1					,)
   assert test_callback_fn.has_been_called
   assert number_of_steps == 6




  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,revision="""onnx"""					,safety_checker=__UpperCamelCase					,feature_extractor=__UpperCamelCase					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   assert isinstance(__UpperCamelCase					,__UpperCamelCase    )
   assert pipe.safety_checker is None

   UpperCAmelCase__					:  Tuple					   =					pipe("""example prompt"""					,num_inference_steps=2    ).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
    pipe.save_pretrained(__UpperCamelCase    )
    UpperCAmelCase__					:  int					   =					OnnxStableDiffusionPipeline.from_pretrained(__UpperCamelCase    )

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   UpperCAmelCase__					:  str					   =					pipe("""example prompt"""					,num_inference_steps=2    ).images[0]
   assert image is not None








"""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 =				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__UpperCAmelCase =				{
    '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 =				{
    '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 =				sorted(arg_to_scheduler.keys())
__UpperCAmelCase =				'{' + ', '.join(arg_to_scheduler_choices) + '}'







class      __lowercase			(    pl.LightningModule					):

  def __init__(  self		:			List[str]					,A		:			argparse.Namespace					,A		:			List[Any]=None					,A		:			Any="base"					,A		:			List[str]=None					,A		:			Optional[Any]=None					,A		:			int=None					,**A		:			Union[str, Any]					,):






   '''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(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					0
   UpperCAmelCase__					:  List[Any]					   =					Path(self.hparams.output_dir    )
   UpperCAmelCase__					:  str					   =					self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
    UpperCAmelCase__					:  Any					   =					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=A					,**A					,)
   else:
    UpperCAmelCase__					:  PretrainedConfig					   =					config

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

   if tokenizer is None:
    UpperCAmelCase__					:  Dict					   =					AutoTokenizer.from_pretrained(
        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,cache_dir=A					,)
   else:
    UpperCAmelCase__					:  PreTrainedTokenizer					   =					tokenizer
   UpperCAmelCase__					:  Optional[int]					   =					MODEL_MODES[mode]
   if model is None:
    UpperCAmelCase__					:  Optional[Any]					   =					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=A					,)
   else:
    UpperCAmelCase__					:  List[Any]					   =					model

  def    __lowercase       (  self		:			Optional[int]					,*A		:			int					,**A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					self.model_type.from_pretrained(*A					,**A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					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__					:  Any					   =					{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
   return scheduler

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.model
   UpperCAmelCase__					:  Any					   =					["""bias""", """LayerNorm.weight"""]
   UpperCAmelCase__					:  str					   =					[
       {
           """params""": [
               p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )
           ],  # check this named paramters
           """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[str]					   =					Adafactor(
        A					,lr=self.hparams.learning_rate					,scale_parameter=A					,relative_step=A    )

   else:
    UpperCAmelCase__					:  Tuple					   =					AdamW(
        A					,lr=self.hparams.learning_rate					,eps=self.hparams.adam_epsilon    )
   UpperCAmelCase__					:  Tuple					   =					optimizer

   UpperCAmelCase__					:  Optional[int]					   =					self.get_lr_scheduler()

   return [optimizer], [scheduler]

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[str]					,A		:			int    ):






   '''simple docstring'''
   return self.validation_step(A					,A    )

  def    __lowercase       (  self		:			Any					,A		:			Tuple    ):






   '''simple docstring'''
   return self.validation_end(A    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					max(1					,self.hparams.gpus    )  # TODO: consider num_tpu_cores
   UpperCAmelCase__					:  Union[str, Any]					   =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
   return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

  def    __lowercase       (  self		:			List[Any]					,A		:			Optional[int]    ):






   '''simple docstring'''
   if stage == "test":
    UpperCAmelCase__					:  Tuple					   =					len(self.test_dataloader().dataset    )
   else:
    UpperCAmelCase__					:  Any					   =					self.get_dataloader("""train"""					,self.hparams.train_batch_size					,shuffle=A    )
    UpperCAmelCase__					:  int					   =					len(self.train_dataloader().dataset    )

  def    __lowercase       (  self		:			Dict					,A		:			str					,A		:			int					,A		:			bool = False    ):






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

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   return self.train_loader

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return self.get_dataloader("""dev"""					,self.hparams.eval_batch_size					,shuffle=A    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return self.get_dataloader("""test"""					,self.hparams.eval_batch_size					,shuffle=A    )

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[int]    ):






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

  @pl.utilities.rank_zero_only
  def    __lowercase       (  self		:			Tuple					,A		:			Dict[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.output_dir.joinpath("""best_tfmr"""    )
   UpperCAmelCase__					:  int					   =					self.step_count
   self.model.save_pretrained(A    )
   self.tokenizer.save_pretrained(A    )




  @staticmethod
  def    __lowercase       (  A		:			Any					,A		:			Optional[int]    ):






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







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			List[Any]					,A		:			Any					,A		:			Dict    ):






   '''simple docstring'''
   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      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int    ):






   '''simple docstring'''
   # print(pl_module.model.rag)
   for name, param in pl_module.model.rag.named_parameters():
    if param.grad is None:
     print(A    )







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Tuple					,A		:			Dict					,A		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					trainer.lr_schedulers[0]["""scheduler"""]
   UpperCAmelCase__					:  int					   =					{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
   pl_module.logger.log_metrics(A    )

  def    __lowercase       (  self		:			List[str]					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






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




  def    __lowercase       (  self		:			Tuple					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






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







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 parser.add_argument(
     """--output_dir"""							,    default=str(Path(__UpperCamelCase      ).parent / """test_run""" / """model_checkpoints"""      )							,    type=__UpperCamelCase							,    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=__UpperCamelCase							,    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=__UpperCamelCase      )
 parser.add_argument("""--max_grad_norm"""							,    dest="""gradient_clip_val"""							,    default=1.0							,    type=__UpperCamelCase							,    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=__UpperCamelCase							,    default=1							,    help="""Number of updates steps to accumulate before performing a backward/update pass."""							,    )
 parser.add_argument("""--seed"""							,    type=__UpperCamelCase							,    default=42							,    help="""random seed for initialization"""      )
 parser.add_argument(
     """--data_dir"""							,    default=str(Path(__UpperCamelCase      ).parent / """test_run""" / """dummy-train-data"""      )							,    type=__UpperCamelCase							,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""							,    )




def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=None							,    __UpperCamelCase=True							,    __UpperCamelCase=[]							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    **__UpperCamelCase							,    ):
 '''simple docstring'''





 pl.seed_everything(args.seed      )

 # init model
 UpperCAmelCase__					:  Optional[int]					   =					Path(model.hparams.output_dir      )
 odir.mkdir(exist_ok=__UpperCamelCase      )

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCAmelCase__					:  List[Any]					   =					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(__UpperCamelCase      )
 if logging_callback is None:
  UpperCAmelCase__					:  Tuple					   =					LoggingCallback()

 UpperCAmelCase__					:  Optional[int]					   =					{}

 if args.fpaa:
  UpperCAmelCase__					:  Any					   =					16

 if args.gpus > 1:
  UpperCAmelCase__					:  Any					   =					"""auto"""
  UpperCAmelCase__					:  str					   =					"""ddp"""

 UpperCAmelCase__					:  Dict					   =					args.accumulate_grad_batches
 UpperCAmelCase__					:  Tuple					   =					None
 UpperCAmelCase__					:  Tuple					   =					"""auto"""

 UpperCAmelCase__					:  Any					   =					pl.Trainer.from_argparse_args(
     __UpperCamelCase							,    weights_summary=__UpperCamelCase							,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,    logger=__UpperCamelCase							,    val_check_interval=1							,    num_sanity_val_steps=2							,    **__UpperCamelCase							,    )

 if args.do_train:
  trainer.fit(__UpperCamelCase      )

 else:
  print("""RAG modeling tests with new set functions successfuly executed!"""      )
 return trainer





