



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	):
   _UpperCAmelCase     	=			'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       List[Any]=0							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		np.random.RandomState(lowerCAmelCase_							)
    a						=		{
        "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 		A							(      self			:       Any							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       Optional[Any]							)       ->				str:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    a						=		PNDMScheduler.from_config(pipe.scheduler.config							,   skip_prk_steps=lowerCAmelCase_							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       int							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    a						=		LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       Optional[Any]							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    a						=		EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    a						=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       int							)       ->				Tuple:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    a						=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		pipe(**lowerCAmelCase_							).images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    a						=		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 		A							(      self			:       Optional[int]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		3 * [inputs["prompt"]]

    # forward
    a						=		pipe(**lowerCAmelCase_							)
    a						=		output.images[0, -3:, -3:, -1]

    a						=		self.get_dummy_inputs()
    a						=		3 * [inputs.pop("prompt"							)]

    a						=		pipe.tokenizer(
        lowerCAmelCase_							,   padding="max_length"							,   max_length=pipe.tokenizer.model_max_length							,   truncation=lowerCAmelCase_							,   return_tensors="np"							,   )
    a						=		text_inputs["input_ids"]

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

    a						=		prompt_embeds

    # forward
    a						=		pipe(**lowerCAmelCase_							)
    a						=		output.images[0, -3:, -3:, -1]

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




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


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider="CPUExecutionProvider"							)
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		self.get_dummy_inputs()
    a						=		3 * ["this is a negative prompt"]
    a						=		negative_prompt
    a						=		3 * [inputs["prompt"]]

    # forward
    a						=		pipe(**lowerCAmelCase_							)
    a						=		output.images[0, -3:, -3:, -1]

    a						=		self.get_dummy_inputs()
    a						=		3 * [inputs.pop("prompt"							)]

    a						=		[]
    for p in [prompt, negative_prompt]:
     a						=		pipe.tokenizer(
         lowerCAmelCase_							,   padding="max_length"							,   max_length=pipe.tokenizer.model_max_length							,   truncation=lowerCAmelCase_							,   return_tensors="np"							,   )
     a						=		text_inputs["input_ids"]

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

    a		,  a						=		embeds

    # forward
    a						=		pipe(**lowerCAmelCase_							)
    a						=		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 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






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

   @property
   def 		A							(      self			:       Union[str, Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		ort.SessionOptions()
    a						=		False
    return options

   def 		A							(      self			:       Any							)       ->				str:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4"							,   revision="onnx"							,   safety_checker=lowerCAmelCase_							,   feature_extractor=lowerCAmelCase_							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
    sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

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

    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    a						=		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 		A							(      self			:       int							)       ->				Optional[int]:


    """simple docstring"""






    a						=		DDIMScheduler.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   subfolder="scheduler"							,   revision="onnx"							)
    a						=		OnnxStableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   revision="onnx"							,   scheduler=lowerCAmelCase_							,   safety_checker=lowerCAmelCase_							,   feature_extractor=lowerCAmelCase_							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
    sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		"open neural network exchange"
    a						=		np.random.RandomState(0							)
    a						=		sd_pipe([prompt]							,   guidance_scale=7.5							,   num_inference_steps=10							,   generator=lowerCAmelCase_							,   output_type="np"							)
    a						=		output.images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    a						=		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 		A							(      self			:       int							)       ->				Tuple:


    """simple docstring"""






    a						=		LMSDiscreteScheduler.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   subfolder="scheduler"							,   revision="onnx"							)
    a						=		OnnxStableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   revision="onnx"							,   scheduler=lowerCAmelCase_							,   safety_checker=lowerCAmelCase_							,   feature_extractor=lowerCAmelCase_							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
    sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		"open neural network exchange"
    a						=		np.random.RandomState(0							)
    a						=		sd_pipe([prompt]							,   guidance_scale=7.5							,   num_inference_steps=10							,   generator=lowerCAmelCase_							,   output_type="np"							)
    a						=		output.images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    a						=		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 		A							(      self			:       str							)       ->				int:


    """simple docstring"""






    a						=		0

    def test_callback_fn(__lowerCAmelCase			:       str							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       Any							) -> None:
     a						=		True
     nonlocal number_of_steps
     number_of_steps += 1
     if step == 0:
      assert latents.shape == (1, 4, 64, 64)
      a						=		latents[0, -3:, -3:, -1]
      a						=		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)
      a						=		latents[0, -3:, -3:, -1]
      a						=		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

    a						=		False

    a						=		OnnxStableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   revision="onnx"							,   safety_checker=lowerCAmelCase_							,   feature_extractor=lowerCAmelCase_							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
    pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

    a						=		"Andromeda galaxy in a bottle"

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




   def 		A							(      self			:       Dict							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		OnnxStableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"							,   revision="onnx"							,   safety_checker=lowerCAmelCase_							,   feature_extractor=lowerCAmelCase_							,   provider=self.gpu_provider							,   sess_options=self.gpu_options							,   )
    assert isinstance(lowerCAmelCase_							,   lowerCAmelCase_							)
    assert pipe.safety_checker is None

    a						=		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(lowerCAmelCase_							)
     a						=		OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_							)

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










import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_		:     int        =     logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(
       default='''NER''',   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(default=UpperCAmelCase__,   metadata={'''help''': '''Set this flag to use fast tokenization.'''}	)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},   )



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''},   )
   _UpperCAmelCase     	=			field(
       default=128,   metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       },   )
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
 if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  a		,  a		,  a						=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  a		,  a		,  a						=		parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir					)
     and os.listdir(training_args.output_dir					)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."					)

 a						=		import_module("tasks"					)
 try:
  a						=		getattr(UpperCAmelCase__ ,					model_args.task_type					)
  a						=		token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""					)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,					datefmt="%m/%d/%Y %H:%M:%S" ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,					)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,					training_args.local_rank ,					training_args.device ,					training_args.n_gpu ,					bool(training_args.local_rank != -1					) ,					training_args.fpaa ,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank					):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("Training/evaluation parameters %s" ,					UpperCAmelCase__					)

 # Set seed
 set_seed(training_args.seed					)

 # Prepare CONLL-2003 task
 a						=		token_classification_task.get_labels(data_args.labels					)
 a						=		dict(enumerate(UpperCAmelCase__					)					)
 a						=		len(UpperCAmelCase__					)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 a						=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path ,					num_labels=UpperCAmelCase__ ,					idalabel=UpperCAmelCase__ ,					labelaid={label: i for i, label in enumerate(UpperCAmelCase__					)} ,					cache_dir=model_args.cache_dir ,					)
 a						=		AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,					cache_dir=model_args.cache_dir ,					use_fast=model_args.use_fast ,					)
 a						=		AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path ,					from_tf=bool(".ckpt" in model_args.model_name_or_path					) ,					config=UpperCAmelCase__ ,					cache_dir=model_args.cache_dir ,					)

 # Get datasets
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.train ,					)
     if training_args.do_train
     else None
 )
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.dev ,					)
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCAmelCase__ :np.ndarray ,					UpperCAmelCase__ :np.ndarray					) -> Tuple[List[int], List[int]]:
  a						=		np.argmax(UpperCAmelCase__ ,					axis=2					)

  a		,  a						=		preds.shape

  a						=		[[] for _ in range(UpperCAmelCase__					)]
  a						=		[[] for _ in range(UpperCAmelCase__					)]

  for i in range(UpperCAmelCase__					):
   for j in range(UpperCAmelCase__					):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]					)
     preds_list[i].append(label_map[preds[i][j]]					)

  return preds_list, out_label_list

 def compute_metrics(UpperCAmelCase__ :EvalPrediction					) -> Dict:
  a		,  a						=		align_predictions(p.predictions ,					p.label_ids					)
  return {
      "accuracy_score": accuracy_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "precision": precision_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "recall": recall_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "f1": fa_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
  }

 # Data collator
 a						=		DataCollatorWithPadding(UpperCAmelCase__ ,					pad_to_multiple_of=8					) if training_args.fpaa else None

 # Initialize our Trainer
 a						=		Trainer(
     model=UpperCAmelCase__ ,					args=UpperCAmelCase__ ,					train_dataset=UpperCAmelCase__ ,					eval_dataset=UpperCAmelCase__ ,					compute_metrics=UpperCAmelCase__ ,					data_collator=UpperCAmelCase__ ,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_process_zero():
   tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
 a						=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  a						=		trainer.evaluate()

  a						=		os.path.join(training_args.output_dir ,					"eval_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    logger.info("***** Eval results *****"					)
    for key, value in result.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

   results.update(UpperCAmelCase__					)

    # Predict
 if training_args.do_predict:
  a						=		TokenClassificationDataset(
      token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.test ,					)

  a		,  a		,  a						=		trainer.predict(UpperCAmelCase__					)
  a		,  a						=		align_predictions(UpperCAmelCase__ ,					UpperCAmelCase__					)

  a						=		os.path.join(training_args.output_dir ,					"test_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    for key, value in metrics.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

        # Save predictions
  a						=		os.path.join(training_args.output_dir ,					"test_predictions.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    with open(os.path.join(data_args.data_dir ,					"test.txt"					) ,					"r"					) as f:
     token_classification_task.write_predictions_to_file(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 return results


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 main()


if __name__ == "__main__":
     main()

