
import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

   if is_vision_available():
      from transformers import OneFormerImageProcessor
      from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
      from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
   from PIL import Image


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]				,					__lowerCamelCase     : Dict="shi-labs/oneformer_demo"    ):
       with open(hf_hub_download(__lowerCamelCase				,					__lowerCamelCase				,					repo_type="dataset"    )				,					"r"    ) as f:
              snake_case       :   Optional[int]							=      json.load(__lowerCamelCase    )
       snake_case       :   Optional[int]							=      {}
       snake_case       :   Any							=      []
       snake_case       :   List[str]							=      []
       for key, info in class_info.items():
              snake_case       :   Union[str, Any]							=      info["name"]
              class_names.append(info["name"]    )
              if info["isthing"]:
                     thing_ids.append(int(__lowerCamelCase    )    )
       snake_case       :   List[Any]							=      thing_ids
       snake_case       :   Optional[Any]							=      class_names
       return metadata






class   UpperCAmelCase		(						unittest.TestCase ):



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[Any]=7    ,						snake_case__		:					str=3    ,						snake_case__		:					Optional[Any]=30    ,						snake_case__		:					Union[str, Any]=4_00    ,						snake_case__		:					List[Any]=None    ,						snake_case__		:					Any=True    ,						snake_case__		:					Optional[int]=True    ,						snake_case__		:					str=[0.5, 0.5, 0.5]    ,						snake_case__		:					Optional[int]=[0.5, 0.5, 0.5]    ,						snake_case__		:					List[str]=10    ,						snake_case__		:					int=False    ,						snake_case__		:					str=2_55    ,						snake_case__		:					List[Any]="shi-labs/oneformer_demo"    ,						snake_case__		:					Optional[Any]="ade20k_panoptic.json"    ,						snake_case__		:					str=10    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   int							=      parent
          snake_case       :   List[Any]							=      batch_size
          snake_case       :   str							=      num_channels
          snake_case       :   Dict							=      min_resolution
          snake_case       :   int							=      max_resolution
          snake_case       :   Dict							=      do_resize
          snake_case       :   Union[str, Any]							=      {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
          snake_case       :   Optional[int]							=      do_normalize
          snake_case       :   Optional[int]							=      image_mean
          snake_case       :   Dict							=      image_std
          snake_case       :   List[str]							=      class_info_file
          snake_case       :   int							=      prepare_metadata(snake_case__    ,						snake_case__ )
          snake_case       :   Optional[int]							=      num_text
          snake_case       :   Any							=      repo_path

          # for the post_process_functions
          snake_case       :   Optional[int]							=      2
          snake_case       :   Optional[int]							=      10
          snake_case       :   List[str]							=      10
          snake_case       :   Optional[int]							=      3
          snake_case       :   Union[str, Any]							=      4
          snake_case       :   str							=      num_labels
          snake_case       :   Optional[Any]							=      do_reduce_labels
          snake_case       :   int							=      ignore_index



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[int]=False )		->	Tuple:


          '''simple docstring'''


          if not batched:
                 snake_case       :   Union[str, Any]							=      image_inputs[0]
                 if isinstance(snake_case__    ,						Image.Image ):
                        snake_case				,					snake_case       :   List[Any]							=      image.size
                 else:
                        snake_case				,					snake_case       :   Dict							=      image.shape[1], image.shape[2]
                 if w < h:
                        snake_case       :   int							=      int(self.size["shortest_edge"] * h / w )
                        snake_case       :   str							=      self.size["shortest_edge"]
                 elif w > h:
                        snake_case       :   int							=      self.size["shortest_edge"]
                        snake_case       :   List[str]							=      int(self.size["shortest_edge"] * w / h )
                 else:
                        snake_case       :   Dict							=      self.size["shortest_edge"]
                        snake_case       :   Optional[Any]							=      self.size["shortest_edge"]

          else:
                 snake_case       :   List[Any]							=      []
                 for image in image_inputs:
                        snake_case				,					snake_case       :   List[Any]							=      self.get_expected_values([image] )
                        expected_values.append((expected_height, expected_width) )
                 snake_case       :   Union[str, Any]							=      max(snake_case__    ,						key=lambda snake_case__ : item[0] )[0]
                 snake_case       :   int							=      max(snake_case__    ,						key=lambda snake_case__ : item[1] )[1]

          return expected_height, expected_width



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	List[Any]:


          '''simple docstring'''


          return OneFormerForUniversalSegmentationOutput(
              # +1 for null class
              class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) )    ,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) )    ,						)






@require_torch
@require_vision
class   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		str						     =     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
   # only for test_image_processing_common.test_image_proc_to_json_string
   A__					:		Tuple						     =     image_processing_class



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Tuple							=      OneFormerImageProcessorTester(self )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          return self.image_processing_tester.prepare_image_processor_dict()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   str							=      self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case__    ,						"image_mean" ) )
          self.assertTrue(hasattr(snake_case__    ,						"image_std" ) )
          self.assertTrue(hasattr(snake_case__    ,						"do_normalize" ) )
          self.assertTrue(hasattr(snake_case__    ,						"do_resize" ) )
          self.assertTrue(hasattr(snake_case__    ,						"size" ) )
          self.assertTrue(hasattr(snake_case__    ,						"ignore_index" ) )
          self.assertTrue(hasattr(snake_case__    ,						"class_info_file" ) )
          self.assertTrue(hasattr(snake_case__    ,						"num_text" ) )
          self.assertTrue(hasattr(snake_case__    ,						"repo_path" ) )
          self.assertTrue(hasattr(snake_case__    ,						"metadata" ) )
          self.assertTrue(hasattr(snake_case__    ,						"do_reduce_labels" ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          snake_case       :   Dict							=      prepare_image_inputs(self.image_processing_tester    ,						equal_resolution=snake_case__ )
          for image in image_inputs:
                 self.assertIsInstance(snake_case__    ,						Image.Image )

          # Test not batched input
          snake_case       :   Optional[int]							=      image_processor(image_inputs[0]    ,						["semantic"]    ,						return_tensors="pt" ).pixel_values

          snake_case				,					snake_case       :   Optional[Any]							=      self.image_processing_tester.get_expected_values(snake_case__ )

          self.assertEqual(
              encoded_images.shape    ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)    ,						)

          # Test batched
          snake_case				,					snake_case       :   Optional[int]							=      self.image_processing_tester.get_expected_values(snake_case__    ,						batched=snake_case__ )

          snake_case       :   str							=      image_processor(
              snake_case__    ,						["semantic"] * len(snake_case__ )    ,						return_tensors="pt" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          snake_case       :   List[str]							=      prepare_image_inputs(self.image_processing_tester    ,						equal_resolution=snake_case__    ,						numpify=snake_case__ )
          for image in image_inputs:
                 self.assertIsInstance(snake_case__    ,						np.ndarray )

          # Test not batched input
          snake_case       :   Union[str, Any]							=      image_processor(image_inputs[0]    ,						["semantic"]    ,						return_tensors="pt" ).pixel_values

          snake_case				,					snake_case       :   int							=      self.image_processing_tester.get_expected_values(snake_case__ )

          self.assertEqual(
              encoded_images.shape    ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)    ,						)

          # Test batched
          snake_case				,					snake_case       :   Union[str, Any]							=      self.image_processing_tester.get_expected_values(snake_case__    ,						batched=snake_case__ )

          snake_case       :   Optional[int]							=      image_processor(
              snake_case__    ,						["semantic"] * len(snake_case__ )    ,						return_tensors="pt" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          snake_case       :   Any							=      prepare_image_inputs(self.image_processing_tester    ,						equal_resolution=snake_case__    ,						torchify=snake_case__ )
          for image in image_inputs:
                 self.assertIsInstance(snake_case__    ,						torch.Tensor )

          # Test not batched input
          snake_case       :   Any							=      image_processor(image_inputs[0]    ,						["semantic"]    ,						return_tensors="pt" ).pixel_values

          snake_case				,					snake_case       :   Union[str, Any]							=      self.image_processing_tester.get_expected_values(snake_case__ )

          self.assertEqual(
              encoded_images.shape    ,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)    ,						)

          # Test batched
          snake_case				,					snake_case       :   Optional[int]							=      self.image_processing_tester.get_expected_values(snake_case__    ,						batched=snake_case__ )

          snake_case       :   List[str]							=      image_processor(
              snake_case__    ,						["semantic"] * len(snake_case__ )    ,						return_tensors="pt" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Dict=False    ,						snake_case__		:					Union[str, Any]=False    ,						snake_case__		:					str="np" )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.image_processing_class(**self.image_processor_dict )
          # prepare image and target
          snake_case       :   int							=      self.image_processing_tester.num_labels
          snake_case       :   Tuple							=      None
          snake_case       :   List[str]							=      None
          snake_case       :   Optional[int]							=      prepare_image_inputs(self.image_processing_tester    ,						equal_resolution=snake_case__ )
          if with_segmentation_maps:
                 snake_case       :   List[str]							=      num_labels
                 if is_instance_map:
                        snake_case       :   int							=      list(range(snake_case__ ) ) * 2
                        snake_case       :   Union[str, Any]							=      dict(enumerate(snake_case__ ) )
                 snake_case       :   Optional[Any]							=      [
                     np.random.randint(0    ,						high * 2    ,						(img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
                 ]
                 if segmentation_type == "pil":
                        snake_case       :   List[str]							=      [Image.fromarray(snake_case__ ) for annotation in annotations]

          snake_case       :   Dict							=      image_processor(
              snake_case__    ,						["semantic"] * len(snake_case__ )    ,						snake_case__    ,						return_tensors="pt"    ,						instance_id_to_semantic_id=snake_case__    ,						pad_and_return_pixel_mask=snake_case__    ,						)

          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Dict:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          def common(snake_case__		:					List[str]=False    ,						snake_case__		:					Optional[Any]=None ):
                 snake_case       :   Dict							=      self.comm_get_image_processor_inputs(
                     with_segmentation_maps=snake_case__    ,						is_instance_map=snake_case__    ,						segmentation_type=snake_case__ )

                 snake_case       :   List[str]							=      inputs["mask_labels"]
                 snake_case       :   Tuple							=      inputs["class_labels"]
                 snake_case       :   Dict							=      inputs["pixel_values"]
                 snake_case       :   Tuple							=      inputs["text_inputs"]

                 # check the batch_size
                 for mask_label, class_label, text_input in zip(snake_case__    ,						snake_case__    ,						snake_case__ ):
                        self.assertEqual(mask_label.shape[0]    ,						class_label.shape[0] )
                        # this ensure padding has happened
                        self.assertEqual(mask_label.shape[1:]    ,						pixel_values.shape[2:] )
                        self.assertEqual(len(snake_case__ )    ,						self.image_processing_tester.num_text )

          common()
          common(is_instance_map=snake_case__ )
          common(is_instance_map=snake_case__    ,						segmentation_type="pil" )
          common(is_instance_map=snake_case__    ,						segmentation_type="pil" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Any							=      np.zeros((20, 50) )
          snake_case       :   List[str]							=      1
          snake_case       :   List[str]							=      1
          snake_case       :   Optional[Any]							=      1

          snake_case       :   int							=      binary_mask_to_rle(snake_case__ )
          self.assertEqual(len(snake_case__ )    ,						4 )
          self.assertEqual(rle[0]    ,						21 )
          self.assertEqual(rle[1]    ,						45 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   int							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes    ,						max_seq_length=77    ,						task_seq_length=77    ,						class_info_file="ade20k_panoptic.json"    ,						num_text=self.image_processing_tester.num_text    ,						repo_path="shi-labs/oneformer_demo"    ,						)
          snake_case       :   Optional[int]							=      self.image_processing_tester.get_fake_oneformer_outputs()

          snake_case       :   Tuple							=      fature_extractor.post_process_semantic_segmentation(snake_case__ )

          self.assertEqual(len(snake_case__ )    ,						self.image_processing_tester.batch_size )
          self.assertEqual(
              segmentation[0].shape    ,						(
                  self.image_processing_tester.height,
                  self.image_processing_tester.width,
              )    ,						)

          snake_case       :   Union[str, Any]							=      [(1, 4) for i in range(self.image_processing_tester.batch_size )]
          snake_case       :   Optional[int]							=      fature_extractor.post_process_semantic_segmentation(snake_case__    ,						target_sizes=snake_case__ )

          self.assertEqual(segmentation[0].shape    ,						target_sizes[0] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	List[str]:


          '''simple docstring'''


          snake_case       :   str							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes    ,						max_seq_length=77    ,						task_seq_length=77    ,						class_info_file="ade20k_panoptic.json"    ,						num_text=self.image_processing_tester.num_text    ,						repo_path="shi-labs/oneformer_demo"    ,						)
          snake_case       :   int							=      self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case       :   List[str]							=      image_processor.post_process_instance_segmentation(snake_case__    ,						threshold=0 )

          self.assertTrue(len(snake_case__ ) == self.image_processing_tester.batch_size )
          for el in segmentation:
                 self.assertTrue("segmentation" in el )
                 self.assertTrue("segments_info" in el )
                 self.assertEqual(type(el["segments_info"] )    ,						snake_case__ )
                 self.assertEqual(
                     el["segmentation"].shape    ,						(self.image_processing_tester.height, self.image_processing_tester.width) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	str:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes    ,						max_seq_length=77    ,						task_seq_length=77    ,						class_info_file="ade20k_panoptic.json"    ,						num_text=self.image_processing_tester.num_text    ,						repo_path="shi-labs/oneformer_demo"    ,						)
          snake_case       :   Dict							=      self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case       :   List[Any]							=      image_processor.post_process_panoptic_segmentation(snake_case__    ,						threshold=0 )

          self.assertTrue(len(snake_case__ ) == self.image_processing_tester.batch_size )
          for el in segmentation:
                 self.assertTrue("segmentation" in el )
                 self.assertTrue("segments_info" in el )
                 self.assertEqual(type(el["segments_info"] )    ,						snake_case__ )
                 self.assertEqual(
                     el["segmentation"].shape    ,						(self.image_processing_tester.height, self.image_processing_tester.width) )







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : PreTrainedTokenizer				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int] = None				,					):
       snake_case       :   int							=      {}

       if train_file is not None:
              snake_case       :   List[Any]							=      [train_file]
       if eval_file is not None:
              snake_case       :   Optional[int]							=      [eval_file]
       if test_file is not None:
              snake_case       :   Any							=      [test_file]

       snake_case       :   int							=      datasets.load_dataset("csv"				,					data_files=__lowerCamelCase    )
       snake_case       :   str							=      list(ds[list(files.keys()    )[0]].features.keys()    )
       snake_case       :   int							=      features_name.pop(__lowerCamelCase    )
       snake_case       :   str							=      list(set(ds[list(files.keys()    )[0]][label_name]    )    )
       snake_case       :   str							=      {label: i for i, label in enumerate(__lowerCamelCase    )}
       snake_case       :   List[Any]							=      tokenizer.model_input_names
       snake_case       :   List[Any]							=      {}

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             example[features_name[0]]				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"    )				,					batched=__lowerCamelCase				,					)
       elif len(__lowerCamelCase    ) == 2:
              for k in files.keys():
                     snake_case       :   List[Any]							=      ds[k].map(
                         lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase				,					padding="max_length"				,					)				,					batched=__lowerCamelCase				,					)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     snake_case       :   Dict							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Union[str, Any]							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Any							=      labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     snake_case       :   str							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   List[str]							=      labelaid[ex[label_name]]
                     yield (d, label)

       snake_case       :   int							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              snake_case       :   Optional[Any]							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       snake_case       :   Tuple							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              snake_case       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           tf.data.Dataset.from_generator(
               __lowerCamelCase				,					({k: tf.intaa for k in input_names}, tf.intaa)				,					({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))				,					)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              snake_case       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the test file"} )
   A__					:		int						     =     field(
       default=1_28       ,metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }       ,)
   A__					:		bool						     =     field(
       default=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






@dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
   A__					:		bool						     =     field(default=A_       ,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.
   A__					:		Optional[str]						     =     field(
       default=A_       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)



def 			UpperCamelCase					(    ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       snake_case       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   int							=      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."    )

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,					datefmt="%m/%d/%Y %H:%M:%S"				,					level=logging.INFO				,					)
       logger.info(
           f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, """
           f"""16-bits training: {training_args.fpaa}"""    )
       logger.info(f"""Training/evaluation parameters {training_args}"""    )

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

       snake_case       :   Tuple							=      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				,					)

       snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      get_tfds(
           train_file=data_args.train_file				,					eval_file=data_args.dev_file				,					test_file=data_args.test_file				,					tokenizer=__lowerCamelCase				,					label_column_id=data_args.label_column_id				,					max_seq_length=data_args.max_seq_length				,					)

       snake_case       :   Optional[Any]							=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=len(__lowerCamelCase    )				,					labelaid=__lowerCamelCase				,					idalabel={id: label for label, id in labelaid.items()}				,					finetuning_task="text-classification"				,					cache_dir=model_args.cache_dir				,					)

       with training_args.strategy.scope():
              snake_case       :   int							=      TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,					from_pt=bool(".bin" in model_args.model_name_or_path    )				,					config=__lowerCamelCase				,					cache_dir=model_args.cache_dir				,					)

       def compute_metrics(__lowerCamelCase     : EvalPrediction    ) -> Dict:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       snake_case       :   int							=      TFTrainer(
           model=__lowerCamelCase				,					args=__lowerCamelCase				,					train_dataset=__lowerCamelCase				,					eval_dataset=__lowerCamelCase				,					compute_metrics=__lowerCamelCase				,					)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir    )

       # Evaluation
       snake_case       :   int							=      {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              snake_case       :   Any							=      trainer.evaluate()
              snake_case       :   List[Any]							=      os.path.join(training_args.output_dir				,					"eval_results.txt"    )

              with open(__lowerCamelCase				,					"w"    ) as writer:
                     logger.info("***** Eval results *****"    )

                     for key, value in result.items():
                            logger.info(f"""  {key} = {value}"""    )
                            writer.write(f"""{key} = {value}\n"""    )

                     results.update(__lowerCamelCase    )

       return results


if __name__ == "__main__":
   main()





