




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


UpperCAmelCase__    :							Union[str, Any]       		=     logging.get_logger(__name__)

class   UpperCAmelCase    (      A ):
   __SCREAMING_SNAKE_CASE		:				Any      					=       ["pixel_values"]






   def __init__(						self :							int    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Dict[str, int] = None    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Dict[str, int] = None    ,       UpperCamelCase_ :							PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Union[int, float] = 1 / 2_55    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       **UpperCamelCase_ :							Any    ,       )->      None:
     super().__init__(**UpperCamelCase_  )
     _lowercase:			Optional[Any]      =     crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
     _lowercase:			List[str]      =     get_size_dict(UpperCamelCase_    ,       param_name='crop_size'  )
     _lowercase:			str      =     size if size is not None else {'height': 2_24, 'width': 2_24}
     _lowercase:			List[Any]      =     get_size_dict(UpperCamelCase_  )

     _lowercase:			Tuple      =     do_center_crop
     _lowercase:			Tuple      =     crop_size
     _lowercase:			int      =     do_resize
     _lowercase:			str      =     size
     _lowercase:			List[str]      =     resample
     _lowercase:			Optional[int]      =     do_rescale
     _lowercase:			Tuple      =     rescale_factor
     _lowercase:			Dict      =     do_normalize
     _lowercase:			Union[str, Any]      =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _lowercase:			List[Any]      =     image_std if image_std is not None else IMAGENET_DEFAULT_STD






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Dict[str, int]    ,       UpperCamelCase_ :							Optional[int] = None    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Any    ,       )->      np.ndarray:
     _lowercase:			str      =     self.size if size is None else size
     _lowercase:			List[Any]      =     get_size_dict(UpperCamelCase_  )
     _lowercase:			List[Any]      =     get_size_dict(UpperCamelCase_    ,       param_name='crop_size'  )

     _lowercase  ,       _lowercase:			str      =     get_image_size(UpperCamelCase_  )
     _lowercase:			List[Any]      =     min(UpperCamelCase_    ,       UpperCamelCase_  )
     _lowercase:			Union[str, Any]      =     (size['height'] / crop_size['height']) * min_dim
     _lowercase:			List[Any]      =     (size['width'] / crop_size['width']) * min_dim
     return center_crop(UpperCamelCase_    ,       size=(cropped_height, cropped_width)    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_  )






   def 			_a    (						self :							Any    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Dict[str, int]    ,       UpperCamelCase_ :							PILImageResampling = PIL.Image.BICUBIC    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Tuple    ,       )->      np.ndarray:
     _lowercase:			Any      =     get_size_dict(UpperCamelCase_  )
     if "height" not in size or "width" not in size:
       raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'  )
     return resize(
         UpperCamelCase_    ,       size=(size['height'], size['width'])    ,       resample=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_  )






   def 			_a    (						self :							Optional[Any]    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Union[int, float]    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Optional[Any]    ,       )->      List[Any]:
     return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_  )






   def 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Union[float, List[float]]    ,       UpperCamelCase_ :							Union[float, List[float]]    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Optional[int]    ,       )->      np.ndarray:
     return normalize(UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_  )






   def 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							ImageInput    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Dict[str, int]] = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Dict[str, int]] = None    ,       UpperCamelCase_ :							PILImageResampling = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[float] = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_ :							ChannelDimension = ChannelDimension.FIRST    ,       **UpperCamelCase_ :							Any    ,       )->      PIL.Image.Image:
     _lowercase:			Optional[int]      =     do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowercase:			Optional[int]      =     crop_size if crop_size is not None else self.crop_size
     _lowercase:			Dict      =     get_size_dict(UpperCamelCase_    ,       param_name='crop_size'  )
     _lowercase:			str      =     do_resize if do_resize is not None else self.do_resize
     _lowercase:			int      =     size if size is not None else self.size
     _lowercase:			List[str]      =     get_size_dict(UpperCamelCase_  )
     _lowercase:			Tuple      =     resample if resample is not None else self.resample
     _lowercase:			Tuple      =     do_rescale if do_rescale is not None else self.do_rescale
     _lowercase:			Tuple      =     rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowercase:			int      =     do_normalize if do_normalize is not None else self.do_normalize
     _lowercase:			Union[str, Any]      =     image_mean if image_mean is not None else self.image_mean
     _lowercase:			Tuple      =     image_std if image_std is not None else self.image_std

     _lowercase:			List[Any]      =     make_list_of_images(UpperCamelCase_  )

     if not valid_images(UpperCamelCase_  ):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'  )

     if do_center_crop and crop_size is None:
       raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'  )

     if do_resize and size is None:
       raise ValueError('Size must be specified if do_resize is True.'  )

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'  )

     # All transformations expect numpy arrays.
     _lowercase:			Any      =     [to_numpy_array(UpperCamelCase_  ) for image in images]

     if do_center_crop:
       _lowercase:			Optional[Any]      =     [self.center_crop(UpperCamelCase_    ,       UpperCamelCase_    ,       size=UpperCamelCase_  ) for image in images]

     if do_resize:
       _lowercase:			Tuple      =     [self.resize(image=UpperCamelCase_    ,       size=UpperCamelCase_    ,       resample=UpperCamelCase_  ) for image in images]

     if do_rescale:
       _lowercase:			List[Any]      =     [self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_  ) for image in images]

     if do_normalize:
       _lowercase:			Any      =     [self.normalize(image=UpperCamelCase_    ,       mean=UpperCamelCase_    ,       std=UpperCamelCase_  ) for image in images]

     _lowercase:			Any      =     [to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_  ) for image in images]

     _lowercase:			Optional[Any]      =     {'pixel_values': images}
     return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_  )






import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase__    :							Any       		=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm
   import run_image_classification
   import run_mlm
   import run_ner
   import run_qa as run_squad
   import run_summarization
   import run_swag
   import run_text_classification
   import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





  _lowercase:			str      =     {}
  _lowercase:			Union[str, Any]      =     os.path.join(UpperCamelCase       ,  'all_results.json'				)
  if os.path.exists(UpperCamelCase				):
    with open(UpperCamelCase       ,  'r'				) as f:
      _lowercase:			Dict      =     json.load(UpperCamelCase				)
  else:
    raise ValueError(F'can\'t find {path}'				)
  return results


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





  return bool(tf.config.list_physical_devices('GPU'				)				)


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






   @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
   def 			_a    (						self :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			List[str]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

     if is_cuda_available():
       testargs.append('--fp16'  )

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_text_classification.main()
       # Reset the mixed precision policy so we don't break other tests
       tf.keras.mixed_precision.set_global_policy('float32'  )
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['eval_accuracy']    ,       0.75  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

     if len(tf.config.list_physical_devices('GPU'  )  ) > 1:
       # Skipping because there are not enough batches to train the model + would need a drop_last to work.
       return

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_clm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






   def 			_a    (						self :							int  )->      Any:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     _lowercase:			List[Any]      =     7 if get_gpu_count() > 1 else 2

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_ner.main()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Any      =     f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['rouge1']    ,       10  )
       self.assertGreaterEqual(result['rouge2']    ,       2  )
       self.assertGreaterEqual(result['rougeL']    ,       7  )
       self.assertGreaterEqual(result['rougeLsum']    ,       7  )






   @slow
   def 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_translation.main()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Dict      =     f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_image_classification.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
