


import unittest

import numpy as np

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 PIL import Image

   from transformers import ViltImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=7   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=2   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			str:
         __UpperCamelCase       				=size if size is not None else {'shortest_edge': 30}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=size_divisor
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[int]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }






    def _a   (     self   ,  A_   ,  A_=False       )    ->			Tuple:
         if not batched:
              __UpperCamelCase       				=self.size['shortest_edge']
              __UpperCamelCase       				=image_inputs[0]
              if isinstance(A_   ,  Image.Image       ):
                   __UpperCamelCase				, __UpperCamelCase       				=image.size
              else:
                   __UpperCamelCase				, __UpperCamelCase       				=image.shape[1], image.shape[2]
              __UpperCamelCase       				=size / min(A_   ,  A_       )
              if h < w:
                   __UpperCamelCase				, __UpperCamelCase       				=size, scale * w
              else:
                   __UpperCamelCase				, __UpperCamelCase       				=scale * h, size

              __UpperCamelCase       				=int((1333 / 800) * size       )
              if max(A_   ,  A_       ) > max_size:
                   __UpperCamelCase       				=max_size / max(A_   ,  A_       )
                   __UpperCamelCase       				=newh * scale
                   __UpperCamelCase       				=neww * scale

              __UpperCamelCase				, __UpperCamelCase       				=int(newh + 0.5       ), int(neww + 0.5       )
              __UpperCamelCase				, __UpperCamelCase       				=(
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

         else:
              __UpperCamelCase       				=[]
              for image in image_inputs:
                   __UpperCamelCase				, __UpperCamelCase       				=self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              __UpperCamelCase       				=max(A_   ,  key=lambda A_       : item[0]       )[0]
              __UpperCamelCase       				=max(A_   ,  key=lambda A_       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						Optional[int]    =    ViltImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[Any]:
         __UpperCamelCase       				=ViltImageProcessingTester(self       )






    @property
    def _a   (     self       )    ->			List[str]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			Optional[int]:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )
         self.assertTrue(hasattr(A_   ,  'size_divisor'       )       )






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'shortest_edge': 30}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'shortest_edge': 42}       )






    def _a   (     self       )    ->			Union[str, Any]:
         pass






    def _a   (     self       )    ->			Optional[Any]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_       )
         self.assertEqual(
             encoded_images.shape   ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_   ,  batched=A_       )
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,  )






    def _a   (     self       )    ->			str:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_       )
         self.assertEqual(
             encoded_images.shape   ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_   ,  batched=A_       )
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_       )
         self.assertEqual(
             encoded_images.shape   ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values

         __UpperCamelCase				, __UpperCamelCase       				=self.image_processor_tester.get_expected_values(A_   ,  batched=A_       )
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,  )









import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_A									=		logging.getLogger(__name__)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=argparse.ArgumentParser()
     parser.add_argument(
         '--model_name_or_path'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	)
     parser.add_argument('--prompt'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''							)
     parser.add_argument('--length'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=20							)
     parser.add_argument('--stop_token'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=SCREAMING_SNAKE_CASE__     ,	help='Token at which text generation is stopped'							)
     parser.add_argument(
         '--temperature'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,	)
     parser.add_argument(
         '--repetition_penalty'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='primarily useful for CTRL model; in that case, use 1.2'							)
     parser.add_argument('--k'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0							)
     parser.add_argument('--penalty_alpha'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.0							)
     parser.add_argument('--p'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.9							)

     parser.add_argument('--prefix'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Text added prior to input.'							)
     parser.add_argument('--padding_text'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Deprecated, the use of `--prefix` is preferred.'							)
     parser.add_argument('--xlm_language'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Optional language when used with the XLM model.'							)

     parser.add_argument('--seed'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=42     ,	help='random seed for initialization'							)
     parser.add_argument('--no_cuda'     ,	action='store_true'     ,	help='Avoid using CUDA when available'							)
     parser.add_argument(
         '--fp16'     ,	action='store_true'     ,	help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

     set_seed(SCREAMING_SNAKE_CASE__							)

     # Initialize the model and tokenizer
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

     # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
     # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
     model.to(args.device							)

     if args.fpaa:
          model.half()

     logger.info(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     __UpperCamelCase       				=tokenizer(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				={key: value.to(args.device							) for key, value in inputs.items()}

     __UpperCamelCase       				=model.generate(
         **SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(inputs['input_ids'][0]							)     ,	penalty_alpha=args.penalty_alpha     ,	top_k=args.k     ,	)

     __UpperCamelCase       				=[]
     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          __UpperCamelCase       				=generated_sequence.tolist()

          # Decode text
          __UpperCamelCase       				=tokenizer.decode(SCREAMING_SNAKE_CASE__     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)

          # Remove all text after the stop token
          __UpperCamelCase       				=text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          __UpperCamelCase       				=(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)							) :]
          )

          generated_sequences.append(SCREAMING_SNAKE_CASE__							)
          print(SCREAMING_SNAKE_CASE__							)

     return generated_sequences


if __name__ == "__main__":
   main()





