"""simple docstring"""
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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor




class   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=3       ,     lowercase=18       ,     lowercase=30       ,     lowercase=4_00       ,     lowercase=True       ,     lowercase=None       ,     lowercase=True       ,     lowercase=False       ,     lowercase=[0.48145466, 0.4578275, 0.40821073]       ,     lowercase=[0.26862954, 0.26130258, 0.27577711]       ,     lowercase=True       ,     )      ->     Optional[int]:





        '''simple docstring'''





        a__:	Dict     				=      size if size is not None else {'height': 20, 'width': 20}
        a__:	Tuple     				=      parent
        a__:	Optional[int]     				=      batch_size
        a__:	Tuple     				=      num_channels
        a__:	Tuple     				=      image_size
        a__:	Optional[int]     				=      min_resolution
        a__:	str     				=      max_resolution
        a__:	Union[str, Any]     				=      do_resize
        a__:	Tuple     				=      size
        a__:	Optional[Any]     				=      do_normalize
        a__:	Optional[Any]     				=      image_mean
        a__:	Dict     				=      image_std
        a__:	List[Any]     				=      do_pad
        a__:	Optional[Any]     				=      do_convert_rgb



      def     lowerCamelCase_  (		self)      ->     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,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }



      def     lowerCamelCase_  (		self       ,     lowercase=False       ,     lowercase=False       ,     lowercase=False)      ->     Optional[int]:





        '''simple docstring'''





        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          a__:	Dict     				=      []
          for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_55       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta))
        else:
          a__:	Any     				=      []
          for i in range(self.batch_size):
            a__				,       a__:	str     				=      np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution)       ,     2)
            image_inputs.append(np.random.randint(2_55       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta))

        if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a__:	List[str]     				=      [Image.fromarray(np.moveaxis(A_       ,     0       ,     -1)) for x in image_inputs]

        if torchify:
          a__:	int     				=      [torch.from_numpy(A_) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class   __snake_case			(	A_			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      BlipImageProcessingTester(self)



      @property
      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



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





        '''simple docstring'''





        a__:	Tuple     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(A_       ,     'do_resize'))
        self.assertTrue(hasattr(A_       ,     'size'))
        self.assertTrue(hasattr(A_       ,     'do_normalize'))
        self.assertTrue(hasattr(A_       ,     'image_mean'))
        self.assertTrue(hasattr(A_       ,     'image_std'))
        self.assertTrue(hasattr(A_       ,     'do_convert_rgb'))



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





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	str     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	int     				=      self.image_processor_tester.prepare_inputs(equal_resolution=A_)
        for image in image_inputs:
          self.assertIsInstance(A_       ,     Image.Image)

        # Test not batched input
        a__:	Optional[Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	List[Any]     				=      image_processor(A_       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        a__:	List[str]     				=      self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        a__:	Tuple     				=      self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,     numpify=A_)
        for image in image_inputs:
          self.assertIsInstance(A_       ,     np.ndarray)

        # Test not batched input
        a__:	Union[str, Any]     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Union[str, Any]     				=      image_processor(A_       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )



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





        '''simple docstring'''





        a__:	Optional[int]     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        a__:	Any     				=      self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,     torchify=A_)
        for image in image_inputs:
          self.assertIsInstance(A_       ,     torch.Tensor)

        # Test not batched input
        a__:	Dict     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Any     				=      image_processor(A_       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )







@require_torch
@require_vision
class   __snake_case			(	A_			, unittest.TestCase							):
      a__								=						BlipImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:	str     				=      BlipImageProcessingTester(self       ,     num_channels=4)
        a__:	List[str]     				=      3



      @property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



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





        '''simple docstring'''





        a__:	Optional[Any]     				=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(A_       ,     'do_resize'))
        self.assertTrue(hasattr(A_       ,     'size'))
        self.assertTrue(hasattr(A_       ,     'do_normalize'))
        self.assertTrue(hasattr(A_       ,     'image_mean'))
        self.assertTrue(hasattr(A_       ,     'image_std'))
        self.assertTrue(hasattr(A_       ,     'do_convert_rgb'))



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:	Dict     				=      self.image_processor_tester.prepare_inputs(equal_resolution=A_)
        for image in image_inputs:
          self.assertIsInstance(A_       ,     Image.Image)

        # Test not batched input
        a__:	str     				=      image_processor(image_inputs[0]       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,     )

        # Test batched
        a__:	Tuple     				=      image_processor(A_       ,     return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape       ,     (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['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()





