





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 			__A(    unittest.TestCase			):

     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],							):





            """simple docstring"""

            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            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 						_UpperCamelCase  (   self,							A,							A=False   ):





            """simple docstring"""

            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 			__A(    A_    ,      unittest.TestCase			):
     __A							=							ViltImageProcessor if is_vision_available() else None

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ViltImageProcessingTester(self   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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()





