






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 ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






     def  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       torchify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					int		  =			image_processing(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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )














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,
)
snake_case			 =						logging.getLogger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






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


def      lowerCamelCase__	(   ):

  """simple docstring"""






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

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

  parser.add_argument("--seed"  ,   type=lowercase  ,   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"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			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(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			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(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(
      **lowercase  ,   max_length=args.length + len(inputs["input_ids"][0] )  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[str]		  =			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
    SCREAMING_SNAKE_CASE				:					str		  =			(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






