






'''simple docstring'''


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,
)
SCREAMING_SNAKE_CASE_				      =							logging.getLogger(__name__)






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






def   lowerCAmelCase__			(					):
 __a    :	Dict									=						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'       ,		)
 __a    :	Dict									=						parser.parse_args()

 __a    :	Tuple									=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'		)
 __a    :	Union[str, Any]									=						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
 __a    :	List[Any]									=						AutoTokenizer.from_pretrained(args.model_name_or_path		)
 __a    :	Optional[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(SCREAMING_SNAKE_CASE__		)
 __a    :	Optional[int]									=						args.prompt if args.prompt else input('Model prompt >>> '		)

 __a    :	List[Any]									=						tokenizer(SCREAMING_SNAKE_CASE__       ,		return_tensors='pt'       ,		add_special_tokens=SCREAMING_SNAKE_CASE__		)
 __a    :	Dict									=						{key: value.to(args.device		) for key, value in inputs.items()}

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

 __a    :	str									=						[]
 for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__		):
  print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''		)
  __a    :	int									=						generated_sequence.tolist()

  # Decode text
  __a    :	Union[str, Any]									=						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
  __a    :	Optional[Any]									=						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
  __a    :	int									=						(
      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()












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




