




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

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

    parser.add_argument("""--seed"""   ,    type=UpperCAmelCase_   ,    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__      =       parser.parse_args()

    a__      =       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""		)
    a__      =       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(UpperCAmelCase_		)

    # Initialize the model and tokenizer
    a__      =       AutoTokenizer.from_pretrained(args.model_name_or_path		)
    a__      =       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(UpperCAmelCase_		)
    a__      =       args.prompt if args.prompt else input("""Model prompt >>> """		)

    a__      =       tokenizer(UpperCAmelCase_   ,    return_tensors="""pt"""   ,    add_special_tokens=UpperCAmelCase_		)
    a__      =       {key: value.to(args.device		) for key, value in inputs.items()}

    a__      =       model.generate(
        **UpperCAmelCase_   ,    max_length=args.length + len(inputs["""input_ids"""][0]		)   ,    penalty_alpha=args.penalty_alpha   ,    top_k=args.k   ,    )

    a__      =       []
    for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase_		):
        print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""		)
        a__      =       generated_sequence.tolist()

        # Decode text
        a__      =       tokenizer.decode(UpperCAmelCase_   ,    clean_up_tokenization_spaces=UpperCAmelCase_   ,    add_special_tokens=UpperCAmelCase_		)

        # Remove all text after the stop token
        a__      =       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__      =       (
            prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]   ,    clean_up_tokenization_spaces=UpperCAmelCase_		)		) :]
        )

        generated_sequences.append(UpperCAmelCase_		)
        print(UpperCAmelCase_		)

    return generated_sequences


if __name__ == "__main__":
 main()












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(torch.allclose(output[:, :3, :3]						,				A						,				atol=1E-4    )    )






