


'''simple docstring'''






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM






@require_torch
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  Any		)   ->  Dict:
      lowerCAmelCase_			:      str			=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
      lowerCAmelCase_			:      Optional[int]			=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   -1

      lowerCAmelCase_			:      str			=   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   model.generate(lowerCamelCase    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   tokenizer.decode(greedy_ids[0]		)

      with CaptureStdout() as cs:
        lowerCAmelCase_			:      Any			=   TextStreamer(lowerCamelCase		)
        model.generate(lowerCamelCase    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase    ,						streamer=lowerCamelCase		)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      lowerCAmelCase_			:      List[Any]			=   cs.out[:-1]

      self.assertEqual(lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Dict		)   ->  Any:
      lowerCAmelCase_			:      Optional[int]			=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
      lowerCAmelCase_			:      Optional[int]			=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      str			=   -1

      lowerCAmelCase_			:      Optional[Any]			=   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Any			=   model.generate(lowerCamelCase    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   tokenizer.decode(greedy_ids[0]		)

      lowerCAmelCase_			:      Union[str, Any]			=   TextIteratorStreamer(lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      lowerCAmelCase_			:      Union[str, Any]			=   Thread(target=model.generate    ,						kwargs=lowerCamelCase		)
      thread.start()
      lowerCAmelCase_			:      Any			=   """"""
      for new_text in streamer:
        streamer_text += new_text

      self.assertEqual(lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Tuple:
      lowerCAmelCase_			:      List[Any]			=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
      lowerCAmelCase_			:      str			=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   -1

      lowerCAmelCase_			:      Tuple			=   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size		).to(lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   model.generate(lowerCamelCase    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   greedy_ids[:, input_ids.shape[1] :]
      lowerCAmelCase_			:      Tuple			=   tokenizer.decode(new_greedy_ids[0]		)

      with CaptureStdout() as cs:
        lowerCAmelCase_			:      List[Any]			=   TextStreamer(lowerCamelCase    ,						skip_prompt=lowerCamelCase		)
        model.generate(lowerCamelCase    ,						max_new_tokens=10    ,						do_sample=lowerCamelCase    ,						streamer=lowerCamelCase		)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      lowerCAmelCase_			:      str			=   cs.out[:-1]

      self.assertEqual(lowerCamelCase    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]		)   ->  int:
      # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
      # with actual models -- the dummy models' tokenizers are not aligned with their models, and
      # `skip_special_tokens=True` has no effect on them
      lowerCAmelCase_			:      Dict			=   AutoTokenizer.from_pretrained("""distilgpt2"""		)
      lowerCAmelCase_			:      Tuple			=   AutoModelForCausalLM.from_pretrained("""distilgpt2"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   -1

      lowerCAmelCase_			:      Tuple			=   torch.ones((1, 5)    ,						device=lowerCamelCase		).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
        lowerCAmelCase_			:      Dict			=   TextStreamer(lowerCamelCase    ,						skip_special_tokens=lowerCamelCase		)
        model.generate(lowerCamelCase    ,						max_new_tokens=1    ,						do_sample=lowerCamelCase    ,						streamer=lowerCamelCase		)

      # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
      # re-tokenized, must only contain one token
      lowerCAmelCase_			:      Tuple			=   cs.out[:-1]  # Remove the final "\n"
      lowerCAmelCase_			:      Optional[int]			=   tokenizer(lowerCamelCase    ,						return_tensors="""pt"""		)
      self.assertEqual(streamer_text_tokenized.input_ids.shape    ,						(1, 1)		)





    def __lowercase   (   self    :  Any		)   ->  Tuple:
      lowerCAmelCase_			:      int			=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
      lowerCAmelCase_			:      Optional[int]			=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   -1

      lowerCAmelCase_			:      List[str]			=   ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   TextIteratorStreamer(lowerCamelCase    ,						timeout=0.001		)
      lowerCAmelCase_			:      int			=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      lowerCAmelCase_			:      Union[str, Any]			=   Thread(target=model.generate    ,						kwargs=lowerCamelCase		)
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(lowerCamelCase		):
        lowerCAmelCase_			:      Dict			=   """"""
        for new_text in streamer:
          streamer_text += new_text







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



