



"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowerCAmelCase    :Optional[Any]     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[Any]     =					{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''gptj'''
     a__									={
         '''max_position_embeddings''': '''n_positions''',
         '''hidden_size''': '''n_embd''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }





     def __init__(					self       ,	A=5_0_4_0_0       ,	A=2_0_4_8       ,	A=4_0_9_6       ,	A=2_8       ,	A=1_6       ,	A=6_4       ,	A=None       ,	A="gelu_new"       ,	A=0.0       ,	A=0.0       ,	A=0.0       ,	A=1E-5       ,	A=0.02       ,	A=True       ,	A=5_0_2_5_6       ,	A=5_0_2_5_6       ,	A=False       ,	**A       ,	)							->					str:
         _UpperCAmelCase							:  Tuple											=    vocab_size
         _UpperCAmelCase							:  Optional[int]											=    n_positions
         _UpperCAmelCase							:  Optional[Any]											=    n_embd
         _UpperCAmelCase							:  Dict											=    n_layer
         _UpperCAmelCase							:  Union[str, Any]											=    n_head
         _UpperCAmelCase							:  Union[str, Any]											=    n_inner
         _UpperCAmelCase							:  int											=    rotary_dim
         _UpperCAmelCase							:  Tuple											=    activation_function
         _UpperCAmelCase							:  int											=    resid_pdrop
         _UpperCAmelCase							:  str											=    embd_pdrop
         _UpperCAmelCase							:  Optional[Any]											=    attn_pdrop
         _UpperCAmelCase							:  Any											=    layer_norm_epsilon
         _UpperCAmelCase							:  Tuple											=    initializer_range
         _UpperCAmelCase							:  int											=    use_cache

         _UpperCAmelCase							:  Any											=    bos_token_id
         _UpperCAmelCase							:  str											=    eos_token_id

         super().__init__(
             bos_token_id=A       ,	eos_token_id=A       ,	tie_word_embeddings=A       ,	**A		)



class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A = "default"       ,	A = None       ,	A = False       ,	)							->					Any:
         super().__init__(A       ,	task=A       ,	patching_specs=A       ,	use_past=A		)
         if not getattr(self._config       ,	'''pad_token_id'''       ,	A		):
             # TODO: how to do that better?
             _UpperCAmelCase							:  List[str]											=    0





     @property
     def  __lowerCAmelCase	(					self		)							->					Mapping[str, Mapping[int, str]]:
         _UpperCAmelCase							:  List[str]											=    OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
         if self.use_past:
             self.fill_with_past_key_values_(A       ,	direction='''inputs'''		)
             _UpperCAmelCase							:  List[Any]											=    {0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             _UpperCAmelCase							:  Optional[int]											=    {0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def  __lowerCAmelCase	(					self		)							->					int:
         return self._config.n_layer





     @property
     def  __lowerCAmelCase	(					self		)							->					int:
         return self._config.n_head





     def  __lowerCAmelCase	(					self       ,	A       ,	A = -1       ,	A = -1       ,	A = False       ,	A = None       ,	)							->					Mapping[str, Any]:
         _UpperCAmelCase							:  Optional[Any]											=    super(A       ,	self		).generate_dummy_inputs(
             A       ,	batch_size=A       ,	seq_length=A       ,	is_pair=A       ,	framework=A		)

         # We need to order the input in the way they appears in the forward()
         _UpperCAmelCase							:  Optional[Any]											=    OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
             else:
                 import torch

                 _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[int]											=    common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 _UpperCAmelCase							:  Dict											=    seqlen + 2
                 _UpperCAmelCase							:  Tuple											=    (
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 _UpperCAmelCase							:  List[Any]											=    [
                     (torch.zeros(A		), torch.zeros(A		)) for _ in range(self.num_layers		)
                 ]

         _UpperCAmelCase							:  Optional[Any]											=    common_inputs['''attention_mask''']
         if self.use_past:
             _UpperCAmelCase							:  Any											=    ordered_inputs['''attention_mask'''].dtype
             _UpperCAmelCase							:  Union[str, Any]											=    torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(A       ,	A       ,	dtype=A		)]       ,	dim=1		)

         return ordered_inputs





     @property
     def  __lowerCAmelCase	(					self		)							->					int:
         return 1_3







"""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 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[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
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # 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
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


