


"""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__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Any   ="gptj"
       a  :				Any   ={
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(						self		,     snake_case__=50_400		,     snake_case__=2_048		,     snake_case__=4_096		,     snake_case__=28		,     snake_case__=16		,     snake_case__=64		,     snake_case__=None		,     snake_case__="gelu_new"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=1e-5		,     snake_case__=0.02		,     snake_case__=True		,     snake_case__=50_256		,     snake_case__=50_256		,     snake_case__=False		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							vocab_size
         lowerCAmelCase     :     Tuple								=							n_positions
         lowerCAmelCase     :     List[Any]								=							n_embd
         lowerCAmelCase     :     Any								=							n_layer
         lowerCAmelCase     :     List[Any]								=							n_head
         lowerCAmelCase     :     Optional[int]								=							n_inner
         lowerCAmelCase     :     List[str]								=							rotary_dim
         lowerCAmelCase     :     Dict								=							activation_function
         lowerCAmelCase     :     Dict								=							resid_pdrop
         lowerCAmelCase     :     List[Any]								=							embd_pdrop
         lowerCAmelCase     :     List[str]								=							attn_pdrop
         lowerCAmelCase     :     Optional[int]								=							layer_norm_epsilon
         lowerCAmelCase     :     Optional[int]								=							initializer_range
         lowerCAmelCase     :     int								=							use_cache

         lowerCAmelCase     :     Dict								=							bos_token_id
         lowerCAmelCase     :     List[str]								=							eos_token_id

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


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__ = "default"		,     snake_case__ = None		,     snake_case__ = False		,     ):






         """simple docstring"""





         super().__init__(snake_case__		,     task=snake_case__		,     patching_specs=snake_case__		,     use_past=snake_case__						)
         if not getattr(self._config		,     "pad_token_id"		,     snake_case__						):
           # TODO: how to do that better?
           lowerCAmelCase     :     Any								=							0

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
         if self.use_past:
           self.fill_with_past_key_values_(snake_case__		,     direction="inputs"						)
           lowerCAmelCase     :     int								=							{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowerCAmelCase     :     Optional[int]								=							{0: "batch", 1: "sequence"}

         return common_inputs

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_layer

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_head

       def        lowercase__							(						self		,     snake_case__		,     snake_case__ = -1		,     snake_case__ = -1		,     snake_case__ = False		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							super(snake_case__		,     self						).generate_dummy_inputs(
             snake_case__		,     batch_size=snake_case__		,     seq_length=snake_case__		,     is_pair=snake_case__		,     framework=snake_case__						)

         # We need to order the input in the way they appears in the forward()
         lowerCAmelCase     :     List[str]								=							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

             lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowerCAmelCase     :     Dict								=							seqlen + 2
             lowerCAmelCase     :     Dict								=							(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowerCAmelCase     :     Union[str, Any]								=							[
                 (torch.zeros(snake_case__						), torch.zeros(snake_case__						)) for _ in range(self.num_layers						)
             ]

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

         return ordered_inputs







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 13










def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     str													=	len(_UpperCAmelCase						)

		for i in range(n - 1						):
				for j in range(i + 1 ,   _UpperCAmelCase						):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) <= 1:
				return arr, 0
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						) // 2
		__UpperCAmelCase    :     Union[str, Any]													=	arr[0:mid]
		__UpperCAmelCase    :     Optional[Any]													=	arr[mid:]

		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	_count_cross_inversions(_UpperCAmelCase ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	inversion_p + inversions_q + cross_inversions
		return c, num_inversions

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Any													=	0
		while i < len(_UpperCAmelCase						) and j < len(_UpperCAmelCase						):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(_UpperCAmelCase						) - i
						r.append(q[j]						)
						j += 1
				else:
						r.append(p[i]						)
						i += 1

		if i < len(_UpperCAmelCase						):
				r.extend(p[i:]						)
		else:
				r.extend(q[j:]						)

		return r, num_inversion

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[10, 2, 1, 5, 5, 2, 11]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		__UpperCAmelCase    :     List[str]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		__UpperCAmelCase    :     Any													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# an empty list should also have zero inversions
		__UpperCAmelCase    :     Tuple													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





