
import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger
def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       copy.deepcopy(_lowerCamelCase					)
					lowerCAmelCase						:   Any							=       sorted(_lowerCamelCase					)
					lowerCAmelCase						:   int							=       [bisect.bisect_right(_lowerCamelCase							,					_lowerCamelCase					) for y in x]
					return quantized
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Any=0					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       np.arange(start=3							,					stop=_lowerCamelCase							,					step=4					).tolist() if k > 0 else [10]
					lowerCAmelCase						:   str							=       _quantize(_lowerCamelCase							,					_lowerCamelCase					)
					# count number of elements per group
					lowerCAmelCase						:   Any							=       np.unique(_lowerCamelCase							,					return_counts=_lowerCamelCase					)[1]
					lowerCAmelCase						:   Union[str, Any]							=       [0] + bins + [np.inf]
					logger.info('''Using {} as bins for aspect lengths quantization'''.format(_lowerCamelCase					)					)
					logger.info('''Count of instances per bin: {}'''.format(_lowerCamelCase					)					)
					return groups


class 						UpperCAmelCase				(  __lowerCAmelCase      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Any			):




							if not isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE			):
												raise ValueError(
												    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(__SCREAMING_SNAKE_CASE			)			)
							lowerCAmelCase						:   str							=       sampler
							lowerCAmelCase						:   Tuple							=       group_ids
							lowerCAmelCase						:   int							=       batch_size





		def __iter__(							self      :		int			):
							lowerCAmelCase						:   Tuple							=       defaultdict(__SCREAMING_SNAKE_CASE			)
							lowerCAmelCase						:   int							=       defaultdict(__SCREAMING_SNAKE_CASE			)

							lowerCAmelCase						:   List[str]							=       0
							for idx in self.sampler:
												lowerCAmelCase						:   Dict							=       self.group_ids[idx]
												buffer_per_group[group_id].append(__SCREAMING_SNAKE_CASE			)
												samples_per_group[group_id].append(__SCREAMING_SNAKE_CASE			)
												if len(buffer_per_group[group_id]			) == self.batch_size:
																	yield buffer_per_group[group_id]  # TODO
																	num_batches += 1
																	del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]			) < self.batch_size

							# now we have run out of elements that satisfy
							# the group criteria, let's return the remaining
							# elements so that the size of the sampler is
							# deterministic
							lowerCAmelCase						:   Union[str, Any]							=       len(self			)
							lowerCAmelCase						:   List[str]							=       expected_num_batches - num_batches
							if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												lowerCAmelCase						:   Dict							=       []
												for group_id, idxs in sorted(buffer_per_group.items()  ,					key=lambda lowerCamelCase__			: x[0]			):
																	batch_idx.extend(__SCREAMING_SNAKE_CASE			)
																	if len(__SCREAMING_SNAKE_CASE			) >= self.batch_size:
																						yield batch_idx[: self.batch_size]
																						lowerCAmelCase						:   Union[str, Any]							=       batch_idx[self.batch_size :]
																						num_remaining -= 1
												if len(__SCREAMING_SNAKE_CASE			) > 0:
																	yield batch_idx
																	num_remaining -= 1
							assert num_remaining == 0





		def __len__(							self      :		str			):
							return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


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


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

__SCREAMING_SNAKE_CASE     :				int             =       {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple=5_0_2_5_7  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Union[str, Any]=7_6_8  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Tuple="gelu_new"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=1E-5  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Tuple="cls_index"  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Tuple=5_0_2_5_6  ,					lowerCamelCase__      :		Optional[int]=5_0_2_5_6  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Tuple=False  ,					**lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       vocab_size
							lowerCAmelCase						:   Dict							=       n_positions
							lowerCAmelCase						:   int							=       n_embd
							lowerCAmelCase						:   Union[str, Any]							=       n_layer
							lowerCAmelCase						:   List[str]							=       n_head
							lowerCAmelCase						:   str							=       n_inner
							lowerCAmelCase						:   Tuple							=       activation_function
							lowerCAmelCase						:   List[Any]							=       resid_pdrop
							lowerCAmelCase						:   Any							=       embd_pdrop
							lowerCAmelCase						:   int							=       attn_pdrop
							lowerCAmelCase						:   Dict							=       layer_norm_epsilon
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       summary_type
							lowerCAmelCase						:   int							=       summary_use_proj
							lowerCAmelCase						:   List[str]							=       summary_activation
							lowerCAmelCase						:   List[str]							=       summary_first_dropout
							lowerCAmelCase						:   Optional[int]							=       summary_proj_to_labels
							lowerCAmelCase						:   Optional[int]							=       scale_attn_weights
							lowerCAmelCase						:   List[str]							=       use_cache
							lowerCAmelCase						:   List[Any]							=       scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:   int							=       reorder_and_upcast_attn

							lowerCAmelCase						:   List[Any]							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       eos_token_id

							super().__init__(bos_token_id=lowerCamelCase__  ,					eos_token_id=lowerCamelCase__  ,					**lowerCamelCase__			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		PretrainedConfig  ,					lowerCamelCase__      :		str = "default"  ,					lowerCamelCase__      :		List[PatchingSpec] = None  ,					lowerCamelCase__      :		bool = False  ,					):
							super().__init__(lowerCamelCase__  ,					task=lowerCamelCase__  ,					patching_specs=lowerCamelCase__  ,					use_past=lowerCamelCase__			)
							if not getattr(self._config  ,					'''pad_token_id'''  ,					lowerCamelCase__			):
												# TODO: how to do that better?
												lowerCAmelCase						:   str							=       0





		@property
		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[Any]							=       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__  ,					direction='''inputs'''			)
												lowerCAmelCase						:   str							=       {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase						:   int							=       {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def  _A							(							self      :		Optional[int]			):
							return self._config.n_layer





		@property
		def  _A							(							self      :		List[str]			):
							return self._config.n_head





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		PreTrainedTokenizer  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		bool = False  ,					lowerCamelCase__      :		Optional[TensorType] = None  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       super(lowerCamelCase__  ,					self			).generate_dummy_inputs(
							    lowerCamelCase__  ,					batch_size=lowerCamelCase__  ,					seq_length=lowerCamelCase__  ,					is_pair=lowerCamelCase__  ,					framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase						:   Tuple							=       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						:   Any							=       common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	lowerCAmelCase						:   Optional[int]							=       seqlen + 2
																	lowerCAmelCase						:   Any							=       (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	lowerCAmelCase						:   Any							=       [
																	    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																	]

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

							return ordered_inputs





		@property
		def  _A							(							self      :		Union[str, Any]			):
							return 1_3
