from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

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


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

__SCREAMING_SNAKE_CASE						:   int         =			{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}



class        lowercase_   (					_lowerCAmelCase		):
							_lowerCamelCase						=		"bloom"
							_lowerCamelCase						=		["past_key_values"]
							_lowerCamelCase						=		{
							    "num_hidden_layers": "n_layer",
							    "num_attention_heads": "n_head",
							}



							def __init__(						self				,    lowercase_=250_880				,    lowercase_=64				,    lowercase_=2				,    lowercase_=8				,    lowercase_=1e-5				,    lowercase_=0.02				,    lowercase_=True				,    lowercase_=1				,    lowercase_=2				,    lowercase_=False				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=1				,    lowercase_=False				,    **lowercase_				,    ):
													_snake_case						:					List[str]    =   vocab_size
													# Backward compatibility with n_embed kwarg
													_snake_case						:					int    =   kwargs.pop("n_embed"				,    _lowercase    )
													_snake_case						:					Any    =   hidden_size if n_embed is None else n_embed
													_snake_case						:					Any    =   n_layer
													_snake_case						:					Optional[Any]    =   n_head
													_snake_case						:					List[str]    =   layer_norm_epsilon
													_snake_case						:					Dict    =   initializer_range
													_snake_case						:					Optional[Any]    =   use_cache
													_snake_case						:					str    =   pretraining_tp
													_snake_case						:					Optional[int]    =   apply_residual_connection_post_layernorm
													_snake_case						:					Any    =   hidden_dropout
													_snake_case						:					int    =   attention_dropout

													_snake_case						:					int    =   bos_token_id
													_snake_case						:					Union[str, Any]    =   eos_token_id
													_snake_case						:					List[str]    =   slow_but_exact

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




class        lowercase_   (					_lowerCAmelCase		):
							_lowerCamelCase						=		version.parse('1.12'		)



							def __init__(						self				,    lowercase_				,    lowercase_ = "default"				,    lowercase_ = None				,    lowercase_ = False				,    ):
													super().__init__(_lowercase				,    task=_lowercase				,    patching_specs=_lowercase				,    use_past=_lowercase    )
													if not getattr(self._config				,    "pad_token_id"				,    _lowercase    ):
																			# TODO: how to do that better?
																			_snake_case						:					Optional[Any]    =   0



							@property
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
													if self.use_past:
																			# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																			self.fill_with_past_key_values_(_lowercase				,    direction="inputs"				,    inverted_values_shape=_lowercase    )
																			_snake_case						:					Tuple    =   {0: "batch", 1: "past_sequence + sequence"}
													else:
																			_snake_case						:					Optional[Any]    =   {0: "batch", 1: "sequence"}

													return common_inputs



							@property
							def 	UpperCamelCase	(						self    ):
													return self._config.n_layer



							@property
							def 	UpperCamelCase	(						self    ):
													return self._config.n_head



							@property
							def 	UpperCamelCase	(						self    ):
													return 1e-3



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    ):
													_snake_case						:					List[Any]    =   super(_lowercase				,    self    ).generate_dummy_inputs(
													    _lowercase				,    batch_size=_lowercase				,    seq_length=_lowercase				,    is_pair=_lowercase				,    framework=_lowercase    )

													# We need to order the input in the way they appears in the forward()
													_snake_case						:					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

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

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

													return ordered_inputs



							@property
							def 	UpperCamelCase	(						self    ):
													return 13
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE						:   int         =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'bertabs'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=512				,    lowercase_=6				,    lowercase_=512				,    lowercase_=8				,    lowercase_=512				,    lowercase_=0.2				,    lowercase_=6				,    lowercase_=768				,    lowercase_=8				,    lowercase_=2_048				,    lowercase_=0.2				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					int    =   max_pos

													_snake_case						:					Tuple    =   enc_layers
													_snake_case						:					Optional[Any]    =   enc_hidden_size
													_snake_case						:					Union[str, Any]    =   enc_heads
													_snake_case						:					str    =   enc_ff_size
													_snake_case						:					Any    =   enc_dropout

													_snake_case						:					Tuple    =   dec_layers
													_snake_case						:					Optional[Any]    =   dec_hidden_size
													_snake_case						:					Dict    =   dec_heads
													_snake_case						:					str    =   dec_ff_size
													_snake_case						:					List[str]    =   dec_dropout