






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


a_							=    logging.get_logger(__name__)

a_							=    {
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}


class      lowercase__     (			_UpperCAmelCase	):
	a_         ="""nllb-moe"""
	a_         =["""past_key_values"""]
	a_         ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

	def __init__(							self		,    __UpperCAmelCase=128112		,    __UpperCAmelCase=1024		,    __UpperCAmelCase=12		,    __UpperCAmelCase=4096		,    __UpperCAmelCase=16		,    __UpperCAmelCase=12		,    __UpperCAmelCase=4096		,    __UpperCAmelCase=16		,    __UpperCAmelCase=0.05		,    __UpperCAmelCase=0.05		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="relu"		,    __UpperCAmelCase=1024		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=2		,    __UpperCAmelCase=True		,    __UpperCAmelCase=False		,    __UpperCAmelCase="float32"		,    __UpperCAmelCase=False		,    __UpperCAmelCase=128		,    __UpperCAmelCase=64		,    __UpperCAmelCase=4		,    __UpperCAmelCase=4		,    __UpperCAmelCase=0.001		,    __UpperCAmelCase=0.001		,    __UpperCAmelCase="all"		,    __UpperCAmelCase=False		,    __UpperCAmelCase=False		,    __UpperCAmelCase=1.0		,    __UpperCAmelCase=0.2		,    __UpperCAmelCase=1		,    __UpperCAmelCase=0		,    __UpperCAmelCase=2		,    __UpperCAmelCase=False		,    **__UpperCAmelCase		,    )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						max_position_embeddings
							lowerCAmelCase__       =						d_model
							lowerCAmelCase__       =						encoder_ffn_dim
							lowerCAmelCase__       =						encoder_layers
							lowerCAmelCase__       =						encoder_attention_heads
							lowerCAmelCase__       =						decoder_ffn_dim
							lowerCAmelCase__       =						decoder_layers
							lowerCAmelCase__       =						decoder_attention_heads
							lowerCAmelCase__       =						dropout
							lowerCAmelCase__       =						attention_dropout
							lowerCAmelCase__       =						activation_dropout
							lowerCAmelCase__       =						activation_function
							lowerCAmelCase__       =						init_std
							lowerCAmelCase__       =						encoder_layerdrop
							lowerCAmelCase__       =						decoder_layerdrop
							lowerCAmelCase__       =						use_cache
							lowerCAmelCase__       =						encoder_layers
							lowerCAmelCase__       =						scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase__       =						router_z_loss_coef
							lowerCAmelCase__       =						router_aux_loss_coef
							lowerCAmelCase__       =						decoder_sparse_step
							lowerCAmelCase__       =						encoder_sparse_step
							lowerCAmelCase__       =						num_experts
							lowerCAmelCase__       =						expert_capacity
							lowerCAmelCase__       =						router_bias
							if router_dtype not in ["float32", "float16", "bfloat16"]:
													raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"       )
							lowerCAmelCase__       =						router_dtype

							lowerCAmelCase__       =						router_ignore_padding_tokens
							lowerCAmelCase__       =						batch_prioritized_routing
							lowerCAmelCase__       =						second_expert_policy
							lowerCAmelCase__       =						normalize_router_prob_before_dropping
							lowerCAmelCase__       =						moe_eval_capacity_token_fraction
							lowerCAmelCase__       =						moe_token_dropout
							lowerCAmelCase__       =						output_router_logits
							super().__init__(
							    pad_token_id=__UpperCAmelCase		,    bos_token_id=__UpperCAmelCase		,    eos_token_id=__UpperCAmelCase		,    is_encoder_decoder=__UpperCAmelCase		,    decoder_start_token_id=__UpperCAmelCase		,    **__UpperCAmelCase		,    )














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


a_							=    logging.get_logger(__name__)


a_							=    {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}


class      lowercase__     (			_UpperCAmelCase	):
	a_         ="""codegen"""
	a_         ={
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}

	def __init__(							self		,    __UpperCAmelCase=50400		,    __UpperCAmelCase=2048		,    __UpperCAmelCase=2048		,    __UpperCAmelCase=4096		,    __UpperCAmelCase=28		,    __UpperCAmelCase=16		,    __UpperCAmelCase=64		,    __UpperCAmelCase=None		,    __UpperCAmelCase="gelu_new"		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=1E-5		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=True		,    __UpperCAmelCase=50256		,    __UpperCAmelCase=50256		,    __UpperCAmelCase=False		,    **__UpperCAmelCase		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						n_ctx
							lowerCAmelCase__       =						n_positions
							lowerCAmelCase__       =						n_embd
							lowerCAmelCase__       =						n_layer
							lowerCAmelCase__       =						n_head
							lowerCAmelCase__       =						n_inner
							lowerCAmelCase__       =						rotary_dim
							lowerCAmelCase__       =						activation_function
							lowerCAmelCase__       =						resid_pdrop
							lowerCAmelCase__       =						embd_pdrop
							lowerCAmelCase__       =						attn_pdrop
							lowerCAmelCase__       =						layer_norm_epsilon
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						use_cache

							lowerCAmelCase__       =						bos_token_id
							lowerCAmelCase__       =						eos_token_id

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





class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = "default"		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False		,    )->					Any:







							'''simple docstring'''





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

	@property
	def 		UpperCAmelCase  (							self       )->					Mapping[str, Mapping[int, str]]:







							'''simple docstring'''





							lowerCAmelCase__       =						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
							if self.use_past:
													self.fill_with_past_key_values_(__UpperCAmelCase		,    direction="inputs"       )
													lowerCAmelCase__       =						{0: "batch", 1: "past_sequence + sequence"}
							else:
													lowerCAmelCase__       =						{0: "batch", 1: "sequence"}

							return common_inputs

	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return self._config.n_layer

	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return self._config.n_head

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = -1		,    __UpperCAmelCase = -1		,    __UpperCAmelCase = False		,    __UpperCAmelCase = None		,    )->					Mapping[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						super(__UpperCAmelCase		,    self       ).generate_dummy_inputs(
							    __UpperCAmelCase		,    batch_size=__UpperCAmelCase		,    seq_length=__UpperCAmelCase		,    is_pair=__UpperCAmelCase		,    framework=__UpperCAmelCase       )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase__       =						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__       =						common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			lowerCAmelCase__       =						seqlen + 2
																			lowerCAmelCase__       =						(
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			lowerCAmelCase__       =						[
																			    (torch.zeros(__UpperCAmelCase       ), torch.zeros(__UpperCAmelCase       )) for _ in range(self.num_layers       )
																			]

							lowerCAmelCase__       =						common_inputs["attention_mask"]
							if self.use_past:
													lowerCAmelCase__       =						ordered_inputs["attention_mask"].dtype
													lowerCAmelCase__       =						torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase		,    __UpperCAmelCase		,    dtype=__UpperCAmelCase       )]		,    dim=1       )

							return ordered_inputs


	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return 13






