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_:      str        =			logging.get_logger(__name__)

A_:      Optional[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 _lowercase					(       __UpperCAmelCase						):





							"""simple docstring"""




							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     ,   UpperCAmelCase=50257     ,   UpperCAmelCase=1024     ,   UpperCAmelCase=768     ,   UpperCAmelCase=12     ,   UpperCAmelCase=12     ,   UpperCAmelCase=None     ,   UpperCAmelCase="gelu_new"     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=1e-5     ,   UpperCAmelCase=0.02     ,   UpperCAmelCase="cls_index"     ,   UpperCAmelCase=True     ,   UpperCAmelCase=None     ,   UpperCAmelCase=True     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase=50256     ,   UpperCAmelCase=50256     ,   UpperCAmelCase=False     ,   UpperCAmelCase=False     ,   **UpperCAmelCase     ,   ):







														'''simple docstring'''






														_lowercase				=	vocab_size
														_lowercase				=	n_positions
														_lowercase				=	n_embd
														_lowercase				=	n_layer
														_lowercase				=	n_head
														_lowercase				=	n_inner
														_lowercase				=	activation_function
														_lowercase				=	resid_pdrop
														_lowercase				=	embd_pdrop
														_lowercase				=	attn_pdrop
														_lowercase				=	layer_norm_epsilon
														_lowercase				=	initializer_range
														_lowercase				=	summary_type
														_lowercase				=	summary_use_proj
														_lowercase				=	summary_activation
														_lowercase				=	summary_first_dropout
														_lowercase				=	summary_proj_to_labels
														_lowercase				=	scale_attn_weights
														_lowercase				=	use_cache
														_lowercase				=	scale_attn_by_inverse_layer_idx
														_lowercase				=	reorder_and_upcast_attn

														_lowercase				=	bos_token_id
														_lowercase				=	eos_token_id

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






class _lowercase					(       __UpperCAmelCase						):





							"""simple docstring"""




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







														'''simple docstring'''






														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?
																					_lowercase				=	0



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






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

														return common_inputs



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return self._config.n_layer



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return self._config.n_head



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = -1     ,   UpperCAmelCase = -1     ,   UpperCAmelCase = False     ,   UpperCAmelCase = None     ,   ):







														'''simple docstring'''






														_lowercase				=	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()
														_lowercase				=	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

																												_lowercase				,       _lowercase				=	common_inputs["""input_ids"""].shape
																												# Not using the same length for past_key_values
																												_lowercase				=	seqlen + 2
																												_lowercase				=	(
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												_lowercase				=	[
																												    (torch.zeros(lowerCamelCase__  ), torch.zeros(lowerCamelCase__  )) for _ in range(self.num_layers  )
																												]

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

														return ordered_inputs



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return 13



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_:      int        =			{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      List[str]        =			['BartTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      int        =			[
				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BartForCausalLM',
				    'BartForConditionalGeneration',
				    'BartForQuestionAnswering',
				    'BartForSequenceClassification',
				    'BartModel',
				    'BartPretrainedModel',
				    'PretrainedBartModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      Any        =			[
				    'TFBartForConditionalGeneration',
				    'TFBartForSequenceClassification',
				    'TFBartModel',
				    'TFBartPretrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      str        =			[
				    'FlaxBartDecoderPreTrainedModel',
				    'FlaxBartForCausalLM',
				    'FlaxBartForConditionalGeneration',
				    'FlaxBartForQuestionAnswering',
				    'FlaxBartForSequenceClassification',
				    'FlaxBartModel',
				    'FlaxBartPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


