
"""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


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

UpperCAmelCase__      :    Union[str, Any]    							= {
    '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 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   '''gptj'''
				__UpperCamelCase      :						Optional[int]					       =   {
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=5_04_00   ,       SCREAMING_SNAKE_CASE__=20_48   ,       SCREAMING_SNAKE_CASE__=40_96   ,       SCREAMING_SNAKE_CASE__=28   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=64   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__="gelu_new"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=1E-5   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       SCREAMING_SNAKE_CASE__=False   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   n_positions
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   n_embd
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   n_layer
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   n_head
											SCREAMING_SNAKE_CASE__						:       Any           =   n_inner
											SCREAMING_SNAKE_CASE__						:       str           =   rotary_dim
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   activation_function
											SCREAMING_SNAKE_CASE__						:       Tuple           =   resid_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   embd_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   attn_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   layer_norm_epsilon
											SCREAMING_SNAKE_CASE__						:       Any           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Tuple           =   use_cache

											SCREAMING_SNAKE_CASE__						:       Dict           =   bos_token_id
											SCREAMING_SNAKE_CASE__						:       str           =   eos_token_id

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






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = "default"   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		str:



											"""simple docstring"""




											super().__init__(SCREAMING_SNAKE_CASE__   ,       task=SCREAMING_SNAKE_CASE__   ,       patching_specs=SCREAMING_SNAKE_CASE__   ,       use_past=SCREAMING_SNAKE_CASE__    )
											if not getattr(self._config   ,       """pad_token_id"""   ,       SCREAMING_SNAKE_CASE__    ):
																		# TODO: how to do that better?
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   0



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
											if self.use_past:
																		self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__   ,       direction="""inputs"""    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {0: """batch""", 1: """past_sequence + sequence"""}
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {0: """batch""", 1: """sequence"""}

											return common_inputs



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return self._config.n_layer



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return self._config.n_head



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   super(SCREAMING_SNAKE_CASE__   ,       self    ).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE__   ,       batch_size=SCREAMING_SNAKE_CASE__   ,       seq_length=SCREAMING_SNAKE_CASE__   ,       is_pair=SCREAMING_SNAKE_CASE__   ,       framework=SCREAMING_SNAKE_CASE__    )

											# We need to order the input in the way they appears in the forward()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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

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

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

											return ordered_inputs



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return 13











'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase_					= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      lowercase_					= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



