
'''simple docstring'''



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__ :						Dict    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Optional[Any]    							=					{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 				_a			(SCREAMING_SNAKE_CASE_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					"mbart"
       SCREAMING_SNAKE_CASE =					["past_key_values"]
       SCREAMING_SNAKE_CASE =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






       def __init__(							self    ,							A__=5_02_65    ,							A__=10_24    ,							A__=12    ,							A__=40_96    ,							A__=16    ,							A__=12    ,							A__=40_96    ,							A__=16    ,							A__=0.0    ,							A__=0.0    ,							A__=True    ,							A__=True    ,							A__="gelu"    ,							A__=10_24    ,							A__=0.1    ,							A__=0.0    ,							A__=0.0    ,							A__=0.02    ,							A__=0.0    ,							A__=False    ,							A__=1    ,							A__=0    ,							A__=2    ,							A__=2    ,							**A__    ,							)      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			max_position_embeddings
          _SCREAMING_SNAKE_CASE       	=			d_model
          _SCREAMING_SNAKE_CASE       	=			encoder_ffn_dim
          _SCREAMING_SNAKE_CASE       	=			encoder_layers
          _SCREAMING_SNAKE_CASE       	=			encoder_attention_heads
          _SCREAMING_SNAKE_CASE       	=			decoder_ffn_dim
          _SCREAMING_SNAKE_CASE       	=			decoder_layers
          _SCREAMING_SNAKE_CASE       	=			decoder_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dropout
          _SCREAMING_SNAKE_CASE       	=			attention_dropout
          _SCREAMING_SNAKE_CASE       	=			activation_dropout
          _SCREAMING_SNAKE_CASE       	=			activation_function
          _SCREAMING_SNAKE_CASE       	=			init_std
          _SCREAMING_SNAKE_CASE       	=			encoder_layerdrop
          _SCREAMING_SNAKE_CASE       	=			decoder_layerdrop
          _SCREAMING_SNAKE_CASE       	=			classifier_dropout
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			encoder_layers
          _SCREAMING_SNAKE_CASE       	=			scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=lowercase_    ,							bos_token_id=lowercase_    ,							eos_token_id=lowercase_    ,							is_encoder_decoder=lowercase_    ,							forced_eos_token_id=lowercase_    ,							**lowercase_    ,							)






class 				_a			(SCREAMING_SNAKE_CASE_):





       """simple docstring"""




       @property
       def     UpperCamelCase			(							self )      ->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ] )

             if self.use_past:
                _SCREAMING_SNAKE_CASE       	=			{0: """batch"""}
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """decoder_sequence"""}
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                self.fill_with_past_key_values_(lowercase_    ,							direction="""inputs""" )
          elif self.task == "causal-lm":
             # TODO: figure this case out.
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ] )
             if self.use_past:
                _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
                for i in range(lowercase_ ):
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                     ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                 ] )

          return common_inputs






       @property
       def     UpperCamelCase			(							self )      ->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			super().outputs
          else:
             _SCREAMING_SNAKE_CASE       	=			super(lowercase_    ,							self ).outputs
             if self.use_past:
                _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
                for i in range(lowercase_ ):
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_ )

          # Generate decoder inputs
          _SCREAMING_SNAKE_CASE       	=			seq_length if not self.use_past else 1
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_ )
          _SCREAMING_SNAKE_CASE       	=			{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          _SCREAMING_SNAKE_CASE       	=			dict(**lowercase_    ,							**lowercase_ )

          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       	=			common_inputs["""input_ids"""].shape
             _SCREAMING_SNAKE_CASE       	=			common_inputs["""decoder_input_ids"""].shape[1]
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_attention_heads
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             _SCREAMING_SNAKE_CASE       	=			decoder_seq_length + 3
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             _SCREAMING_SNAKE_CASE       	=			torch.cat(
                 [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_    ,							lowercase_ )]    ,							dim=1 )

             _SCREAMING_SNAKE_CASE       	=			[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
             _SCREAMING_SNAKE_CASE       	=			min(lowercase_    ,							lowercase_ )
             _SCREAMING_SNAKE_CASE       	=			max(lowercase_    ,							lowercase_ ) - min_num_layers
             _SCREAMING_SNAKE_CASE       	=			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

             for _ in range(lowercase_ ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(lowercase_ ),
                        torch.zeros(lowercase_ ),
                        torch.zeros(lowercase_ ),
                        torch.zeros(lowercase_ ),
                    ) )
             # TODO: test this.
             _SCREAMING_SNAKE_CASE       	=			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
             for _ in range(lowercase_    ,							lowercase_ ):
                common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_ )

          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       	=			common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             _SCREAMING_SNAKE_CASE       	=			seqlen + 2
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_attention_heads
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             _SCREAMING_SNAKE_CASE       	=			common_inputs["""attention_mask"""].dtype
             _SCREAMING_SNAKE_CASE       	=			torch.cat(
                 [common_inputs["""attention_mask"""], torch.ones(lowercase_    ,							lowercase_    ,							dtype=lowercase_ )]    ,							dim=1 )
             _SCREAMING_SNAKE_CASE       	=			[
                 (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
             ]
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          _SCREAMING_SNAKE_CASE       	=			compute_effective_axis_dimension(
              lowercase_    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0 )

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          _SCREAMING_SNAKE_CASE       	=			tokenizer.num_special_tokens_to_add(lowercase_ )
          _SCREAMING_SNAKE_CASE       	=			compute_effective_axis_dimension(
              lowercase_    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=lowercase_ )

          # Generate dummy inputs according to compute batch and sequence
          _SCREAMING_SNAKE_CASE       	=			[""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
          _SCREAMING_SNAKE_CASE       	=			dict(tokenizer(lowercase_    ,							return_tensors=lowercase_ ) )
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 lowercase_    ,							batch_size=lowercase_    ,							seq_length=lowercase_    ,							is_pair=lowercase_    ,							framework=lowercase_ )

          elif self.task == "causal-lm":
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_causal_lm(
                 lowercase_    ,							batch_size=lowercase_    ,							seq_length=lowercase_    ,							is_pair=lowercase_    ,							framework=lowercase_ )
          else:
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_    ,							batch_size=lowercase_    ,							seq_length=lowercase_    ,							is_pair=lowercase_    ,							framework=lowercase_ )

          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			super()._flatten_past_key_values_(lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_ )
          else:
             _SCREAMING_SNAKE_CASE       	=			super(lowercase_    ,							self )._flatten_past_key_values_(
                 lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_ )








'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase__ :						List[str]    							=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Optional[Any]    							=					[
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
         "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
         "TFGPTJForSequenceClassification",
         "TFGPTJModel",
         "TFGPTJPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					[
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
         "FlaxGPTJPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase__ :						Any    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






