



'''simple docstring'''





from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


lowercase_		       =  logging.get_logger(__name__)


lowercase_		       =  OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

lowercase_		       =  OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

lowercase_		       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_		       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_		       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_		       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_		       =  _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       FLAX_MODEL_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModel)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       FLAX_MODEL_FOR_PRETRAINING_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       FLAX_MODEL_FOR_MASKED_LM_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       int          =       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING





lowercase_		       =  auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")






class __A			(  _BaseAutoModelClass						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING





lowercase_		       =  auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)








'''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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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 __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



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

      _a      =  bos_token_id
      _a      =  eos_token_id

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






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



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



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


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



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


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  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

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

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



