


"""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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Any   ="gptj"
       a  :				Any   ={
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

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






         """simple docstring"""





         lowerCAmelCase     :     Any								=							vocab_size
         lowerCAmelCase     :     Tuple								=							n_positions
         lowerCAmelCase     :     List[Any]								=							n_embd
         lowerCAmelCase     :     Any								=							n_layer
         lowerCAmelCase     :     List[Any]								=							n_head
         lowerCAmelCase     :     Optional[int]								=							n_inner
         lowerCAmelCase     :     List[str]								=							rotary_dim
         lowerCAmelCase     :     Dict								=							activation_function
         lowerCAmelCase     :     Dict								=							resid_pdrop
         lowerCAmelCase     :     List[Any]								=							embd_pdrop
         lowerCAmelCase     :     List[str]								=							attn_pdrop
         lowerCAmelCase     :     Optional[int]								=							layer_norm_epsilon
         lowerCAmelCase     :     Optional[int]								=							initializer_range
         lowerCAmelCase     :     int								=							use_cache

         lowerCAmelCase     :     Dict								=							bos_token_id
         lowerCAmelCase     :     List[str]								=							eos_token_id

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


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


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






         """simple docstring"""





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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
         if self.use_past:
           self.fill_with_past_key_values_(snake_case__		,     direction="inputs"						)
           lowerCAmelCase     :     int								=							{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowerCAmelCase     :     Optional[int]								=							{0: "batch", 1: "sequence"}

         return common_inputs

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_layer

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_head

       def        lowercase__							(						self		,     snake_case__		,     snake_case__ = -1		,     snake_case__ = -1		,     snake_case__ = False		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							super(snake_case__		,     self						).generate_dummy_inputs(
             snake_case__		,     batch_size=snake_case__		,     seq_length=snake_case__		,     is_pair=snake_case__		,     framework=snake_case__						)

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

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

         return ordered_inputs







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 13









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase	:						str       =	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						str       =	[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	__lowercase	:						Tuple       =	[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	__lowercase	:						Dict       =	[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	__lowercase	:						Optional[Any]       =	[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						int       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



