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

from packaging import version


if TYPE_CHECKING:
      from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowercase__       =logging.get_logger(__name__)

lowercase__       ={
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Dict	       =      "bloom"
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE      :							int	       =      {
       "num_hidden_layers": "n_layer",
       "num_attention_heads": "n_head",
   }
   def __init__(self :					Dict					,				snake_case_ :					str=2_5_0_8_8_0					,				snake_case_ :					Dict=6_4					,				snake_case_ :					Optional[int]=2					,				snake_case_ :					int=8					,				snake_case_ :					Dict=1E-5					,				snake_case_ :					Optional[int]=0.02					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					Any=1					,				snake_case_ :					int=2					,				snake_case_ :					Optional[int]=False					,				snake_case_ :					Union[str, Any]=0.0					,				snake_case_ :					Optional[int]=0.0					,				snake_case_ :					List[Any]=1					,				snake_case_ :					List[Any]=False					,				**snake_case_ :					List[Any]					,				):
    __a :  Union[str, Any]						=  vocab_size
    # Backward compatibility with n_embed kwarg
    __a :  Union[str, Any]						=  kwargs.pop('''n_embed'''					,				snake_case_   )
    __a :  List[str]						=  hidden_size if n_embed is None else n_embed
    __a :  Optional[Any]						=  n_layer
    __a :  Optional[Any]						=  n_head
    __a :  Union[str, Any]						=  layer_norm_epsilon
    __a :  Dict						=  initializer_range
    __a :  Union[str, Any]						=  use_cache
    __a :  Tuple						=  pretraining_tp
    __a :  str						=  apply_residual_connection_post_layernorm
    __a :  Union[str, Any]						=  hidden_dropout
    __a :  Optional[int]						=  attention_dropout

    __a :  str						=  bos_token_id
    __a :  List[Any]						=  eos_token_id
    __a :  List[Any]						=  slow_but_exact

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





class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      version.parse("1.12"    )
   def __init__(self :					List[Any]					,				snake_case_ :					PretrainedConfig					,				snake_case_ :					str = "default"					,				snake_case_ :					List[PatchingSpec] = None					,				snake_case_ :					bool = False					,				):
    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?
     __a :  Optional[int]						=  0
   @property
   def       lowerCAmelCase			(self :					List[str]   ):
    __a :  Union[str, Any]						=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
    if self.use_past:
     # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
     self.fill_with_past_key_values_(snake_case_					,				direction='''inputs'''					,				inverted_values_shape=snake_case_   )
     __a :  List[str]						=  {0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
     __a :  Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}

    return common_inputs
   @property
   def       lowerCAmelCase			(self :					Any   ):
    return self._config.n_layer
   @property
   def       lowerCAmelCase			(self :					str   ):
    return self._config.n_head
   @property
   def       lowerCAmelCase			(self :					str   ):
    return 1E-3
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					"PreTrainedTokenizer"					,				snake_case_ :					int = -1					,				snake_case_ :					int = -1					,				snake_case_ :					bool = False					,				snake_case_ :					Optional["TensorType"] = None					,				):
    __a :  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()
    __a :  int						=  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 :  Any						=  common_inputs['''input_ids'''].shape
      # Not using the same length for past_key_values
      __a :  int						=  seqlen + 2
      __a :  Dict						=  self._config.hidden_size // self.num_attention_heads
      __a :  Any						=  (
          batch * self.num_attention_heads,
          head_dim,
          past_key_values_length,
      )
      __a :  List[str]						=  (
          batch * self.num_attention_heads,
          past_key_values_length,
          head_dim,
      )
      __a :  Dict						=  [
          (torch.zeros(snake_case_   ), torch.zeros(snake_case_   )) for _ in range(self.num_layers   )
      ]

    __a :  Optional[int]						=  common_inputs['''attention_mask''']
    if self.use_past:
     __a :  List[Any]						=  ordered_inputs['''attention_mask'''].dtype
     __a :  str						=  torch.cat(
         [ordered_inputs['''attention_mask'''], torch.ones(snake_case_					,				snake_case_					,				dtype=snake_case_   )]					,				dim=1   )

    return ordered_inputs







   @property
   def       lowerCAmelCase			(self :					List[Any]   ):
    return 1_3


from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      lowercase__       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
