





'''simple docstring'''


import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

_SCREAMING_SNAKE_CASE									=				[
    "word_embeddings_layernorm.weight",
    "word_embeddings_layernorm.bias",
    "input_layernorm.weight",
    "input_layernorm.bias",
    "post_attention_layernorm.weight",
    "post_attention_layernorm.bias",
    "self_attention.dense.bias",
    "mlp.dense_4h_to_h.bias",
    "ln_f.weight",
    "ln_f.bias",
]

_SCREAMING_SNAKE_CASE									=				[
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]


def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {
        "word_embeddings.weight": "word_embeddings.weight",
        "word_embeddings.norm.weight": "word_embeddings_layernorm.weight",
        "word_embeddings.norm.bias": "word_embeddings_layernorm.bias",
        "weight": "ln_f.weight",
        "bias": "ln_f.bias",
    }

    if key in layer_rename_map:
        return layer_rename_map[key]

    # Handle transformer blocks
    _lowerCAmelCase						      =   int(re.match(R".*layer_(\d*).*"		,							SCREAMING_SNAKE_CASE_     )[1]     )
    layer_number -= 3
    return F'''h.{layer_number}.''' + key


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    if dtype == torch.bool:
        return 1 / 8
    _lowerCAmelCase						      =   re.search(R"[^\d](\d+)$"		,							str(SCREAMING_SNAKE_CASE_     )     )
    if bit_search is None:
        raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.'''     )
    _lowerCAmelCase						      =   int(bit_search.groups()[0]     )
    return bit_size // 8


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    if bloom_config_file == "":
        _lowerCAmelCase						      =   BloomConfig()
    else:
        _lowerCAmelCase						      =   BloomConfig.from_json_file(SCREAMING_SNAKE_CASE_     )

    if shard_model:
        _lowerCAmelCase						      =   os.listdir(SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   sorted(filter(lambda SCREAMING_SNAKE_CASE_     : s.startswith("layer"     ) and "model_00" in s		,							SCREAMING_SNAKE_CASE_     )     )

        _lowerCAmelCase						      =   {"weight_map": {}, "metadata": {}}
        _lowerCAmelCase						      =   0

        _lowerCAmelCase						      =   None

        _lowerCAmelCase						      =   BloomConfig()

        for j, file in enumerate(SCREAMING_SNAKE_CASE_     ):
            print("Processing file: {}".format(SCREAMING_SNAKE_CASE_     )     )
            _lowerCAmelCase						      =   None

            for i in range(SCREAMING_SNAKE_CASE_     ):
                # load all TP files
                _lowerCAmelCase						      =   file.replace("model_00"		,							F'''model_0{i}'''     )
                _lowerCAmelCase						      =   torch.load(os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )		,							map_location="cpu"     )

                # Rename keys in the transformers names
                _lowerCAmelCase						      =   list(temp.keys()     )
                for key in keys:
                    _lowerCAmelCase						      =   temp.pop(SCREAMING_SNAKE_CASE_     )

                if tensors is None:
                    _lowerCAmelCase						      =   temp
                else:
                    for key in tensors.keys():
                        if any(key.endswith(SCREAMING_SNAKE_CASE_     ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH     ):
                            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            _lowerCAmelCase						      =   1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN     ) else 0
                            # We concatenate these weights accross TP ranks
                            _lowerCAmelCase						      =   torch.cat([tensors[key], temp[key]]		,							dim=SCREAMING_SNAKE_CASE_     )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(SCREAMING_SNAKE_CASE_     ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH     ):
                    _lowerCAmelCase						      =   tensors[key] / pretraining_tp
            torch.save(
                SCREAMING_SNAKE_CASE_		,							os.path.join(
                    SCREAMING_SNAKE_CASE_		,							"pytorch_model_{}-of-{}.bin".format(str(j + 1     ).zfill(5     )		,							str(len(SCREAMING_SNAKE_CASE_     )     ).zfill(5     )     )		,							)		,							)

            for key in tensors.keys():
                _lowerCAmelCase						      =   tensors[key]
                total_size += value.numel() * get_dtype_size(value.dtype     )
                if key not in index_dict["weight_map"]:
                    _lowerCAmelCase						      =   "pytorch_model_{}-of-{}.bin".format(
                        str(j + 1     ).zfill(5     )		,							str(len(SCREAMING_SNAKE_CASE_     )     ).zfill(5     )     )

        _lowerCAmelCase						      =   BloomConfig()
        _lowerCAmelCase						      =   pytorch_dump_folder_path + "/" + CONFIG_NAME
        _lowerCAmelCase						      =   total_size
        with open(SCREAMING_SNAKE_CASE_		,							"w"		,							encoding="utf-8"     ) as f:
            f.write(config.to_json_string()     )
        with open(os.path.join(SCREAMING_SNAKE_CASE_		,							WEIGHTS_NAME + ".index.json"     )		,							"w"		,							encoding="utf-8"     ) as f:
            _lowerCAmelCase						      =   json.dumps(SCREAMING_SNAKE_CASE_		,							indent=2		,							sort_keys=SCREAMING_SNAKE_CASE_     ) + "\n"
            f.write(SCREAMING_SNAKE_CASE_     )
    else:
        _lowerCAmelCase						      =   BloomModel(SCREAMING_SNAKE_CASE_     )

        _lowerCAmelCase						      =   os.listdir(SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   sorted(filter(lambda SCREAMING_SNAKE_CASE_     : s.startswith("layer"     ) and "model_00" in s		,							SCREAMING_SNAKE_CASE_     )     )

        _lowerCAmelCase						      =   None
        for i, file in enumerate(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   None
            for i in range(SCREAMING_SNAKE_CASE_     ):
                # load all TP files
                _lowerCAmelCase						      =   file.replace("model_00"		,							F'''model_0{i}'''     )
                _lowerCAmelCase						      =   torch.load(os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )		,							map_location="cpu"     )

                # Rename keys in the transformers names
                _lowerCAmelCase						      =   list(temp.keys()     )
                for key in keys:
                    _lowerCAmelCase						      =   temp.pop(SCREAMING_SNAKE_CASE_     )

                if tensors is None:
                    _lowerCAmelCase						      =   temp
                else:
                    for key in tensors.keys():
                        # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                        if any(key.endswith(SCREAMING_SNAKE_CASE_     ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH     ):
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            _lowerCAmelCase						      =   1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN     ) else 0
                            # We concatenate these weights accross TP ranks
                            _lowerCAmelCase						      =   torch.cat([tensors[key], temp[key]]		,							dim=SCREAMING_SNAKE_CASE_     )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(SCREAMING_SNAKE_CASE_     ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH     ):
                    _lowerCAmelCase						      =   tensors[key] / pretraining_tp

            _lowerCAmelCase						      =   model.load_state_dict(SCREAMING_SNAKE_CASE_		,							strict=SCREAMING_SNAKE_CASE_     )
            assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
            if missing_keys is None:
                _lowerCAmelCase						      =   set(other_keys.missing_keys     )
            else:
                _lowerCAmelCase						      =   missing_keys.intersection(set(other_keys.missing_keys     )     )

        assert not missing_keys, F'''The keys {missing_keys} are missing'''

        # Save pytorch-model
        os.makedirs(SCREAMING_SNAKE_CASE_		,							exist_ok=SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
        _lowerCAmelCase						      =   pytorch_dump_folder_path + "/" + CONFIG_NAME
        print(F'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'''     )
        if config.torch_dtype is not None:
            _lowerCAmelCase						      =   model.to(config.torch_dtype     )
        torch.save(model.state_dict()		,							SCREAMING_SNAKE_CASE_     )
        print(F'''Save configuration file to {pytorch_config_dump_path}'''     )
        with open(SCREAMING_SNAKE_CASE_		,							"w"		,							encoding="utf-8"     ) as f:
            f.write(config.to_json_string()     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--bloom_checkpoint_path",
           default=None,
           type=str,
           required=True,
           help="Path to the Megatron-LM checkpoint path.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--bloom_config_file",
           default="",
           type=str,
           help=(
               "An optional config json file corresponding to the pre-trained model. \n"
               "This specifies the model architecture."
           ),
       )
       parser.add_argument(
           "--shard_model",
           action="store_true",
           help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
       )
       parser.add_argument(
           "--pretraining_tp",
           default=4,
           type=int,
           help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
       )
       _SCREAMING_SNAKE_CASE									=				parser.parse_args()
       convert_bloom_checkpoint_to_pytorch(
           args.bloom_checkpoint_path,
           args.bloom_config_file,
           args.pytorch_dump_folder_path,
           args.shard_model,
           args.pretraining_tp,
       )









'''simple docstring'''


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

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "blenderbot-small"
 __lowerCamelCase    :				Optional[Any]								=       ["past_key_values"]
 __lowerCamelCase    :				str								=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   encoder_ffn_dim
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   encoder_attention_heads
     _lowerCAmelCase						      =   decoder_ffn_dim
     _lowerCAmelCase						      =   decoder_layers
     _lowerCAmelCase						      =   decoder_attention_heads
     _lowerCAmelCase						      =   dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   init_std
     _lowerCAmelCase						      =   encoder_layerdrop
     _lowerCAmelCase						      =   decoder_layerdrop
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  is_encoder_decoder=_lowerCAmelCase      ,  decoder_start_token_id=_lowerCAmelCase      ,  forced_eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )







class  lowerCAmelCase_		(		__magic_name__		):




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

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

         if self.use_past:
             self.fill_with_past_key_values_(_lowerCAmelCase      ,  direction="inputs"		)
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     else:
         _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super().outputs
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		).outputs
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

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

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

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

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 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
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  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
     _lowerCAmelCase						      =   tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_sequence      ,  num_token_to_add=_lowerCAmelCase		)

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




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

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

     return common_inputs





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super()._flatten_past_key_values_(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		)._flatten_past_key_values_(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)


