






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=0       ):
     # Format the message.
     if name is None:
          __lowerCAmelCase								=					None
     else:
          __lowerCAmelCase								=					"." * max(0					, spaces - 2       ) + "# {:" + str(50 - spaces       ) + "s}"
          __lowerCAmelCase								=					fmt.format(__snake_case       )

     # Print and recurse (if needed).
     if isinstance(__snake_case					, __snake_case       ):
          if msg is not None:
               print(__snake_case       )
          for k in val.keys():
               recursive_print(__snake_case					, val[k]					, spaces + 2       )
     elif isinstance(__snake_case					, torch.Tensor       ):
          print(__snake_case					, ":"					, val.size()       )
     else:
          print(__snake_case					, ":"					, __snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace GPT2.
     __lowerCAmelCase								=					param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          __lowerCAmelCase								=					(num_heads, hidden_size, num_splits) + input_shape[1:]
          __lowerCAmelCase								=					param.view(*__snake_case       )
          __lowerCAmelCase								=					param.transpose(0					, 2       )
          __lowerCAmelCase								=					param.transpose(1					, 2       ).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          __lowerCAmelCase								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
          __lowerCAmelCase								=					param.view(*__snake_case       )
          __lowerCAmelCase								=					param.transpose(0					, 1       ).contiguous()
     __lowerCAmelCase								=					param.view(*__snake_case       )
     return param





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     # The converted output model.
     __lowerCAmelCase								=					{}

     # old versions did not store training args
     __lowerCAmelCase								=					input_state_dict.get("args"					, __snake_case       )
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __lowerCAmelCase								=					ds_args.padded_vocab_size
          __lowerCAmelCase								=					ds_args.max_position_embeddings
          __lowerCAmelCase								=					ds_args.hidden_size
          __lowerCAmelCase								=					ds_args.num_layers
          __lowerCAmelCase								=					ds_args.num_attention_heads
          __lowerCAmelCase								=					ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     __lowerCAmelCase								=					config.n_head
     # The hidden_size per head.
     __lowerCAmelCase								=					config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __lowerCAmelCase								=					input_state_dict["checkpoint_version"]
     else:
          __lowerCAmelCase								=					0.0

     # The model.
     __lowerCAmelCase								=					input_state_dict["model"]
     # The language model.
     __lowerCAmelCase								=					model["language_model"]
     # The embeddings.
     __lowerCAmelCase								=					lm["embedding"]

     # The word embeddings.
     __lowerCAmelCase								=					embeddings["word_embeddings"]["weight"]
     # Truncate the embedding table to vocab_size rows.
     __lowerCAmelCase								=					word_embeddings[: config.vocab_size, :]
     __lowerCAmelCase								=					word_embeddings

     # The position embeddings.
     __lowerCAmelCase								=					embeddings["position_embeddings"]["weight"]
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     __lowerCAmelCase								=					pos_embeddings.size(0       )
     if n_positions != config.n_positions:
          raise ValueError(
              F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""       )
     # Store the position embeddings.
     __lowerCAmelCase								=					pos_embeddings

     # The transformer.
     __lowerCAmelCase								=					lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

     # The regex to extract layer names.
     __lowerCAmelCase								=					re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"       )

     # The simple map of names for "automated" rules.
     __lowerCAmelCase								=					{
         "attention.dense": ".attn.c_proj.",
         "self_attention.dense": ".attn.c_proj.",
         "mlp.dense_h_to_4h": ".mlp.c_fc.",
         "mlp.dense_4h_to_h": ".mlp.c_proj.",
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __lowerCAmelCase								=					layer_re.match(__snake_case       )

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          __lowerCAmelCase								=					int(m.group(1       )       )
          # The name of the operation.
          __lowerCAmelCase								=					m.group(2       )
          # Is it a weight or a bias?
          __lowerCAmelCase								=					m.group(3       )

          # The name of the layer.
          __lowerCAmelCase								=					F"""transformer.h.{layer_idx}"""

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("layernorm"       ):
               __lowerCAmelCase								=					"ln_1" if op_name.startswith("input"       ) else "ln_2"
               __lowerCAmelCase								=					val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               __lowerCAmelCase								=					torch.tril(torch.ones((n_positions, n_positions)					, dtype=torch.floataa       )       ).view(
                   1					, 1					, __snake_case					, __snake_case       )
               __lowerCAmelCase								=					causal_mask

               # Insert a "dummy" tensor for masked_bias.
               __lowerCAmelCase								=					torch.tensor(-1E4					, dtype=torch.floataa       )
               __lowerCAmelCase								=					masked_bias

               __lowerCAmelCase								=					fix_query_key_value_ordering(__snake_case					, __snake_case					, 3					, __snake_case					, __snake_case       )
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               __lowerCAmelCase								=					out_val.transpose(0					, 1       ).contiguous()
               # Store.
               __lowerCAmelCase								=					out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               __lowerCAmelCase								=					fix_query_key_value_ordering(__snake_case					, __snake_case					, 3					, __snake_case					, __snake_case       )
               # Store. No change of shape.
               __lowerCAmelCase								=					out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               __lowerCAmelCase								=					megatron_to_transformers[op_name]
               __lowerCAmelCase								=					val.transpose(0					, 1       )

          # Copy the bias.
          elif weight_or_bias == "bias":
               __lowerCAmelCase								=					megatron_to_transformers[op_name]
               __lowerCAmelCase								=					val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     __lowerCAmelCase								=					transformer["final_layernorm.weight"]
     __lowerCAmelCase								=					transformer["final_layernorm.bias"]

     # For LM head, transformers' wants the matrix to weight embeddings.
     __lowerCAmelCase								=					word_embeddings

     # It should be done!
     return output_state_dict





def       __lowerCAmelCase      (						):
     # Create the argument parser.
     __lowerCAmelCase								=					argparse.ArgumentParser()
     parser.add_argument("--print-checkpoint-structure"					, action="store_true"       )
     parser.add_argument(
         "path_to_checkpoint"					, type=__snake_case					, help="Path to the checkpoint file (.zip archive or direct .pt file)"					, )
     parser.add_argument(
         "--config_file"					, default=""					, type=__snake_case					, help="An optional config json file describing the pre-trained model."					, )
     __lowerCAmelCase								=					parser.parse_args()

     # Extract the basename.
     __lowerCAmelCase								=					os.path.dirname(args.path_to_checkpoint       )

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(F"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""       )
     if args.path_to_checkpoint.endswith(".zip"       ):
          with zipfile.ZipFile(args.path_to_checkpoint					, "r"       ) as checkpoint:
               with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"       ) as pytorch_dict:
                    __lowerCAmelCase								=					torch.load(__snake_case					, map_location="cpu"       )
     else:
          __lowerCAmelCase								=					torch.load(args.path_to_checkpoint					, map_location="cpu"       )

     __lowerCAmelCase								=					input_state_dict.get("args"					, __snake_case       )

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    __lowerCAmelCase								=					"gelu_fast"
               elif ds_args.openai_gelu:
                    __lowerCAmelCase								=					"gelu_new"
               else:
                    __lowerCAmelCase								=					"gelu"
          else:
               # in the very early days this used to be "gelu_new"
               __lowerCAmelCase								=					"gelu_new"

          # Spell out all parameters in case the defaults change.
          __lowerCAmelCase								=					GPTaConfig(
              vocab_size=5_0257					, n_positions=1024					, n_embd=1024					, n_layer=24					, n_head=16					, n_inner=4096					, activation_function=__snake_case					, resid_pdrop=0.1					, embd_pdrop=0.1					, attn_pdrop=0.1					, layer_norm_epsilon=1E-5					, initializer_range=0.02					, summary_type="cls_index"					, summary_use_proj=__snake_case					, summary_activation=__snake_case					, summary_proj_to_labels=__snake_case					, summary_first_dropout=0.1					, scale_attn_weights=__snake_case					, use_cache=__snake_case					, bos_token_id=5_0256					, eos_token_id=5_0256					, )
     else:
          __lowerCAmelCase								=					GPTaConfig.from_json_file(args.config_file       )

     __lowerCAmelCase								=					["GPT2LMHeadModel"]

     # Convert.
     print("Converting"       )
     __lowerCAmelCase								=					convert_megatron_checkpoint(__snake_case					, __snake_case					, __snake_case       )

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(__snake_case					, __snake_case       )

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          __lowerCAmelCase								=					ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               __lowerCAmelCase								=					"gpt2"
          elif tokenizer_type == "PretrainedFromHF":
               __lowerCAmelCase								=					ds_args.tokenizer_name_or_path
          else:
               raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""       )
     else:
          __lowerCAmelCase								=					"gpt2"

     __lowerCAmelCase								=					AutoTokenizer.from_pretrained(__snake_case       )
     __lowerCAmelCase								=					type(__snake_case       ).__name__
     __lowerCAmelCase								=					tokenizer_class

     # Store the config to file.
     print("Saving config"       )
     config.save_pretrained(__snake_case       )

     # Save tokenizer based on args
     print(F"""Adding {tokenizer_class} tokenizer files"""       )
     tokenizer.save_pretrained(__snake_case       )

     # Store the state_dict to file.
     __lowerCAmelCase								=					os.path.join(__snake_case					, "pytorch_model.bin"       )
     print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""       )
     torch.save(__snake_case					, __snake_case       )


####################################################################################################

if __name__ == "__main__":
 main()

####################################################################################################














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


lowerCamelCase			:					List[Any]         =	logging.get_logger(__name__)

lowerCamelCase			:					Union[str, Any]         =	{
    '''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 							_UpperCamelCase			(a_			):
      snake_case_  =							"""blenderbot-small"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(	self					,						__UpperCamelCase=5_0_2_6_5					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1					,						__UpperCamelCase=False					,						__UpperCamelCase=0					,						__UpperCamelCase=1					,						__UpperCamelCase=2					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Any:
           __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=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						is_encoder_decoder=__UpperCamelCase					,						decoder_start_token_id=__UpperCamelCase					,						forced_eos_token_id=__UpperCamelCase					,						**__UpperCamelCase					,						)



class 							_UpperCamelCase			(a_			):


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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

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

           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(__UpperCamelCase					,						__UpperCamelCase	)]					,						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(__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					max(__UpperCamelCase					,						__UpperCamelCase	) - min_num_layers
                __lowerCAmelCase								=					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           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(__UpperCamelCase					,						__UpperCamelCase					,						dtype=__UpperCamelCase	)]					,						dim=1	)
                __lowerCAmelCase								=					[
                    (torch.zeros(__UpperCamelCase	), torch.zeros(__UpperCamelCase	)) for _ in range(__UpperCamelCase	)
                ]
           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = 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(
               __UpperCamelCase					,						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(__UpperCamelCase	)
           __lowerCAmelCase								=					compute_effective_axis_dimension(
               __UpperCamelCase					,						fixed_dimension=OnnxConfig.default_fixed_sequence					,						num_token_to_add=__UpperCamelCase	)

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


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

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

           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	List[Any]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					super()._flatten_past_key_values_(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           else:
                __lowerCAmelCase								=					super(__UpperCamelCase					,						self	)._flatten_past_key_values_(
                    __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)






