





"""simple docstring"""





# 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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=0     )   ->Dict:





  """simple docstring"""


  if name is None:
    lowerCAmelCase__     :Dict											=				None
  else:
    lowerCAmelCase__     :List[Any]											=				'.' * max(0	,  spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
    lowerCAmelCase__     :Union[str, Any]											=				fmt.format(_SCREAMING_SNAKE_CASE     )

  # Print and recurse (if needed).
  if isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    if msg is not None:
      print(_SCREAMING_SNAKE_CASE     )
    for k in val.keys():
      recursive_print(_SCREAMING_SNAKE_CASE	,  val[k]	,  spaces + 2     )
  elif isinstance(_SCREAMING_SNAKE_CASE	,  torch.Tensor     ):
    print(_SCREAMING_SNAKE_CASE	,  ':'	,  val.size()     )
  else:
    print(_SCREAMING_SNAKE_CASE	,  ':'	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    lowerCAmelCase__     :Dict											=				(num_heads, hidden_size, num_splits) + input_shape[1:]
    lowerCAmelCase__     :Optional[int]											=				param.view(*_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Dict											=				param.transpose(0	,  2     )
    lowerCAmelCase__     :int											=				param.transpose(1	,  2     ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    lowerCAmelCase__     :str											=				(num_heads, num_splits, hidden_size) + input_shape[1:]
    lowerCAmelCase__     :Union[str, Any]											=				param.view(*_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :str											=				param.transpose(0	,  1     ).contiguous()
  lowerCAmelCase__     :str											=				param.view(*_SCREAMING_SNAKE_CASE     )
  return param
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :str											=				{}

  # old versions did not store training args
  lowerCAmelCase__     :List[str]											=				input_state_dict.get('args'	,  _SCREAMING_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__     :Optional[Any]											=				ds_args.padded_vocab_size
    lowerCAmelCase__     :str											=				ds_args.max_position_embeddings
    lowerCAmelCase__     :str											=				ds_args.hidden_size
    lowerCAmelCase__     :int											=				ds_args.num_layers
    lowerCAmelCase__     :List[str]											=				ds_args.num_attention_heads
    lowerCAmelCase__     :Dict											=				ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  lowerCAmelCase__     :int											=				input_state_dict['model']
  # The language model.
  lowerCAmelCase__     :Any											=				model['language_model']
  # The embeddings.
  lowerCAmelCase__     :List[Any]											=				lm['embedding']

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

  # The position embeddings.
  lowerCAmelCase__     :Optional[int]											=				embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  lowerCAmelCase__     :Optional[int]											=				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__     :List[Any]											=				pos_embeddings

  # The transformer.
  lowerCAmelCase__     :Any											=				lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  lowerCAmelCase__     :str											=				{
      '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__     :Tuple											=				layer_re.match(_SCREAMING_SNAKE_CASE     )

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

    # The index of the layer.
    lowerCAmelCase__     :List[str]											=				int(m.group(1     )     )
    # The name of the operation.
    lowerCAmelCase__     :int											=				m.group(2     )
    # Is it a weight or a bias?
    lowerCAmelCase__     :Optional[int]											=				m.group(3     )

    # The name of the layer.
    lowerCAmelCase__     :Optional[Any]											=				F"transformer.h.{layer_idx}"

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'     ):
      lowerCAmelCase__     :Optional[int]											=				'ln_1' if op_name.startswith('input'     ) else 'ln_2'
      lowerCAmelCase__     :Tuple											=				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__     :Dict											=				torch.tril(torch.ones((n_positions, n_positions)	,  dtype=torch.floataa     )     ).view(
          1	,  1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :List[Any]											=				causal_mask

      # Insert a "dummy" tensor for masked_bias.
      lowerCAmelCase__     :List[Any]											=				torch.tensor(-1e4	,  dtype=torch.floataa     )
      lowerCAmelCase__     :Dict											=				masked_bias

      lowerCAmelCase__     :Tuple											=				fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  3	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      lowerCAmelCase__     :Tuple											=				out_val.transpose(0	,  1     ).contiguous()
      # Store.
      lowerCAmelCase__     :Optional[Any]											=				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__     :Optional[Any]											=				fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  3	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
      # Store. No change of shape.
      lowerCAmelCase__     :Dict											=				out_val

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      lowerCAmelCase__     :Any											=				megatron_to_transformers[op_name]
      lowerCAmelCase__     :Optional[int]											=				val

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

  # The final layernorm.
  lowerCAmelCase__     :Optional[Any]											=				transformer['final_layernorm.weight']
  lowerCAmelCase__     :int											=				transformer['final_layernorm.bias']

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

  # It should be done!
  return output_state_dict
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'	,  action='store_true'     )
  parser.add_argument(
      'path_to_checkpoint'	,  type=_SCREAMING_SNAKE_CASE	,  help='Path to the checkpoint file (.zip archive or direct .pt file)'	,  )
  parser.add_argument(
      '--config_file'	,  default=''	,  type=_SCREAMING_SNAKE_CASE	,  help='An optional config json file describing the pre-trained model.'	,  )
  lowerCAmelCase__     :str											=				parser.parse_args()

  # Extract the basename.
  lowerCAmelCase__     :Union[str, Any]											=				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__     :int											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  else:
    lowerCAmelCase__     :Optional[int]											=				torch.load(args.path_to_checkpoint	,  map_location='cpu'     )

  lowerCAmelCase__     :Tuple											=				input_state_dict.get('args'	,  _SCREAMING_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__     :Optional[Any]											=				'gelu_fast'
      elif ds_args.openai_gelu:
        lowerCAmelCase__     :Any											=				'gelu_new'
      else:
        lowerCAmelCase__     :str											=				'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      lowerCAmelCase__     :Union[str, Any]											=				'gelu_new'

    # Spell out all parameters in case the defaults change.
    lowerCAmelCase__     :int											=				GPTaConfig(
        vocab_size=5_0257	,  n_positions=1024	,  n_embd=1024	,  n_layer=24	,  n_head=16	,  n_inner=4096	,  activation_function=_SCREAMING_SNAKE_CASE	,  resid_pdrop=0.1	,  embd_pdrop=0.1	,  attn_pdrop=0.1	,  layer_norm_epsilon=1e-5	,  initializer_range=0.0_2	,  summary_type='cls_index'	,  summary_use_proj=_SCREAMING_SNAKE_CASE	,  summary_activation=_SCREAMING_SNAKE_CASE	,  summary_proj_to_labels=_SCREAMING_SNAKE_CASE	,  summary_first_dropout=0.1	,  scale_attn_weights=_SCREAMING_SNAKE_CASE	,  use_cache=_SCREAMING_SNAKE_CASE	,  bos_token_id=5_0256	,  eos_token_id=5_0256	,  )
  else:
    lowerCAmelCase__     :Dict											=				GPTaConfig.from_json_file(args.config_file     )

  lowerCAmelCase__     :List[Any]											=				['GPT2LMHeadModel']

  # Convert.
  print('Converting'     )
  lowerCAmelCase__     :Union[str, Any]											=				convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

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

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

  lowerCAmelCase__     :Optional[int]											=				AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				type(_SCREAMING_SNAKE_CASE     ).__name__
  lowerCAmelCase__     :Optional[Any]											=				tokenizer_class

  # Store the config to file.
  print('Saving config'     )
  config.save_pretrained(_SCREAMING_SNAKE_CASE     )

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

  # Store the state_dict to file.
  lowerCAmelCase__     :Optional[Any]											=				os.path.join(_SCREAMING_SNAKE_CASE	,  'pytorch_model.bin'     )
  print(F"Saving checkpoint to \"{output_checkpoint_file}\""     )
  torch.save(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )


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

if __name__ == "__main__":
  main()

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



import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
      )
      from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class    _lowercase				:

   def __init__(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Dict=1_3			,						lowerCamelCase__	:    Any=7			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    Optional[int]=9_9			,						lowerCamelCase__	:    Optional[int]=2_4			,						lowerCamelCase__	:    Optional[int]=2			,						lowerCamelCase__	:    Dict=6			,						lowerCamelCase__	:    List[str]=3_7			,						lowerCamelCase__	:    Optional[Any]="gelu"			,						lowerCamelCase__	:    Dict=0.1			,						lowerCamelCase__	:    Optional[int]=0.1			,						lowerCamelCase__	:    Any=5_1_2			,						lowerCamelCase__	:    int=1_6			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    Optional[int]=0.02			,						lowerCamelCase__	:    Optional[Any]=3			,						lowerCamelCase__	:    Optional[int]=None			,						lowerCamelCase__	:    Any=1_0_0_0			,						) ->			Any:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_labels
         A_							       =		scope
         A_							       =		range_bbox

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

         A_							       =		ids_tensor([self.batch_size, self.seq_length, 4]			,						self.range_bbox       )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]       ):
               for j in range(bbox.shape[1]       ):
                     if bbox[i, j, 3] < bbox[i, j, 1]:
                           A_							       =		bbox[i, j, 3]
                           A_							       =		bbox[i, j, 1]
                           A_							       =		t
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                           A_							       =		bbox[i, j, 2]
                           A_							       =		bbox[i, j, 0]
                           A_							       =		t

         A_							       =		None
         if self.use_input_mask:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

         A_							       =		None
         if self.use_token_type_ids:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

         A_							       =		None
         A_							       =		None
         if self.use_labels:
               A_							       =		ids_tensor([self.batch_size]			,						self.type_sequence_label_size       )
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels       )

         A_							       =		self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[str]:
         """simple docstring"""





         return LiltConfig(
             vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						)

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Any			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		LiltModel(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size)       )

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[Any]			,						) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		LiltForTokenClassification(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[str]			,						) ->			Any:
         """simple docstring"""





         A_							       =		LiltForQuestionAnswering(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						start_positions=lowerCamelCase__			,						end_positions=lowerCamelCase__			,						)
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)       )





   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         (
             (
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,
         )							       =		config_and_inputs
         A_							       =		{
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict



@require_torch
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    (
       (
           LiltModel,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   _lowercase     :       List[Any]  					=    (
       {
           'feature-extraction': LiltModel,
           'question-answering': LiltForQuestionAnswering,
           'text-classification': LiltForSequenceClassification,
           'token-classification': LiltForTokenClassification,
           'zero-shot': LiltForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _lowercase     :       Dict  					=    False
   _lowercase     :       Optional[int]  					=    False

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]       ) ->			Any:
         """simple docstring"""





         return True

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         A_							       =		LiltModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               A_							       =		type
               self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_							       =		LiltModel.from_pretrained(lowerCamelCase__       )
               self.assertIsNotNone(lowerCamelCase__       )



@require_torch
@slow
class    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(lowerCamelCase__       )

         A_							       =		torch.tensor([[1, 2]]			,						device=lowerCamelCase__       )
         A_							       =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,						device=lowerCamelCase__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(input_ids=lowerCamelCase__			,						bbox=lowerCamelCase__       )

         A_							       =		torch.Size([1, 2, 7_6_8]       )
         A_							       =		torch.tensor(
             [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			,						device=lowerCamelCase__			,						)

         self.assertTrue(outputs.last_hidden_state.shape			,						lowerCamelCase__       )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]			,						lowerCamelCase__			,						atol=1e-3       )       )





