






"""simple docstring"""






import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase_   : str   			= {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}



def 					_A			(__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		list(s_dict.keys()							)
    for key in keys:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		R'''.*/layers_(\d+)'''
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key
        if re.match(__a    ,				__a							):
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		re.sub(R'''layers_(\d+)'''    ,				R'''block/\1/layer'''    ,				__a							)

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		R'''(encoder|decoder)\/'''

        if re.match(__a    ,				__a							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		re.match(__a    ,				__a							).groups()
            if groups[0] == "encoder":
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		re.sub(R'''/mlp/'''    ,				R'''/1/mlp/'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		re.sub(R'''/pre_mlp_layer_norm/'''    ,				R'''/1/layer_norm/'''    ,				__a							)

            elif groups[0] == "decoder":
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		re.sub(R'''/mlp/'''    ,				R'''/2/mlp/'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  List[Any]    						=		re.sub(R'''/pre_mlp_layer_norm/'''    ,				R'''/2/layer_norm/'''    ,				__a							)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		new_key.replace(__a    ,				__a							)

        print(f'{key} -> {new_key}'							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		s_dict.pop(__a							)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		s_dict[
            '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		s_dict[
            '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()							):
        if "expert" in key:
            SCREAMING_SNAKE_CASE_      :  Any    						=		s_dict[key].shape[0]
            SCREAMING_SNAKE_CASE_      :  Any    						=		s_dict[key]
            for idx in range(__a							):
                SCREAMING_SNAKE_CASE_      :  str    						=		expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"    ,				"nested fstring"							)}'							)

            s_dict.pop(__a							)

    return s_dict


UpperCAmelCase_   : Tuple   			= {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}



def 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    import regex as re

    with open(__a    ,				'''r'''							) as f:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		f.read()

    SCREAMING_SNAKE_CASE_      :  Any    						=		re.findall(R'''(.*) = ([0-9.]*)'''    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		float(__a							) if '''.''' in value else int(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		re.findall(R'''(.*activations) = \(\'(.*)\',\)'''    ,				__a							)[0]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		str(activation[1]							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_experts
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		SwitchTransformersConfig(**__a							)
    return config



def 					_A			(__a    ,				__a    ,				__a=None    ,				__a="./"    ,				__a=8							) ->       Union[str, Any]:
    """simple docstring"""






    print(f'Loading flax weights from : {flax_checkpoint_path}'							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		checkpoints.load_tax_checkpoint(__a							)

    if gin_file is not None:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		convert_gin_to_config(__a    ,				__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  int    						=		SwitchTransformersConfig.from_pretrained(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		SwitchTransformersForConditionalGeneration(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		flax_params['''target''']
    SCREAMING_SNAKE_CASE_      :  Any    						=		flatten_dict(__a    ,				sep='''/'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		rename_keys(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		unflatten_dict(__a    ,				sep='''/'''							)

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(__a    ,				__a							)

    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    pt_model.save_pretrained(__a							)


if __name__ == "__main__":
  UpperCAmelCase_   : List[Any]   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--switch_t5x_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
          """ model architecture. If not provided, a `gin_file` has to be provided."""
      ),
  )
  parser.add_argument(
      """--gin_file""",
      default=None,
      type=str,
      required=False,
      help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
  )
  parser.add_argument(
      """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
  )
  parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
  UpperCAmelCase_   : Any   			= parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )










"""simple docstring"""






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 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  List[Any]						,	lowercase_     :  List[str]=13						,	lowercase_     :  int=7						,	lowercase_     :  Any=True						,	lowercase_     :  str=True						,	lowercase_     :  List[Any]=True						,	lowercase_     :  List[Any]=True						,	lowercase_     :  Dict=99						,	lowercase_     :  Union[str, Any]=24						,	lowercase_     :  int=2						,	lowercase_     :  List[str]=6						,	lowercase_     :  Any=37						,	lowercase_     :  Dict="gelu"						,	lowercase_     :  List[str]=0.1						,	lowercase_     :  Dict=0.1						,	lowercase_     :  Union[str, Any]=512						,	lowercase_     :  List[str]=16						,	lowercase_     :  Any=2						,	lowercase_     :  Any=0.02						,	lowercase_     :  List[Any]=3						,	lowercase_     :  Optional[int]=None						,	lowercase_     :  str=1000						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		seq_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		use_input_mask
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		use_token_type_ids
          SCREAMING_SNAKE_CASE_      :  int    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  str    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		type_vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		type_sequence_label_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scope
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		range_bbox
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		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]:
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		bbox[i, j, 3]
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		bbox[i, j, 1]
                      SCREAMING_SNAKE_CASE_      :  str    						=		t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                      SCREAMING_SNAKE_CASE_      :  List[str]    						=		bbox[i, j, 2]
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		bbox[i, j, 0]
                      SCREAMING_SNAKE_CASE_      :  List[str]    						=		t

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		None
          if self.use_input_mask:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		None
          if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		None
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  int    						=		ids_tensor([self.batch_size]						,	self.type_sequence_label_size)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''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 _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		LiltModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	bbox=lowercase_						,	token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		model(lowercase_						,	bbox=lowercase_)

          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 _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]						,	lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LiltForTokenClassification(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  List[str]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LiltForQuestionAnswering(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model(
              lowercase_						,	bbox=lowercase_						,	attention_mask=lowercase_						,	token_type_ids=lowercase_						,	start_positions=lowercase_						,	end_positions=lowercase_						,	)
          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 _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.prepare_config_and_inputs()
          (
              (
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			(
              SCREAMING_SNAKE_CASE_
          )						,			
          )      :  List[str]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LiltModel,
              "question-answering": LiltForQuestionAnswering,
              "text-classification": LiltForSequenceClassification,
              "token-classification": LiltForTokenClassification,
              "zero-shot": LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  str):




          '''simple docstring'''


          return True
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		LiltModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ConfigTester(self						,	config_class=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          self.config_tester.run_common_tests()
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		type
              self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LiltModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.tensor([[1, 2]]						,	device=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]						,	device=lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(input_ids=lowercase_						,	bbox=lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.Size([1, 2, 768])
          SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.tensor(
              [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]						,	device=lowercase_						,	)

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


