
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def 						lowercase_      (       A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= [
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "decoder.output_projection.weight",
        "_float_tensor",
        "encoder.embed_positions._float_tensor",
        "decoder.embed_positions._float_tensor",
    ]
    for k in ignore_keys:
        state_dict.pop(A__			,		A__		)



def 						lowercase_      (       A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case					,							snake_case								= emb.weight.shape
    snake_case								= nn.Linear(A__			,		A__			,		bias=A__		)
    snake_case								= emb.weight.data
    return lin_layer



def 						lowercase_      (       A__			,		A__=None		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= {}
    for old_key in state_dict.keys():
        snake_case								= old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                snake_case								= key.replace("moe_layer.experts.0"			,		F'ffn.experts.expert_{expert_idx}'		)
            else:
                snake_case								= key.replace("moe_layer.experts."			,		"ffn.experts.expert_"		)
        if "gate" in key:
            snake_case								= key.replace(".moe_layer.gate.wg"			,		".ffn.router.classifier"		)
        if "fc2" and "experts" not in key:
            snake_case								= key.replace(".fc2."			,		".ffn.fc2."		)
        if "fc1" and "experts" not in key:
            snake_case								= key.replace(".fc1."			,		".ffn.fc1."		)
        if ".encoder_attn." in key:
            snake_case								= key.replace(".encoder_attn."			,		".cross_attention."		)
        if "encoder_attn_layer_norm" in key:
            snake_case								= key.replace("encoder_attn_layer_norm"			,		"cross_attention_layer_norm"		)
        if "final_layer_norm" in key:
            snake_case								= key.replace("final_layer_norm"			,		"ff_layer_norm"		)
        snake_case								= state_dict[old_key]
    return new_dict



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__ = WEIGHTS_NAME		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= []
    snake_case								= 0
    os.makedirs(A__			,		exist_ok=A__		)

    for expert in range(A__		):
        snake_case								= switch_checkpoint_path + F'-rank-{expert}.pt'
        if os.path.isfile(A__		):
            snake_case								= torch.load(A__		)["model"]
            remove_ignore_keys_(A__		)
            snake_case								= rename_fairseq_keys(A__			,		A__		)
            snake_case								= os.path.join(
                A__			,		weights_name.replace(".bin"			,		F'-{len(A__		)+1:05d}-of-???.bin'		)		)
            torch.save(A__			,		A__		)
            sharded_state_dicts.append(expert_state.keys()		)
            total_size += sum([value.numel() for key, value in expert_state.items()]		) * dtype_byte_size(
                expert_state[list(A__		)[0]].dtype		)

    # Add the last block
    snake_case								= os.path.join(A__			,		weights_name.replace(".bin"			,		F'-{len(A__		)+1:05d}-of-???.bin'		)		)
    snake_case								= torch.load(switch_checkpoint_path + "-shared.pt"		)["model"]
    remove_ignore_keys_(A__		)
    snake_case								= rename_fairseq_keys(A__			,		A__		)
    snake_case								= shared_weights["decoder.embed_tokens.weight"]
    sharded_state_dicts.append(shared_weights.keys()		)

    # If we only have the shared weights (dummy model/experts saved on the same file)
    if len(A__		) == 1:
        snake_case								= os.path.join(A__			,		A__		)
        torch.save(A__			,		A__		)
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(A__			,		A__		)
    # Otherwise, let's build the index
    snake_case								= {}
    for idx, shard in enumerate(A__		):
        snake_case								= weights_name.replace(".bin"			,		F'-{idx+1:05d}-of-{len(A__		):05d}.bin'		)
        snake_case								= os.path.join(A__			,		weights_name.replace(".bin"			,		F'-{idx+1:05d}-of-???.bin'		)		)
        os.rename(A__			,		os.path.join(A__			,		A__		)		)
        for key in shard:
            snake_case								= shard_file

    # Add the metadata
    snake_case								= {"total_size": total_size}
    snake_case								= {"metadata": metadata, "weight_map": weight_map}

    with open(os.path.join(A__			,		A__		)			,		"w"			,		encoding="utf-8"		) as f:
        snake_case								= json.dumps(A__			,		indent=2			,		sort_keys=A__		) + "\n"
        f.write(A__		)

    return metadata, index


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--nllb_moe_checkpoint_path",
       default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
       type=str,
       required=False,
       help="Path to a directory containing a folder per layer. Follows the original Google format.",
   )
   parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
       type=str,
       required=False,
       help="Path to the output pytorch model.",
   )
   _A   =			parser.parse_args()
   _A ,  _A   =			shard_on_the_fly(
       args.nllb_moe_checkpoint_path,
       args.pytorch_dump_folder_path,
       1_28,
       args.dtype,
   )

   _A   =			NllbMoeConfig.from_pretrained(
       "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
   )
   config.save_pretrained(args.pytorch_dump_folder_path)
   _A   =			NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print("Done")
   model.save_pretrained(args.pytorch_dump_folder_path)



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		:





     def __init__(self				:   Union[str, Any]    ,   _A				:   Any    ,   _A				:   Tuple=1_3    ,   _A				:   Optional[int]=7    ,   _A				:   Any=True    ,   _A				:   str=True    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[int]=True    ,   _A				:   str=9_9    ,   _A				:   str=2_4    ,   _A				:   int=2    ,   _A				:   Optional[Any]=6    ,   _A				:   int=3_7    ,   _A				:   List[Any]="gelu"    ,   _A				:   str=0.1    ,   _A				:   Dict=0.1    ,   _A				:   Dict=5_1_2    ,   _A				:   Tuple=1_6    ,   _A				:   List[str]=2    ,   _A				:   Dict=0.02    ,   _A				:   List[str]=3    ,   _A				:   Optional[Any]=None    ,   _A				:   Dict=1_0_0_0    ,   )						->						Any:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= seq_length
         snake_case								= is_training
         snake_case								= use_input_mask
         snake_case								= use_token_type_ids
         snake_case								= use_labels
         snake_case								= vocab_size
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= max_position_embeddings
         snake_case								= type_vocab_size
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range
         snake_case								= num_labels
         snake_case								= scope
         snake_case								= range_bbox





     def 		UpperCAmelCase(self				:   List[str]       )						->						List[str]:
         snake_case								= ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size       )

         snake_case								= 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]:
                     snake_case								= bbox[i, j, 3]
                     snake_case								= bbox[i, j, 1]
                     snake_case								= t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     snake_case								= bbox[i, j, 2]
                     snake_case								= bbox[i, j, 0]
                     snake_case								= t

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

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

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

         snake_case								= self.get_config()

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





     def 		UpperCAmelCase(self				:   Tuple       )						->						Tuple:
         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				:   List[str]    ,   _A				:   Dict    ,   _A				:   List[Any]    ,   _A				:   Optional[Any]    ,   _A				:   Dict    ,   _A				:   str    ,   _A				:   Optional[Any]    ,   _A				:   Tuple    ,   )						->						Dict:
         snake_case								= LiltModel(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A       )

         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[Any]    ,   _A				:   Optional[int]    ,   _A				:   Dict    ,   _A				:   List[Any]    ,   _A				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   Tuple    ,   _A				:   Union[str, Any]    ,   )						->						Optional[int]:
         snake_case								= self.num_labels
         snake_case								= LiltForTokenClassification(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(
             _A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A    ,   labels=_A       )
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Any    ,   _A				:   List[str]    ,   _A				:   List[str]    ,   _A				:   Optional[int]    ,   _A				:   Optional[Any]    ,   )						->						Optional[int]:
         snake_case								= LiltForQuestionAnswering(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(
             _A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A    ,   start_positions=_A    ,   end_positions=_A    ,   )
         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				:   str       )						->						str:
         snake_case								= self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							
         )								= config_and_inputs
         snake_case								= {
             "input_ids": input_ids,
             "bbox": bbox,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict







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





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   Dict    ,   _A				:   Union[str, Any]    ,   _A				:   int    ,   _A				:   Union[str, Any]       )						->						int:
         return True





     def 		UpperCAmelCase(self				:   str       )						->						Tuple:
         snake_case								= LiltModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						List[str]:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   Tuple       )						->						Dict:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   int       )						->						Union[str, Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case								= type
             self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Optional[int]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_A       )





     @slow
     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Optional[Any]:
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= LiltModel.from_pretrained(_A       )
             self.assertIsNotNone(_A       )







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





     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         snake_case								= LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"       ).to(_A       )

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

         # forward pass
         with torch.no_grad():
             snake_case								= model(input_ids=_A    ,   bbox=_A       )

         snake_case								= torch.Size([1, 2, 7_6_8]       )
         snake_case								= torch.tensor(
             [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]    ,   device=_A    ,   )

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

