






"""simple docstring"""





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   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    [
        '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(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__       ,						UpperCamelCase__         =    emb.weight.shape
    UpperCamelCase__         =    nn.Linear(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	bias=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    emb.weight.data
    return lin_layer







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None					)					->	str:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = WEIGHTS_NAME					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    []
    UpperCamelCase__         =    0
    os.makedirs(SCREAMING_SNAKE_CASE ,	exist_ok=SCREAMING_SNAKE_CASE					)

    for expert in range(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    switch_checkpoint_path + F'-rank-{expert}.pt'
        if os.path.isfile(SCREAMING_SNAKE_CASE					):
            UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE					)['model']
            remove_ignore_keys_(SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    rename_fairseq_keys(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    os.path.join(
                SCREAMING_SNAKE_CASE ,	weights_name.replace('.bin' ,	F'-{len(SCREAMING_SNAKE_CASE					)+1:05d}-of-???.bin'					)					)
            torch.save(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            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(SCREAMING_SNAKE_CASE					)[0]].dtype					)

    # Add the last block
    UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	weights_name.replace('.bin' ,	F'-{len(SCREAMING_SNAKE_CASE					)+1:05d}-of-???.bin'					)					)
    UpperCamelCase__         =    torch.load(switch_checkpoint_path + '-shared.pt'					)['model']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    rename_fairseq_keys(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    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(SCREAMING_SNAKE_CASE					) == 1:
        UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        torch.save(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    # Otherwise, let's build the index
    UpperCamelCase__         =    {}
    for idx, shard in enumerate(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    weights_name.replace('.bin' ,	F'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE					):05d}.bin'					)
        UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	weights_name.replace('.bin' ,	F'-{idx+1:05d}-of-???.bin'					)					)
        os.rename(SCREAMING_SNAKE_CASE ,	os.path.join(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					)
        for key in shard:
            UpperCamelCase__         =    shard_file

    # Add the metadata
    UpperCamelCase__         =    {'total_size': total_size}
    UpperCamelCase__         =    {'metadata': metadata, 'weight_map': weight_map}

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

    return metadata, index


if __name__ == "__main__":
  A__					: int=	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__					: Tuple=	parser.parse_args()
  A__,     A__					: List[str]=	shard_on_the_fly(
      args.nllb_moe_checkpoint_path,
      args.pytorch_dump_folder_path,
      1_28,
      args.dtype,
  )

  A__					: int=	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__					: Dict=	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print("""Done""")
  model.save_pretrained(args.pytorch_dump_folder_path)












"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=100 ,				snake_case_=13 ,				snake_case_=30 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=10 ,				snake_case_=0.02 ,				snake_case_=3 ,				)		->       Optional[int]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__         =    (image_size // patch_size) ** 2
       UpperCamelCase__         =    num_patches + 1

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       UpperCamelCase__         =    None
       if self.use_labels:
           UpperCamelCase__         =    ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )

       UpperCamelCase__         =    BeitConfig(
           vocab_size=self.vocab_size ,				image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				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 ,				is_decoder=snake_case_ ,				initializer_range=self.initializer_range ,				)

       return config, pixel_values, labels

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitModel(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.type_sequence_label_size
       UpperCamelCase__         =    FlaxBeitForImageClassification(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       UpperCamelCase__         =    1
       UpperCamelCase__         =    FlaxBeitForImageClassification(snake_case_      )

       UpperCamelCase__         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       UpperCamelCase__         =    model(snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						
       )         =    config_and_inputs
       UpperCamelCase__         =    {'pixel_values': pixel_values}
       return config, inputs_dict







@require_flax
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				int 						=(
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       None:
       UpperCamelCase__         =    FlaxBeitModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				has_text_modality=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    inspect.signature(model.__call__      )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__         =    [*signature.parameters.keys()]

           UpperCamelCase__         =    ['pixel_values']
           self.assertListEqual(arg_names[:1] ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
               UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
               UpperCamelCase__         =    model_class(snake_case_      )

               @jax.jit
               def model_jitted(snake_case_ ,				**snake_case_      ):
                   return model(pixel_values=snake_case_ ,				**snake_case_      )

               with self.subTest('JIT Enabled'      ):
                   UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               with self.subTest('JIT Disabled'      ):
                   with jax.disable_jit():
                       UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      )      )
               for jitted_output, output in zip(snake_case_ ,				snake_case_      ):
                   self.assertEqual(jitted_output.shape ,				output.shape      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__         =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'      )
           UpperCamelCase__         =    model(np.ones((1, 3, 224, 224)      )      )
           self.assertIsNotNone(snake_case_      )







def   lowerCAmelCase_( )					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_vision
@require_flax
class    __lowerCamelCase    (  unittest.TestCase  ):

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      ).pixel_values

       # prepare bool_masked_pos
       UpperCamelCase__         =    np.ones((1, 196) ,				dtype=snake_case_      )

       # forward pass
       UpperCamelCase__         =    model(pixel_values=snake_case_ ,				bool_masked_pos=snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 196, 8192)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,				snake_case_ ,				atol=1E-2      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 1000)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([-1.2_385, -1.0_987, -1.0_108]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    281
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 2_1841)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([1.6_881, -0.2_787, 0.5_901]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    2396
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )




