
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			(			a__  )       ->  Tuple:
				__a										=    [
				    '''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     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				__a							,     __a										=    emb.weight.shape
				__a										=    nn.Linear(a__						,    a__						,    bias=a__  )
				__a										=    emb.weight.data
				return lin_layer


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


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__ = WEIGHTS_NAME  )       ->  Any:
				__a										=    []
				__a										=    0
				os.makedirs(a__						,    exist_ok=a__  )

				for expert in range(a__  ):
								__a										=    switch_checkpoint_path + F"""-rank-{expert}.pt"""
								if os.path.isfile(a__  ):
												__a										=    torch.load(a__  )['''model''']
												remove_ignore_keys_(a__  )
												__a										=    rename_fairseq_keys(a__						,    a__  )
												__a										=    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
				__a										=    os.path.join(a__						,    weights_name.replace('''.bin'''						,    F"""-{len(a__  )+1:05d}-of-???.bin"""  )  )
				__a										=    torch.load(switch_checkpoint_path + '''-shared.pt'''  )['''model''']
				remove_ignore_keys_(a__  )
				__a										=    rename_fairseq_keys(a__						,    a__  )
				__a										=    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:
								__a										=    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
				__a										=    {}
				for idx, shard in enumerate(a__  ):
								__a										=    weights_name.replace('''.bin'''						,    F"""-{idx+1:05d}-of-{len(a__  ):05d}.bin"""  )
								__a										=    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:
												__a										=    shard_file

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

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

				return metadata, index


if __name__ == "__main__":
					A     :	List[Any]    = 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     :	Optional[Any]    = parser.parse_args()
					A ,   A     :	List[str]    = shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					A     :	Dict    = NllbMoeConfig.from_pretrained(
					    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					A     :	Optional[Any]    = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('Done')
					model.save_pretrained(args.pytorch_dump_folder_path)





import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor
class 			a__    :






						def __init__(   self		,			_A		,			_A=1_3		,			_A=3_0		,			_A=2		,			_A=3		,			_A=True		,			_A=True		,			_A=3_2		,			_A=5		,			_A=4		,			_A=3_7		,			_A="gelu"		,			_A=0.1		,			_A=0.1		,			_A=1_0		,			_A=0.02		,			_A=3		,			_A=None		,			_A=2		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       image_size
											__lowerCAmelCase				   =       patch_size
											__lowerCAmelCase				   =       num_channels
											__lowerCAmelCase				   =       is_training
											__lowerCAmelCase				   =       use_labels
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       num_hidden_layers
											__lowerCAmelCase				   =       num_attention_heads
											__lowerCAmelCase				   =       intermediate_size
											__lowerCAmelCase				   =       hidden_act
											__lowerCAmelCase				   =       hidden_dropout_prob
											__lowerCAmelCase				   =       attention_probs_dropout_prob
											__lowerCAmelCase				   =       type_sequence_label_size
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       scope
											__lowerCAmelCase				   =       encoder_stride

											# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
											__lowerCAmelCase				   =       (image_size // patch_size) ** 2
											__lowerCAmelCase				   =       num_patches + 2






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

											__lowerCAmelCase				   =       self.get_config()

											return config, pixel_values, labels






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return DeiTConfig(
											    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=_A		,			initializer_range=self.initializer_range		,			encoder_stride=self.encoder_stride		,			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTModel(config=_A			)
											model.to(_A			)
											model.eval()
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTForMaskedImageModeling(config=_A			)
											model.to(_A			)
											model.eval()
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(
											    result.reconstruction.shape		,			(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

											# test greyscale images
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       DeiTForMaskedImageModeling(_A			)
											model.to(_A			)
											model.eval()

											__lowerCAmelCase				   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(result.reconstruction.shape		,			(self.batch_size, 1, self.image_size, self.image_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.type_sequence_label_size
											__lowerCAmelCase				   =       DeiTForImageClassification(_A			)
											model.to(_A			)
											model.eval()
											__lowerCAmelCase				   =       model(_A		,			labels=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)			)

											# test greyscale images
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       DeiTForImageClassification(_A			)
											model.to(_A			)
											model.eval()

											__lowerCAmelCase				   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											__lowerCAmelCase				   =       model(_A		,			labels=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       config_and_inputs
											__lowerCAmelCase				   =       {"pixel_values": pixel_values}
											return config, inputs_dict






@require_torch
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    Optional[Any]					=    (
						    (
						        DeiTModel,
						        DeiTForImageClassification,
						        DeiTForImageClassificationWithTeacher,
						        DeiTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						_a							:    int					=    (
						    {
						        """feature-extraction""": DeiTModel,
						        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						_a							:    Optional[Any]					=    False
						_a							:    Tuple					=    False
						_a							:    Tuple					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			has_text_modality=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="DeiT does not use inputs_embeds"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       model_class(_A			)
																self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)			)
																__lowerCAmelCase				   =       model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_A		,			nn.Linear			)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase				   =       ["pixel_values"]
																self.assertListEqual(arg_names[:1]		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A=False			):




											"""simple docstring"""



											__lowerCAmelCase				   =       super()._prepare_for_class(_A		,			_A		,			return_labels=_A			)

											if return_labels:
																if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																					del inputs_dict["labels"]

											return inputs_dict






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.model_tester.is_training:
																return

											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase				   =       True

											for model_class in self.all_model_classes:
																# DeiTForImageClassificationWithTeacher supports inference-only
																if (
																    model_class in get_values(_A			)
																    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																):
																					continue
																__lowerCAmelCase				   =       model_class(_A			)
																model.to(_A			)
																model.train()
																__lowerCAmelCase				   =       self._prepare_for_class(_A		,			_A		,			return_labels=_A			)
																__lowerCAmelCase				   =       model(**_A			).loss
																loss.backward()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																return

											__lowerCAmelCase				   =       False
											__lowerCAmelCase				   =       True

											for model_class in self.all_model_classes:
																if model_class in get_values(_A			) or not model_class.supports_gradient_checkpointing:
																					continue
																# DeiTForImageClassificationWithTeacher supports inference-only
																if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																					continue
																__lowerCAmelCase				   =       model_class(_A			)
																model.gradient_checkpointing_enable()
																model.to(_A			)
																model.train()
																__lowerCAmelCase				   =       self._prepare_for_class(_A		,			_A		,			return_labels=_A			)
																__lowerCAmelCase				   =       model(**_A			).loss
																loss.backward()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											__lowerCAmelCase				   =       [
											    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
											    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
											    {"title": "regression", "num_labels": 1, "dtype": torch.float},
											]

											for model_class in self.all_model_classes:
																if (
																    model_class
																    not in [
																        *get_values(_A			),
																        *get_values(_A			),
																    ]
																    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																):
																					continue

																for problem_type in problem_types:
																					with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""			):
																										__lowerCAmelCase				   =       problem_type["title"]
																										__lowerCAmelCase				   =       problem_type["num_labels"]

																										__lowerCAmelCase				   =       model_class(_A			)
																										model.to(_A			)
																										model.train()

																										__lowerCAmelCase				   =       self._prepare_for_class(_A		,			_A		,			return_labels=_A			)

																										if problem_type["num_labels"] > 1:
																															__lowerCAmelCase				   =       inputs["labels"].unsqueeze(1			).repeat(1		,			problem_type["num_labels"]			)

																										__lowerCAmelCase				   =       inputs["labels"].to(problem_type["dtype"]			)

																										# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																										# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																										# they have the same size." which is a symptom something in wrong for the regression problem.
																										# See https://github.com/huggingface/transformers/issues/11780
																										with warnings.catch_warnings(record=_A			) as warning_list:
																															__lowerCAmelCase				   =       model(**_A			).loss
																										for w in warning_list:
																															if "Using a target size that is different to the input size" in str(w.message			):
																																				raise ValueError(
																																				    f"""Something is going wrong in the regression problem: intercepted {w.message}"""			)

																										loss.backward()






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       DeiTModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)







def 	_a      (						):
					__lowerCAmelCase				   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					return image


@require_torch
@require_vision
class 			a__    (					unittest.TestCase      ):






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return (
											    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"			)
											    if is_vision_available()
											    else None
											)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"			).to(
											    _A			)

											__lowerCAmelCase				   =       self.default_image_processor
											__lowerCAmelCase				   =       prepare_img()
											__lowerCAmelCase				   =       image_processor(images=_A		,			return_tensors="pt"			).to(_A			)

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase				   =       model(**_A			)

											# verify the logits
											__lowerCAmelCase				   =       torch.Size((1, 1_0_0_0)			)
											self.assertEqual(outputs.logits.shape		,			_A			)

											__lowerCAmelCase				   =       torch.tensor([-1.02_66, 0.19_12, -1.28_61]			).to(_A			)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			_A		,			atol=1E-4			)			)






						@slow
						@require_accelerate
						@require_torch_gpu
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       DeiTModel.from_pretrained(
											    "facebook/deit-base-distilled-patch16-224"		,			torch_dtype=torch.floataa		,			device_map="auto"			)
											__lowerCAmelCase				   =       self.default_image_processor

											__lowerCAmelCase				   =       prepare_img()
											__lowerCAmelCase				   =       image_processor(images=_A		,			return_tensors="pt"			)
											__lowerCAmelCase				   =       inputs.pixel_values.to(_A			)

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																__lowerCAmelCase				   =       model(_A			)




