



"""simple docstring"""






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import (
     MODEL_MAPPING,
     SegformerForImageClassification,
     SegformerForSemanticSegmentation,
     SegformerModel,
 )
 from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor



class     A__     (						__lowercase							):




 '''simple docstring'''





 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Any:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= self.config_class(**self.inputs_dict)
    self.parent.assertTrue(hasattr(__a ,    "hidden_sizes"))
    self.parent.assertTrue(hasattr(__a ,    "num_attention_heads"))
    self.parent.assertTrue(hasattr(__a ,    "num_encoder_blocks"))






class     A__     :




 '''simple docstring'''





 def __init__(  self:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=13 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=64 ,    _SCREAMING_SNAKE_CASE:	int=3 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=4 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=[2, 2, 2, 2] ,    _SCREAMING_SNAKE_CASE:	Tuple=[8, 4, 2, 1] ,    _SCREAMING_SNAKE_CASE:	Dict=[16, 32, 64, 128] ,    _SCREAMING_SNAKE_CASE:	int=[1, 4, 8, 16] ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=[1, 2, 4, 8] ,    _SCREAMING_SNAKE_CASE:	int=True ,    _SCREAMING_SNAKE_CASE:	Optional[int]=True ,    _SCREAMING_SNAKE_CASE:	Tuple="gelu" ,    _SCREAMING_SNAKE_CASE:	int=0.1 ,    _SCREAMING_SNAKE_CASE:	List[Any]=0.1 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=0.02 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3 ,    _SCREAMING_SNAKE_CASE:	str=None ,    )       -> Optional[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= parent
    __lowerCAmelCase   :						Optional[int]				= batch_size
    __lowerCAmelCase   :						List[str]				= image_size
    __lowerCAmelCase   :						str				= num_channels
    __lowerCAmelCase   :						str				= num_encoder_blocks
    __lowerCAmelCase   :						Any				= sr_ratios
    __lowerCAmelCase   :						List[Any]				= depths
    __lowerCAmelCase   :						Any				= hidden_sizes
    __lowerCAmelCase   :						Tuple				= downsampling_rates
    __lowerCAmelCase   :						Tuple				= num_attention_heads
    __lowerCAmelCase   :						Tuple				= is_training
    __lowerCAmelCase   :						Tuple				= use_labels
    __lowerCAmelCase   :						Optional[int]				= hidden_act
    __lowerCAmelCase   :						List[Any]				= hidden_dropout_prob
    __lowerCAmelCase   :						Tuple				= attention_probs_dropout_prob
    __lowerCAmelCase   :						Optional[int]				= initializer_range
    __lowerCAmelCase   :						Optional[int]				= num_labels
    __lowerCAmelCase   :						Tuple				= scope

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> int:

    """simple docstring"""

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

    __lowerCAmelCase   :						int				= None
    if self.use_labels:
       __lowerCAmelCase   :						Union[str, Any]				= ids_tensor([self.batch_size, self.image_size, self.image_size] ,    self.num_labels)

    __lowerCAmelCase   :						Tuple				= self.get_config()
    return config, pixel_values, labels

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> str:

    """simple docstring"""

    return SegformerConfig(
        image_size=self.image_size ,    num_channels=self.num_channels ,    num_encoder_blocks=self.num_encoder_blocks ,    depths=self.depths ,    hidden_sizes=self.hidden_sizes ,    num_attention_heads=self.num_attention_heads ,    hidden_act=self.hidden_act ,    hidden_dropout_prob=self.hidden_dropout_prob ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob ,    initializer_range=self.initializer_range ,    )

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= SegformerModel(config=__a)
    model.to(__a)
    model.eval()
    __lowerCAmelCase   :						Any				= model(__a)
    __lowerCAmelCase   :						Tuple				= self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape ,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

 def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	str)       -> Optional[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Tuple				= self.num_labels
    __lowerCAmelCase   :						List[Any]				= SegformerForSemanticSegmentation(__a)
    model.to(__a)
    model.eval()
    __lowerCAmelCase   :						Any				= model(__a)
    self.parent.assertEqual(
        result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
    __lowerCAmelCase   :						List[str]				= model(__a ,    labels=__a)
    self.parent.assertEqual(
        result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
    self.parent.assertGreater(result.loss ,    0.0)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Optional[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= 1
    __lowerCAmelCase   :						Tuple				= SegformerForSemanticSegmentation(config=__a)
    model.to(__a)
    model.eval()
    __lowerCAmelCase   :						Optional[Any]				= torch.randint(0 ,    1 ,    (self.batch_size, self.image_size, self.image_size)).to(__a)
    __lowerCAmelCase   :						int				= model(__a ,    labels=__a)
    self.parent.assertGreater(result.loss ,    0.0)







 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						List[str]				= self.prepare_config_and_inputs()
    __lowerCAmelCase   :						List[Any]				= config_and_inputs
    __lowerCAmelCase   :						List[str]				= {"""pixel_values""": pixel_values}
    return config, inputs_dict






@require_torch
class     A__     (						__lowercase						,	__lowercase						,	unittest.TestCase							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			(
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE					   =			(
     {
         '''feature-extraction''': SegformerModel,
         '''image-classification''': SegformerForImageClassification,
         '''image-segmentation''': SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE					   =			True
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False
 SCREAMING_SNAKE_CASE					   =			False

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= SegformerModelTester(self)
    __lowerCAmelCase   :						Optional[int]				= SegformerConfigTester(self ,    config_class=__a)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> List[str]:

    """simple docstring"""

    self.config_tester.run_common_tests()

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__a)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_binary_image_segmentation(*__a)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_segmentation(*__a)

 @unittest.skip("SegFormer does not use inputs_embeds")
 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[Any]:

    """simple docstring"""

    pass

 @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> int:

    """simple docstring"""

    pass

 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase   :						List[str]				= ["""pixel_values"""]
       self.assertListEqual(arg_names[:1] ,    __a)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCAmelCase   :						int				= True

    for model_class in self.all_model_classes:
       __lowerCAmelCase   :						Union[str, Any]				= True
       __lowerCAmelCase   :						List[str]				= False
       __lowerCAmelCase   :						Tuple				= True
       __lowerCAmelCase   :						int				= model_class(__a)
       model.to(__a)
       model.eval()
       with torch.no_grad():
          __lowerCAmelCase   :						List[Any]				= model(**self._prepare_for_class(__a ,    __a))
       __lowerCAmelCase   :						List[Any]				= outputs.attentions

       __lowerCAmelCase   :						Dict				= sum(self.model_tester.depths)
       self.assertEqual(len(__a) ,    __a)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __lowerCAmelCase   :						str				= True
       __lowerCAmelCase   :						int				= model_class(__a)
       model.to(__a)
       model.eval()
       with torch.no_grad():
          __lowerCAmelCase   :						str				= model(**self._prepare_for_class(__a ,    __a))
       __lowerCAmelCase   :						int				= outputs.attentions

       self.assertEqual(len(__a) ,    __a)

       # verify the first attentions (first block, first layer)
       __lowerCAmelCase   :						int				= (self.model_tester.image_size // 4) ** 2
       __lowerCAmelCase   :						List[Any]				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]) ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,    )

       # verify the last attentions (last block, last layer)
       __lowerCAmelCase   :						List[Any]				= (self.model_tester.image_size // 32) ** 2
       __lowerCAmelCase   :						Any				= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]) ,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,    )
       __lowerCAmelCase   :						Optional[int]				= len(__a)

       # Check attention is always last and order is fine
       __lowerCAmelCase   :						Any				= True
       __lowerCAmelCase   :						int				= True
       __lowerCAmelCase   :						str				= model_class(__a)
       model.to(__a)
       model.eval()
       with torch.no_grad():
          __lowerCAmelCase   :						Any				= model(**self._prepare_for_class(__a ,    __a))

       self.assertEqual(out_len + 1 ,    len(__a))

       __lowerCAmelCase   :						List[Any]				= outputs.attentions

       self.assertEqual(len(__a) ,    __a)
       # verify the first attentions (first block, first layer)
       __lowerCAmelCase   :						Dict				= (self.model_tester.image_size // 4) ** 2
       __lowerCAmelCase   :						Any				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]) ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,    )

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> str:

    """simple docstring"""

    def check_hidden_states_output(_SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any):
       __lowerCAmelCase   :						Any				= model_class(__a)
       model.to(__a)
       model.eval()

       with torch.no_grad():
          __lowerCAmelCase   :						Tuple				= model(**self._prepare_for_class(__a ,    __a))

       __lowerCAmelCase   :						Tuple				= outputs.hidden_states

       __lowerCAmelCase   :						List[str]				= self.model_tester.num_encoder_blocks
       self.assertEqual(len(__a) ,    __a)

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]) ,    [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ] ,    )

    __lowerCAmelCase   :						Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCAmelCase   :						int				= True
       check_hidden_states_output(__a ,    __a ,    __a)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowerCAmelCase   :						Dict				= True

       check_hidden_states_output(__a ,    __a ,    __a)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> int:

    """simple docstring"""

    if not self.model_tester.is_training:
       return

    __lowerCAmelCase   :						int				= self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCAmelCase   :						List[str]				= True

    for model_class in self.all_model_classes:
       if model_class in get_values(__a):
          continue

       __lowerCAmelCase   :						Optional[int]				= model_class(__a)
       model.to(__a)
       model.train()
       __lowerCAmelCase   :						List[Any]				= self._prepare_for_class(__a ,    __a ,    return_labels=__a)
       __lowerCAmelCase   :						str				= model(**__a).loss
       loss.backward()

 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

    """simple docstring"""

    pass







 @slow
 def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Tuple:

    """simple docstring"""

    for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase   :						int				= SegformerModel.from_pretrained(__a)
       self.assertIsNotNone(__a)






def     _lowercase       (						)		->     int:
   __lowerCAmelCase   :						Optional[int]				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
   return image





@require_torch
class     A__     (						unittest.TestCase							):




 '''simple docstring'''





 @slow
 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Any:

    """simple docstring"""

    __lowerCAmelCase   :						Tuple				= SegformerImageProcessor(
        image_scale=(512, 512) ,    keep_ratio=__a ,    align=__a ,    do_random_crop=__a)
    __lowerCAmelCase   :						Union[str, Any]				= SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
        __a)

    __lowerCAmelCase   :						Union[str, Any]				= prepare_img()
    __lowerCAmelCase   :						List[str]				= image_processor(images=__a ,    return_tensors="pt")
    __lowerCAmelCase   :						Any				= encoded_inputs.pixel_values.to(__a)

    with torch.no_grad():
       __lowerCAmelCase   :						Dict				= model(__a)

    __lowerCAmelCase   :						Any				= torch.Size((1, model.config.num_labels, 128, 128))
    self.assertEqual(outputs.logits.shape ,    __a)

    __lowerCAmelCase   :						List[str]				= torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
            [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
        ]).to(__a)
    self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,    __a ,    atol=1e-4))

 @slow
 def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> str:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= SegformerImageProcessor(
        image_scale=(512, 512) ,    keep_ratio=__a ,    align=__a ,    do_random_crop=__a)
    __lowerCAmelCase   :						List[Any]				= SegformerForSemanticSegmentation.from_pretrained(
        "nvidia/segformer-b1-finetuned-cityscapes-1024-1024").to(__a)

    __lowerCAmelCase   :						Optional[int]				= prepare_img()
    __lowerCAmelCase   :						str				= image_processor(images=__a ,    return_tensors="pt")
    __lowerCAmelCase   :						int				= encoded_inputs.pixel_values.to(__a)

    with torch.no_grad():
       __lowerCAmelCase   :						Tuple				= model(__a)

    __lowerCAmelCase   :						Optional[int]				= torch.Size((1, model.config.num_labels, 128, 128))
    self.assertEqual(outputs.logits.shape ,    __a)

    __lowerCAmelCase   :						List[Any]				= torch.tensor(
        [
            [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
            [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ]).to(__a)
    self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,    __a ,    atol=1e-1))







 @slow
 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= SegformerImageProcessor(
        image_scale=(512, 512) ,    keep_ratio=__a ,    align=__a ,    do_random_crop=__a)
    __lowerCAmelCase   :						Union[str, Any]				= SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
        __a)

    __lowerCAmelCase   :						Optional[Any]				= prepare_img()
    __lowerCAmelCase   :						Optional[Any]				= image_processor(images=__a ,    return_tensors="pt")
    __lowerCAmelCase   :						Optional[int]				= encoded_inputs.pixel_values.to(__a)

    with torch.no_grad():
       __lowerCAmelCase   :						List[Any]				= model(__a)

    __lowerCAmelCase   :						Dict				= outputs.logits.detach().cpu()

    __lowerCAmelCase   :						List[Any]				= image_processor.post_process_semantic_segmentation(outputs=__a ,    target_sizes=[(500, 300)])
    __lowerCAmelCase   :						Union[str, Any]				= torch.Size((500, 300))
    self.assertEqual(segmentation[0].shape ,    __a)

    __lowerCAmelCase   :						Any				= image_processor.post_process_semantic_segmentation(outputs=__a)
    __lowerCAmelCase   :						List[str]				= torch.Size((128, 128))
    self.assertEqual(segmentation[0].shape ,    __a)




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)