'''simple docstring'''






import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
     from transformers.models.maskformer import MaskFormerSwinModel





class     A__							:





   """simple docstring"""





   def __init__( self     :		Optional[int]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		Tuple=1_3						,     lowerCAmelCase__     :		Optional[Any]=3_2						,     lowerCAmelCase__     :		Dict=2						,     lowerCAmelCase__     :		Dict=3						,     lowerCAmelCase__     :		Union[str, Any]=1_6						,     lowerCAmelCase__     :		Any=[1, 2, 1]						,     lowerCAmelCase__     :		int=[2, 2, 4]						,     lowerCAmelCase__     :		int=2						,     lowerCAmelCase__     :		Optional[int]=2.0						,     lowerCAmelCase__     :		Optional[int]=True						,     lowerCAmelCase__     :		Any=0.0						,     lowerCAmelCase__     :		str=0.0						,     lowerCAmelCase__     :		Optional[Any]=0.1						,     lowerCAmelCase__     :		Tuple="gelu"						,     lowerCAmelCase__     :		Optional[Any]=False						,     lowerCAmelCase__     :		Any=True						,     lowerCAmelCase__     :		List[str]=0.02						,     lowerCAmelCase__     :		Union[str, Any]=1e-5						,     lowerCAmelCase__     :		str=True						,     lowerCAmelCase__     :		List[str]=None						,     lowerCAmelCase__     :		Union[str, Any]=True						,     lowerCAmelCase__     :		Tuple=1_0						,     lowerCAmelCase__     :		Dict=8						,     lowerCAmelCase__     :		Tuple=["stage1", "stage2", "stage3"]						,     lowerCAmelCase__     :		Tuple=[1, 2, 3]						,     )       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   parent
      _UpperCAmelCase							:	Tuple							=   batch_size
      _UpperCAmelCase							:	Optional[int]							=   image_size
      _UpperCAmelCase							:	str							=   patch_size
      _UpperCAmelCase							:	Union[str, Any]							=   num_channels
      _UpperCAmelCase							:	Optional[Any]							=   embed_dim
      _UpperCAmelCase							:	str							=   depths
      _UpperCAmelCase							:	Union[str, Any]							=   num_heads
      _UpperCAmelCase							:	Optional[Any]							=   window_size
      _UpperCAmelCase							:	Union[str, Any]							=   mlp_ratio
      _UpperCAmelCase							:	Tuple							=   qkv_bias
      _UpperCAmelCase							:	List[Any]							=   hidden_dropout_prob
      _UpperCAmelCase							:	str							=   attention_probs_dropout_prob
      _UpperCAmelCase							:	str							=   drop_path_rate
      _UpperCAmelCase							:	int							=   hidden_act
      _UpperCAmelCase							:	Optional[Any]							=   use_absolute_embeddings
      _UpperCAmelCase							:	str							=   patch_norm
      _UpperCAmelCase							:	Tuple							=   layer_norm_eps
      _UpperCAmelCase							:	Tuple							=   initializer_range
      _UpperCAmelCase							:	Optional[Any]							=   is_training
      _UpperCAmelCase							:	List[Any]							=   scope
      _UpperCAmelCase							:	List[str]							=   use_labels
      _UpperCAmelCase							:	int							=   type_sequence_label_size
      _UpperCAmelCase							:	Tuple							=   encoder_stride
      _UpperCAmelCase							:	Optional[Any]							=   out_features
      _UpperCAmelCase							:	Dict							=   out_indices




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







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      _UpperCAmelCase							:	Optional[int]							=   None
      if self.use_labels:
         _UpperCAmelCase							:	Tuple							=   ids_tensor([self.batch_size]						,     self.type_sequence_label_size			)

      _UpperCAmelCase							:	Union[str, Any]							=   self.get_config()

      return config, pixel_values, labels




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    str:







      """simple docstring"""




      return MaskFormerSwinConfig(
          image_size=self.image_size						,     patch_size=self.patch_size						,     num_channels=self.num_channels						,     embed_dim=self.embed_dim						,     depths=self.depths						,     num_heads=self.num_heads						,     window_size=self.window_size						,     mlp_ratio=self.mlp_ratio						,     qkv_bias=self.qkv_bias						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     drop_path_rate=self.drop_path_rate						,     hidden_act=self.hidden_act						,     use_absolute_embeddings=self.use_absolute_embeddings						,     path_norm=self.patch_norm						,     layer_norm_eps=self.layer_norm_eps						,     initializer_range=self.initializer_range						,     encoder_stride=self.encoder_stride						,     out_features=self.out_features						,     out_indices=self.out_indices						,     )




   def 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		Optional[int]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   MaskFormerSwinModel(config=UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()
      _UpperCAmelCase							:	List[Any]							=   model(UpperCamelCase__			)

      _UpperCAmelCase							:	List[Any]							=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
      _UpperCAmelCase							:	str							=   int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

      self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, expected_seq_len, expected_dim)			)




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		List[str]			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   MaskFormerSwinBackbone(config=UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()
      _UpperCAmelCase							:	List[Any]							=   model(UpperCamelCase__			)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps			)						,     len(config.out_features			)			)
      self.parent.assertListEqual(list(result.feature_maps[0].shape			)						,     [1_3, 1_6, 1_6, 1_6]			)

      # verify channels
      self.parent.assertEqual(len(model.channels			)						,     len(config.out_features			)			)
      self.parent.assertListEqual(model.channels						,     [1_6, 3_2, 6_4]			)

      # verify ValueError
      with self.parent.assertRaises(UpperCamelCase__			):
         _UpperCAmelCase							:	Optional[int]							=   ["stem"]
         _UpperCAmelCase							:	Any							=   MaskFormerSwinBackbone(config=UpperCamelCase__			)




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.prepare_config_and_inputs()
      _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:	Optional[int]							=   config_and_inputs
      _UpperCAmelCase							:	Union[str, Any]							=   {"pixel_values": pixel_values}
      return config, inputs_dict





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





   """simple docstring"""





   UpperCamelCase_       :						Union[str, Any]    				=     (
       (
           MaskFormerSwinModel,
           MaskFormerSwinBackbone,
       )
       if is_torch_available()
       else ()
   )
   UpperCamelCase_       :						Dict    				=     {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
   UpperCamelCase_       :						Dict    				=     False
   UpperCamelCase_       :						Tuple    				=     False
   UpperCamelCase_       :						Any    				=     False
   UpperCamelCase_       :						int    				=     False
   UpperCamelCase_       :						str    				=     False




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







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   MaskFormerSwinModelTester(self			)
      _UpperCAmelCase							:	Optional[Any]							=   ConfigTester(self						,     config_class=UpperCamelCase__						,     embed_dim=3_7			)




   @require_torch_multi_gpu
   @unittest.skip(
       reason=(
           "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
           " `nn.DataParallel`"
       )			)
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Optional[int]:







      """simple docstring"""




      pass




   def 			_lowerCAmelCase		( self     :		str			)       ->    Dict:







      """simple docstring"""




      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()




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







      """simple docstring"""




      return




   def 			_lowerCAmelCase		( self     :		str			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase__			)




   def 			_lowerCAmelCase		( self     :		int			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*UpperCamelCase__			)




   @unittest.skip("Swin does not use inputs_embeds"			)
   def 			_lowerCAmelCase		( self     :		Any			)       ->    int:







      """simple docstring"""




      pass




   @unittest.skip("Swin does not support feedforward chunking"			)
   def 			_lowerCAmelCase		( self     :		str			)       ->    List[Any]:







      """simple docstring"""




      pass




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







      """simple docstring"""




      _UpperCAmelCase							,       _UpperCAmelCase							:	List[str]							=   self.model_tester.prepare_config_and_inputs_for_common()

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




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Dict:







      """simple docstring"""




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

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

         _UpperCAmelCase							:	Optional[int]							=   ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,     UpperCamelCase__			)




   @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions"			)
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    int:







      """simple docstring"""




      pass




   @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"			)
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Dict:







      """simple docstring"""




      pass




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		List[Any]						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Union[str, Any]			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   model_class(UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()

      with torch.no_grad():
         _UpperCAmelCase							:	Dict							=   model(**self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__			)			)

      _UpperCAmelCase							:	Optional[int]							=   outputs.hidden_states

      _UpperCAmelCase							:	Any							=   getattr(
          self.model_tester						,     "expected_num_hidden_layers"						,     len(self.model_tester.depths			) + 1			)
      self.assertEqual(len(UpperCamelCase__			)						,     UpperCamelCase__			)

      # Swin has a different seq_length
      _UpperCAmelCase							:	List[Any]							=   (
          config.patch_size
          if isinstance(config.patch_size						,     collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      _UpperCAmelCase							:	List[str]							=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]			)						,     [num_patches, self.model_tester.embed_dim]						,     )




   def 			_lowerCAmelCase		( self     :		Dict			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							,       _UpperCAmelCase							:	Any							=   self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase							:	List[Any]							=   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size						,     collections.abc.Iterable			)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
         _UpperCAmelCase							:	Optional[int]							=   True
         self.check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _UpperCAmelCase							:	Optional[int]							=   True

         self.check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__			)




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







      """simple docstring"""




      _UpperCAmelCase							,       _UpperCAmelCase							:	List[str]							=   self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase							:	Optional[int]							=   3

      _UpperCAmelCase							:	List[Any]							=   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size						,     collections.abc.Iterable			)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      _UpperCAmelCase							:	Optional[Any]							=   (
          config.patch_size
          if isinstance(config.patch_size						,     collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      _UpperCAmelCase							:	Union[str, Any]							=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      _UpperCAmelCase							:	Optional[Any]							=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
         _UpperCAmelCase							:	int							=   True
         self.check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     (padded_height, padded_width)			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _UpperCAmelCase							:	Any							=   True
         self.check_hidden_states_output(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     (padded_height, padded_width)			)




   @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints"			)
   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    int:







      """simple docstring"""




      pass




   @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"			)
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    List[Any]:







      """simple docstring"""




      pass




   @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"			)
   def 			_lowerCAmelCase		( self     :		Dict			)       ->    Optional[Any]:







      """simple docstring"""




      pass




   def 			_lowerCAmelCase		( self     :		Dict			)       ->    Any:







      """simple docstring"""




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

      def set_nan_tensor_to_zero(lowerCAmelCase__     :		Union[str, Any]			):
         _UpperCAmelCase							:	List[str]							=   0
         return t

      def check_equivalence(lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		int={}			):
         with torch.no_grad():
            _UpperCAmelCase							:	List[Any]							=   model(**UpperCamelCase__						,     return_dict=UpperCamelCase__						,     **UpperCamelCase__			)
            _UpperCAmelCase							:	Optional[Any]							=   model(**UpperCamelCase__						,     return_dict=UpperCamelCase__						,     **UpperCamelCase__			).to_tuple()

            def recursive_check(lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		List[Any]			):
               if isinstance(UpperCamelCase__						,     (List, Tuple)			):
                  for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__						,     UpperCamelCase__			):
                     recursive_check(UpperCamelCase__						,     UpperCamelCase__			)
               elif isinstance(UpperCamelCase__						,     UpperCamelCase__			):
                  for tuple_iterable_value, dict_iterable_value in zip(
                      tuple_object.values()						,     dict_object.values()			):
                     recursive_check(UpperCamelCase__						,     UpperCamelCase__			)
               elif tuple_object is None:
                  return
               else:
                  self.assertTrue(
                      torch.allclose(
                          set_nan_tensor_to_zero(UpperCamelCase__			)						,     set_nan_tensor_to_zero(UpperCamelCase__			)						,     atol=1e-5			)						,     msg=(
                          "Tuple and dict output are not equal. Difference:"
                          F""" {torch.max(torch.abs(tuple_object - dict_object			)			)}. Tuple has `nan`:"""
                          F""" {torch.isnan(UpperCamelCase__			).any()} and `inf`: {torch.isinf(UpperCamelCase__			)}. Dict has"""
                          F""" `nan`: {torch.isnan(UpperCamelCase__			).any()} and `inf`: {torch.isinf(UpperCamelCase__			)}."""
                      )						,     )

            recursive_check(UpperCamelCase__						,     UpperCamelCase__			)

      for model_class in self.all_model_classes:
         _UpperCAmelCase							:	Any							=   model_class(UpperCamelCase__			)
         model.to(UpperCamelCase__			)
         model.eval()

         _UpperCAmelCase							:	int							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__			)
         _UpperCAmelCase							:	Optional[Any]							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__			)
         check_equivalence(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__			)

         _UpperCAmelCase							:	Optional[Any]							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__			)
         _UpperCAmelCase							:	int							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__			)
         check_equivalence(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__			)

         _UpperCAmelCase							:	str							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__			)
         _UpperCAmelCase							:	Dict							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__			)
         check_equivalence(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     {"output_hidden_states": True}			)

         _UpperCAmelCase							:	str							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__			)
         _UpperCAmelCase							:	str							=   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__			)
         check_equivalence(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     {"output_hidden_states": True}			)





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





   """simple docstring"""





   UpperCamelCase_       :						Any    				=     (MaskFormerSwinBackbone,) if is_torch_available() else ()
   UpperCamelCase_       :						List[str]    				=     MaskFormerSwinConfig




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







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   MaskFormerSwinModelTester(self			)




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







      """simple docstring"""




      _UpperCAmelCase							,       _UpperCAmelCase							:	Any							=   self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase							:	Dict							=   inputs_dict["pixel_values"].shape[0]

      for backbone_class in self.all_model_classes:
         _UpperCAmelCase							:	Optional[Any]							=   backbone_class(UpperCamelCase__			)
         backbone.to(UpperCamelCase__			)
         backbone.eval()

         _UpperCAmelCase							:	Dict							=   backbone(**UpperCamelCase__			)

         # Test default outputs and verify feature maps
         self.assertIsInstance(outputs.feature_maps						,     UpperCamelCase__			)
         self.assertTrue(len(outputs.feature_maps			) == len(backbone.channels			)			)
         for feature_map, n_channels in zip(outputs.feature_maps						,     backbone.channels			):
            self.assertTrue(feature_map.shape[:2]						,     (batch_size, n_channels)			)
         self.assertIsNone(outputs.hidden_states			)
         self.assertIsNone(outputs.attentions			)

         # Test output_hidden_states=True
         _UpperCAmelCase							:	str							=   backbone(**UpperCamelCase__						,     output_hidden_states=UpperCamelCase__			)
         self.assertIsNotNone(outputs.hidden_states			)
         self.assertTrue(len(outputs.hidden_states			)						,     len(backbone.stage_names			)			)
         # We skip the stem layer
         for hidden_states, n_channels in zip(outputs.hidden_states[1:]						,     backbone.channels			):
            for hidden_state in hidden_states:
               # Hidden states are in the format (batch_size, (height * width), n_channels)
               _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:	List[Any]							=   hidden_state.shape
               self.assertTrue((h_batch_size, h_n_channels)						,     (batch_size, n_channels)			)

                 # Test output_attentions=True
         if self.has_attentions:
            _UpperCAmelCase							:	Optional[Any]							=   backbone(**UpperCamelCase__						,     output_attentions=UpperCamelCase__			)
            self.assertIsNotNone(outputs.attentions			)

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,     A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       emb.weight.shape
  __magic_name__				     =       nn.Linear(A_,     A_,     bias=A_					)
  __magic_name__				     =       emb.weight.data
  return lin_layer

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       torch.load(A_,     map_location="""cpu"""					)
  __magic_name__				     =       Namespace(**checkpoint["""cfg"""]["""model"""]					)
  __magic_name__				     =       checkpoint["""model"""]
  remove_ignore_keys_(A_					)
  __magic_name__				     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  __magic_name__				     =       {key.replace("""decoder""",     """model"""					): val for key, val in state_dict.items()}

  __magic_name__				     =       XGLMConfig(
      vocab_size=A_,     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,     )

  __magic_name__				     =       XGLMForCausalLM(A_					)
  __magic_name__				     =       model.load_state_dict(A_,     strict=A_					)
  print(A_					)
  __magic_name__				     =       make_linear_from_emb(model.model.embed_tokens					)

  return model


if __name__ == "__main__":
    __lowerCAmelCase			:     Dict           =   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.')
    __lowerCAmelCase			:     List[str]           =   parser.parse_args()
    __lowerCAmelCase			:     str           =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)

