
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[str]=13			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Optional[int]=2			,							UpperCamelCase__			: List[Any]=3			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Optional[Any]=32			,							UpperCamelCase__			: int=5			,							UpperCamelCase__			: List[Any]=4			,							UpperCamelCase__			: List[str]=37			,							UpperCamelCase__			: Any="gelu"			,							UpperCamelCase__			: List[Any]=0.1			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: Dict=10			,							UpperCamelCase__			: List[Any]=0.02			,							)			->	Dict:







    """simple docstring"""






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

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (image_size // patch_size) ** 2
    __magic_name__				     =       num_patches + 1
  def 							_lowercase							( self			: Any    )			->	Optional[int]:







    """simple docstring"""






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

    __magic_name__				     =       ViTConfig(
        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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)

    return config, pixel_values
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModel(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (self.image_size, self.image_size)
    __magic_name__				     =       (self.patch_size, self.patch_size)
    __magic_name__				     =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.type_sequence_label_size
    __magic_name__				     =       FlaxViTForImageClassification(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

    # test greyscale images
    __magic_name__				     =       1
    __magic_name__				     =       FlaxViTForImageClassification(UpperCamelCase__    )

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





  def 							_lowercase							( self			: int    )			->	int:







    """simple docstring"""






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







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
  def 							_lowercase							( self			: Tuple    )			->	None:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: int    )			->	Dict:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 							_lowercase							( self			: Optional[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )
  def 							_lowercase							( self			: str    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

        @jax.jit
        def model_jitted(UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Dict    ):
          return model(pixel_values=UpperCamelCase__			,							**UpperCamelCase__    )

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

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

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





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







    """simple docstring"""






    for model_class_name in self.all_model_classes:
      __magic_name__				     =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
      __magic_name__				     =       model(np.ones((1, 3, 224, 224)    )    )
      self.assertIsNotNone(UpperCamelCase__    )



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)

