



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor






class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						Any						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Tuple=13						,  __UpperCAmelCase     :						Optional[int]=3						,  __UpperCAmelCase     :						Union[str, Any]=224						,  __UpperCAmelCase     :						Dict=30						,  __UpperCAmelCase     :						Optional[int]=400						,  __UpperCAmelCase     :						int=True						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Any=True						,  __UpperCAmelCase     :						int=[0.5, 0.5, 0.5]						,  __UpperCAmelCase     :						str=[0.5, 0.5, 0.5]						,  )      ->			Optional[int]:
							UpperCAmelCase_=       size if size is not None else {"height": 18, "width": 18}
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       min_resolution
							UpperCAmelCase_=       max_resolution
							UpperCAmelCase_=       do_resize
							UpperCAmelCase_=       size
							UpperCAmelCase_=       do_normalize
							UpperCAmelCase_=       image_mean
							UpperCAmelCase_=       image_std




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}






@require_torch
@require_vision
class 		lowercase						(			__snake_case			,   unittest.TestCase):






	"""simple docstring"""


	a__				: List[str]		       =							ViTImageProcessor if is_vision_available() else None




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       EfficientFormerImageProcessorTester(self					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							return self.image_proc_tester.prepare_image_processor_dict()




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(UpperCamelCase__						,  """image_mean"""					)					)
							self.assertTrue(hasattr(UpperCamelCase__						,  """image_std"""					)					)
							self.assertTrue(hasattr(UpperCamelCase__						,  """do_normalize"""					)					)
							self.assertTrue(hasattr(UpperCamelCase__						,  """do_resize"""					)					)
							self.assertTrue(hasattr(UpperCamelCase__						,  """size"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Dict:
							pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCAmelCase_=       prepare_image_inputs(self.image_proc_tester						,  equal_resolution=UpperCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__						,  Image.Image					)

							# Test not batched input
							UpperCAmelCase_=       image_processor(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processor(UpperCamelCase__						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[int]:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_proc_tester						,  equal_resolution=UpperCamelCase__						,  numpify=UpperCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__						,  np.ndarray					)

							# Test not batched input
							UpperCAmelCase_=       image_processor(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processor(UpperCamelCase__						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			int:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_proc_tester						,  equal_resolution=UpperCamelCase__						,  torchify=UpperCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__						,  torch.Tensor					)

							# Test not batched input
							UpperCAmelCase_=       image_processor(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processor(UpperCamelCase__						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )						,  )









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



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

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

						UpperCAmelCase_=       XGLMConfig(
						    vocab_size=lowerCAmelCase_      ,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      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


if __name__ == "__main__":
	__A       =						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.''')
	__A       =						parser.parse_args()
	__A       =						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)




