





'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase=7     ,_lowerCamelCase=3     ,_lowerCamelCase=18     ,_lowerCamelCase=30     ,_lowerCamelCase=400     ,_lowerCamelCase=True     ,_lowerCamelCase=None     ,_lowerCamelCase=True     ,_lowerCamelCase=False     ,_lowerCamelCase=True     ,_lowerCamelCase=True     ,_lowerCamelCase=[0.5, 0.5, 0.5]     ,_lowerCamelCase=[0.5, 0.5, 0.5]     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							parent
				__lowercase										=							batch_size
				__lowercase										=							num_channels
				__lowercase										=							image_size
				__lowercase										=							min_resolution
				__lowercase										=							max_resolution
				__lowercase										=							do_resize
				__lowercase										=							size if size is not None else {'''height''': 18, '''width''': 20}
				__lowercase										=							do_thumbnail
				__lowercase										=							do_align_axis
				__lowercase										=							do_pad
				__lowercase										=							do_normalize
				__lowercase										=							image_mean
				__lowercase										=							image_std


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_thumbnail": self.do_thumbnail,
				    "do_align_long_axis": self.do_align_axis,
				    "do_pad": self.do_pad,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






@require_torch
@require_vision
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				List[Any]											=       DonutImageProcessor if is_vision_available() else None


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							DonutImageProcessingTester(self )


	@property
	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_resize''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''size''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_thumbnail''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_align_long_axis''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_pad''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''do_normalize''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''image_mean''' ) )
				self.assertTrue(hasattr(_lowerCamelCase     ,'''image_std''' ) )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size     ,{'''height''': 18, '''width''': 20} )

				__lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict     ,size=42 )
				self.assertEqual(image_processor.size     ,{'''height''': 42, '''width''': 42} )

				# Previous config had dimensions in (width, height) order
				__lowercase										=							self.image_processing_class.from_dict(self.image_processor_dict     ,size=(42, 84) )
				self.assertEqual(image_processor.size     ,{'''height''': 84, '''width''': 42} )


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				pass


	@is_flaky()
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				__lowercase										=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_lowerCamelCase )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase     ,Image.Image )

				# Test not batched input
				__lowercase										=							image_processing(image_inputs[0]     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)

				# Test batched
				__lowercase										=							image_processing(_lowerCamelCase     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)


	@is_flaky()
	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				__lowercase										=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_lowerCamelCase     ,numpify=_lowerCamelCase )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase     ,np.ndarray )

				# Test not batched input
				__lowercase										=							image_processing(image_inputs[0]     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)

				# Test batched
				__lowercase										=							image_processing(_lowerCamelCase     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)


	@is_flaky()
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				__lowercase										=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_lowerCamelCase     ,torchify=_lowerCamelCase )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase     ,torch.Tensor )

				# Test not batched input
				__lowercase										=							image_processing(image_inputs[0]     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)

				# Test batched
				__lowercase										=							image_processing(_lowerCamelCase     ,return_tensors='''pt''' ).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )     ,)












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





