

'''simple docstring'''

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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=7,						lowercase_=3,						lowercase_=18,						lowercase_=30,						lowercase_=400,						lowercase_=True,						lowercase_=None,						lowercase_=True,						lowercase_=False,						lowercase_=[0.48_145_466, 0.4_578_275, 0.40_821_073],						lowercase_=[0.26_862_954, 0.26_130_258, 0.27_577_711],						lowercase_=True,						)    ->							int:
					snake_case							=					size if size is not None else {'''height''': 20, '''width''': 20}
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					num_channels
					snake_case							=					image_size
					snake_case							=					min_resolution
					snake_case							=					max_resolution
					snake_case							=					do_resize
					snake_case							=					size
					snake_case							=					do_normalize
					snake_case							=					image_mean
					snake_case							=					image_std
					snake_case							=					do_pad
					snake_case							=					do_convert_rgb

				def 					_lowerCamelCase    (       self )    ->							Dict:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					    "do_pad": self.do_pad,
					}






				def 					_lowerCamelCase    (       self,						lowercase_=False,						lowercase_=False,						lowercase_=False )    ->							Optional[Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
						snake_case							=					[]
						for i in range(self.batch_size ):
							image_inputs.append(
							    np.random.randint(
							        255,						size=(self.num_channels, self.max_resolution, self.max_resolution),						dtype=np.uinta ) )
					else:
						snake_case							=					[]
						for i in range(self.batch_size ):
							snake_case							=					np.random.choice(np.arange(self.min_resolution,						self.max_resolution ),						2 )
							image_inputs.append(np.random.randint(255,						size=(self.num_channels, width, height),						dtype=np.uinta ) )

					if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						snake_case							=					[Image.fromarray(np.moveaxis(lowerCamelCase_,						0,						-1 ) ) for x in image_inputs]

					if torchify:
						snake_case							=					[torch.from_numpy(lowerCamelCase_ ) for x in image_inputs]

					return image_inputs







@require_torch
@require_vision
class lowerCamelCase   (    _lowerCamelCase				,				unittest.TestCase				):
				snake_case_							     = BlipImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					BlipImageProcessingTester(self )

				@property
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'size' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'image_std' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_convert_rgb' ) )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					pass

				def 					_lowerCamelCase    (       self )    ->							Any:
					# Initialize image_processor
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					snake_case							=					self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_ )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_,						Image.Image )

					# Test not batched input
					snake_case							=					image_processor(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
					snake_case							=					image_processor(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'],
					    ),						)

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# Initialize image_processor
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					snake_case							=					self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_,						numpify=lowerCamelCase_ )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_,						np.ndarray )

					# Test not batched input
					snake_case							=					image_processor(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
					snake_case							=					image_processor(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'],
					    ),						)






				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					# Initialize image_processor
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					snake_case							=					self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_,						torchify=lowerCamelCase_ )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_,						torch.Tensor )

					# Test not batched input
					snake_case							=					image_processor(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
					snake_case							=					image_processor(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'],
					    ),						)







@require_torch
@require_vision
class lowerCamelCase   (    _lowerCamelCase				,				unittest.TestCase				):
				snake_case_							     = BlipImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					BlipImageProcessingTester(self,						num_channels=4 )
					snake_case							=					3

				@property
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'size' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'image_std' ) )
					self.assertTrue(hasattr(lowerCamelCase_,						'do_convert_rgb' ) )

				def 					_lowerCamelCase    (       self )    ->							str:
					pass






				def 					_lowerCamelCase    (       self )    ->							Any:
					# Initialize image_processor
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					snake_case							=					self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase_ )
					for image in image_inputs:
						self.assertIsInstance(lowerCamelCase_,						Image.Image )

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

					# Test batched
					snake_case							=					image_processor(lowerCamelCase_,						return_tensors='pt' ).pixel_values
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size['height'],
					        self.image_processor_tester.size['width'],
					    ),						)











import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__lowerCamelCase :    Any      						=      get_logger(__name__)







def 		lowercase__ (		__A:						str				,__A:						str					):





					'''simple docstring'''






					if text.startswith(__A					):
										return text[len(__A					) :]
					return text  # or whatever







def 		lowercase__ (		__A:						List[str]					):





					'''simple docstring'''






					return {remove_prefix(__A				,'''model.'''					): v for k, v in sd.items()}







def 		lowercase__ (		__A:						List[Dict[str, torch.Tensor]]					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				{}
					for k in state_dicts[0].keys():
										__magic_name__				:						str     				=				[sd[k] for sd in state_dicts]
										__magic_name__				:						List[Any]     				=				sum(__A					) / len(__A					)
										assert isinstance(__A				,torch.Tensor					)
										__magic_name__				:						Any     				=				new_t
					return new_sd







def 		lowercase__ (		__A:						str				,__A:						str				,__A:						str					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				AutoModelForSeqaSeqLM.from_pretrained(__A					)
					if os.path.isfile(__A					):
										__magic_name__				:						str     				=				[pl_ckpt_path]
					else:
										assert os.path.isdir(__A					)
										__magic_name__				:						List[str]     				=				list(Path(__A					).glob('''*.ckpt'''					)					)
										assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

					if len(__A					) > 1:
										logger.info(F'''averaging the weights of {ckpt_files}'''					)

					__magic_name__				:						Union[str, Any]     				=				[sanitize(torch.load(__A				,map_location='''cpu'''					)['''state_dict''']					) for x in ckpt_files]
					__magic_name__				:						Dict     				=				average_state_dicts(__A					)

					__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				hf_model.load_state_dict(__A				,strict=__A					)
					assert not missing, F'''missing keys: {missing}'''
					hf_model.save_pretrained(__A					)
					try:
										__magic_name__				:						Optional[Any]     				=				AutoTokenizer.from_pretrained(__A					)
										tok.save_pretrained(__A					)
					except Exception:
										pass
										# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)



