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_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor






class 		a__						(					unittest.TestCase					):

					def __init__(    self      :       List[Any],_A      :       Any,_A      :       Union[str, Any]=7,_A      :       Optional[Any]=3,_A      :       Optional[int]=10,_A      :       Optional[int]=18,_A      :       Optional[Any]=30,_A      :       Optional[Any]=400,_A      :       int=True,_A      :       Dict=None,_A      :       Any=True,_A      :       List[Any]=[0.5, 0.5, 0.5],_A      :       Any=[0.5, 0.5, 0.5],_A      :       str=None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    size if size is not None else {"shortest_edge": 18}
							SCREAMING_SNAKE_CASE_  :		str				      =    crop_size if crop_size is not None else {"height": 18, "width": 18}

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    num_frames
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    min_resolution
							SCREAMING_SNAKE_CASE_  :		Dict				      =    max_resolution
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    do_resize
							SCREAMING_SNAKE_CASE_  :		int				      =    size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    do_normalize
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    image_mean
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image_std
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    crop_size






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}







@require_torch
@require_vision
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							VivitImageProcessor if is_vision_available() else None

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    VivitImageProcessingTester(self    )

					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(_A,"image_mean"    )    )
							self.assertTrue(hasattr(_A,"image_std"    )    )
							self.assertTrue(hasattr(_A,"do_normalize"    )    )
							self.assertTrue(hasattr(_A,"do_resize"    )    )
							self.assertTrue(hasattr(_A,"do_center_crop"    )    )
							self.assertTrue(hasattr(_A,"size"    )    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.image_processing_class.from_dict(self.image_processor_dict    )
							self.assertEqual(image_processor.size,{"shortest_edge": 18}    )
							self.assertEqual(image_processor.crop_size,{"height": 18, "width": 18}    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84    )
							self.assertEqual(image_processor.size,{"shortest_edge": 42}    )
							self.assertEqual(image_processor.crop_size,{"height": 84, "width": 84}    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PIL videos
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    prepare_video_inputs(self.image_processor_tester,equal_resolution=_A    )
							for video in video_inputs:
									self.assertIsInstance(_A,_A    )
									self.assertIsInstance(video[0],Image.Image    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		int				      =    image_processing(video_inputs[0],return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image_processing(_A,return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_  :		str				      =    prepare_video_inputs(self.image_processor_tester,equal_resolution=_A,numpify=_A    )
							for video in video_inputs:
									self.assertIsInstance(_A,_A    )
									self.assertIsInstance(video[0],np.ndarray    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    image_processing(video_inputs[0],return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    image_processing(_A,return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_  :		Dict				      =    prepare_video_inputs(self.image_processor_tester,equal_resolution=_A,torchify=_A    )
							for video in video_inputs:
									self.assertIsInstance(_A,_A    )
									self.assertIsInstance(video[0],torch.Tensor    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    image_processing(video_inputs[0],return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    image_processing(_A,return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_videos.shape,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),)

import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    question_encoder_name_or_path

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class.from_pretrained_question_encoder_generator(
		    lowerCAmelCase   , lowerCAmelCase   , config=lowerCAmelCase	)
		rag_model.save_pretrained(lowerCAmelCase	)

		# Sanity check.
		model_class.from_pretrained(lowerCAmelCase	)

		# Save tokenizers.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[Any]       =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type''',
		    choices=['''rag_sequence''', '''rag_token'''],
		    required=True,
		    type=str,
		    help='''RAG model type: rag_sequence, rag_token''',
		)
		parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
		parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
		parser.add_argument(
		    '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
		)

		parser.add_argument(
		    '''--generator_tokenizer_name_or_path''',
		    type=str,
		    help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
		)
		parser.add_argument(
		    '''--question_encoder_tokenizer_name_or_path''',
		    type=str,
		    help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
		)
		parser.add_argument(
		    '''--config_name_or_path''',
		    type=str,
		    help=(
		        '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
		        ''' ``model_type``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	int       =  Path(args.dest)
		dest_dir.mkdir(exist_ok=True)

		consolidate(
		    args.model_type,
		    args.generator_name_or_path,
		    args.question_encoder_name_or_path,
		    dest_dir,
		    args.config_name_or_path,
		    args.generator_tokenizer_name_or_path,
		    args.question_encoder_tokenizer_name_or_path,
		)
