




'''simple docstring'''
import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_SCREAMING_SNAKE_CASE =	logging.getLogger(__name__)





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		str:
			np.random.seed(args.seed    )
			torch.manual_seed(args.seed    )
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed    )





def  lowerCamelCase(       )		->		Union[str, Any]:
			A_       		=   argparse.ArgumentParser()
			parser.add_argument(
			    '--model_name_or_path'					,default=lowercase__					,type=lowercase__					,required=lowercase__					,)
			parser.add_argument('--prompt'					,type=lowercase__					,default=''    )
			parser.add_argument('--length'					,type=lowercase__					,default=20    )
			parser.add_argument('--stop_token'					,type=lowercase__					,default=lowercase__					,help='Token at which text generation is stopped'    )
			parser.add_argument(
			    '--temperature'					,type=lowercase__					,default=1.0					,help='temperature of 1.0 has no effect, lower tend toward greedy sampling'					,)
			parser.add_argument(
			    '--repetition_penalty'					,type=lowercase__					,default=1.0					,help='primarily useful for CTRL model; in that case, use 1.2'    )
			parser.add_argument('--k'					,type=lowercase__					,default=0    )
			parser.add_argument('--penalty_alpha'					,type=lowercase__					,default=0.0    )
			parser.add_argument('--p'					,type=lowercase__					,default=0.9    )

			parser.add_argument('--prefix'					,type=lowercase__					,default=''					,help='Text added prior to input.'    )
			parser.add_argument('--padding_text'					,type=lowercase__					,default=''					,help='Deprecated, the use of `--prefix` is preferred.'    )
			parser.add_argument('--xlm_language'					,type=lowercase__					,default=''					,help='Optional language when used with the XLM model.'    )

			parser.add_argument('--seed'					,type=lowercase__					,default=42					,help='random seed for initialization'    )
			parser.add_argument('--no_cuda'					,action='store_true'					,help='Avoid using CUDA when available'    )
			parser.add_argument(
			    '--fp16'					,action='store_true'					,help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'					,)
			A_       		=   parser.parse_args()

			A_       		=   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'    )
			A_       		=   0 if args.no_cuda else torch.cuda.device_count()

			logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'    )

			set_seed(lowercase__    )

			# Initialize the model and tokenizer
			A_       		=   AutoTokenizer.from_pretrained(args.model_name_or_path    )
			A_       		=   AutoModelForCausalLM.from_pretrained(args.model_name_or_path    )

			# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
			# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
			model.to(args.device    )

			if args.fpaa:
						model.half()

			logger.info(lowercase__    )
			A_       		=   args.prompt if args.prompt else input('Model prompt >>> '    )

			A_       		=   tokenizer(lowercase__					,return_tensors='pt'					,add_special_tokens=lowercase__    )
			A_       		=   {key: value.to(args.device    ) for key, value in inputs.items()}

			A_       		=   model.generate(
			    **lowercase__					,max_length=args.length + len(inputs['input_ids'][0]    )					,penalty_alpha=args.penalty_alpha					,top_k=args.k					,)

			A_       		=   []
			for generated_sequence_idx, generated_sequence in enumerate(lowercase__    ):
						print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='    )
						A_       		=   generated_sequence.tolist()

						# Decode text
						A_       		=   tokenizer.decode(lowercase__					,clean_up_tokenization_spaces=lowercase__					,add_special_tokens=lowercase__    )

						# Remove all text after the stop token
						A_       		=   text[: text.find(args.stop_token    ) if args.stop_token else None]

						# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
						A_       		=   (
						    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]					,clean_up_tokenization_spaces=lowercase__    )    ) :]
						)

						generated_sequences.append(lowercase__    )
						print(lowercase__    )

			return generated_sequences


if __name__ == "__main__":
							main()







'''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 CLIPImageProcessor
class      __UpperCAmelCase							(    unittest.TestCase							):



					'''simple docstring'''





					def __init__(  self    :	Dict			,					_lowercase    :	List[Any]			,					_lowercase    :	List[Any]=7			,					_lowercase    :	Dict=3			,					_lowercase    :	List[Any]=18			,					_lowercase    :	List[str]=30			,					_lowercase    :	List[str]=400			,					_lowercase    :	Any=True			,					_lowercase    :	Any=None			,					_lowercase    :	Any=True			,					_lowercase    :	Dict=None			,					_lowercase    :	Optional[int]=True			,					_lowercase    :	int=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,					_lowercase    :	Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,					_lowercase    :	Optional[Any]=True			,					)						->       List[str]:
								A_       		=   size if size is not None else {'shortest_edge': 20}
								A_       		=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
								A_       		=   parent
								A_       		=   batch_size
								A_       		=   num_channels
								A_       		=   image_size
								A_       		=   min_resolution
								A_       		=   max_resolution
								A_       		=   do_resize
								A_       		=   size
								A_       		=   do_center_crop
								A_       		=   crop_size
								A_       		=   do_normalize
								A_       		=   image_mean
								A_       		=   image_std
								A_       		=   do_convert_rgb



					def 						__snake_case	(  self    :	Optional[Any])						->       List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}



					def 						__snake_case	(  self    :	Dict			,					_lowercase    :	Tuple=False			,					_lowercase    :	Tuple=False			,					_lowercase    :	Union[str, Any]=False)						->       List[Any]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
											A_       		=   []
											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:
											A_       		=   []
											for i in range(self.batch_size):
														A_		,   A_       		=   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
											A_       		=   [Image.fromarray(np.moveaxis(_lowercase			,					0			,					-1)) for x in image_inputs]

								if torchify:
											A_       		=   [torch.from_numpy(_lowercase) for x in image_inputs]

								return image_inputs





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	Optional[Any])						->       List[str]:
								A_       		=   CLIPImageProcessingTester(self)



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



					def 						__snake_case	(  self    :	Optional[Any])						->       str:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Union[str, Any])						->       Optional[int]:
								A_       		=   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size			,					{'shortest_edge': 20})
								self.assertEqual(image_processor.crop_size			,					{'height': 18, 'width': 18})

								A_       		=   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 						__snake_case	(  self    :	str)						->       List[str]:
								pass



					def 						__snake_case	(  self    :	List[str])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

								# Test not batched input
								A_       		=   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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)

								# Test batched
								A_       		=   image_processing(_lowercase			,					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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)



					def 						__snake_case	(  self    :	Union[str, Any])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					np.ndarray)

								# Test not batched input
								A_       		=   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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)

								# Test batched
								A_       		=   image_processing(_lowercase			,					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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)



					def 						__snake_case	(  self    :	Optional[Any])						->       Tuple:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					torch.Tensor)

								# Test not batched input
								A_       		=   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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)

								# Test batched
								A_       		=   image_processing(_lowercase			,					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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	int)						->       List[Any]:
								A_       		=   CLIPImageProcessingTester(self			,					num_channels=4)
								A_       		=   3



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



					def 						__snake_case	(  self    :	Any)						->       Dict:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Any)						->       Optional[int]:
								pass



					def 						__snake_case	(  self    :	str)						->       List[Any]:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

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

								# Test batched
								A_       		=   image_processing(_lowercase			,					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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)




