
"""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,
)
snake_case			       =		logging.getLogger(__name__)

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

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser()
					parser.add_argument(
					    '--model_name_or_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					)
					parser.add_argument('--prompt',					type=SCREAMING_SNAKE_CASE_,					default=''      )
					parser.add_argument('--length',					type=SCREAMING_SNAKE_CASE_,					default=2_0      )
					parser.add_argument('--stop_token',					type=SCREAMING_SNAKE_CASE_,					default=SCREAMING_SNAKE_CASE_,					help='Token at which text generation is stopped'      )
					parser.add_argument(
					    '--temperature',					type=SCREAMING_SNAKE_CASE_,					default=1.0,					help='temperature of 1.0 has no effect, lower tend toward greedy sampling',					)
					parser.add_argument(
					    '--repetition_penalty',					type=SCREAMING_SNAKE_CASE_,					default=1.0,					help='primarily useful for CTRL model; in that case, use 1.2'      )
					parser.add_argument('--k',					type=SCREAMING_SNAKE_CASE_,					default=0      )
					parser.add_argument('--penalty_alpha',					type=SCREAMING_SNAKE_CASE_,					default=0.0      )
					parser.add_argument('--p',					type=SCREAMING_SNAKE_CASE_,					default=0.9      )

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

					parser.add_argument('--seed',					type=SCREAMING_SNAKE_CASE_,					default=4_2,					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',					)
					SCREAMING_SNAKE_CASE           =     parser.parse_args()

					SCREAMING_SNAKE_CASE           =     torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
					SCREAMING_SNAKE_CASE           =     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(SCREAMING_SNAKE_CASE_      )

					# Initialize the model and tokenizer
					SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(args.model_name_or_path      )
					SCREAMING_SNAKE_CASE           =     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(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     args.prompt if args.prompt else input('Model prompt >>> '      )

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

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

					SCREAMING_SNAKE_CASE           =     []
					for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE_      ):
										print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
										SCREAMING_SNAKE_CASE           =     generated_sequence.tolist()

										# Decode text
										SCREAMING_SNAKE_CASE           =     tokenizer.decode(SCREAMING_SNAKE_CASE_,					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,					add_special_tokens=SCREAMING_SNAKE_CASE_      )

										# Remove all text after the stop token
										SCREAMING_SNAKE_CASE           =     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
										SCREAMING_SNAKE_CASE           =     (
										    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0],					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_      )      ) :]
										)

										generated_sequences.append(SCREAMING_SNAKE_CASE_      )
										print(SCREAMING_SNAKE_CASE_      )

					return generated_sequences


if __name__ == "__main__":
	main()


"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	snake_case			       =		False

if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    PixaStructImageProcessor,
	    PixaStructProcessor,
	    PreTrainedTokenizerFast,
	    TaTokenizer,
	)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,				reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."  ,				)
@require_vision
@require_torch
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE           =     PixaStructImageProcessor()
									SCREAMING_SNAKE_CASE           =     TaTokenizer.from_pretrained('t5-small'      )

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(lowercase__		,     lowercase__      )

									processor.save_pretrained(self.tmpdirname      )


				def 							A (    self		,     **lowercase__      )   ->   Dict:
									"""simple docstring"""







									return AutoProcessor.from_pretrained(self.tmpdirname		,     **lowercase__      ).tokenizer


				def 							A (    self		,     **lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									return AutoProcessor.from_pretrained(self.tmpdirname		,     **lowercase__      ).image_processor


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname      )


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta      )]

									SCREAMING_SNAKE_CASE           =     [Image.fromarray(np.moveaxis(lowercase__		,     0		,     -1      )      ) for x in image_inputs]

									return image_inputs


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()      )
									processor.save_pretrained(self.tmpdirname      )

									SCREAMING_SNAKE_CASE           =     self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )
									SCREAMING_SNAKE_CASE           =     self.get_image_processor(do_normalize=lowercase__		,     padding_value=1.0      )

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor.from_pretrained(
									    self.tmpdirname		,     bos_token='(BOS)'		,     eos_token='(EOS)'		,     do_normalize=lowercase__		,     padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer		,     lowercase__      )

									self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.image_processor		,     lowercase__      )


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     image_processor(lowercase__		,     return_tensors='np'      )
									SCREAMING_SNAKE_CASE           =     processor(images=lowercase__		,     return_tensors='np'      )

									for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1E-2      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									SCREAMING_SNAKE_CASE           =     tokenizer(lowercase__		,     return_token_type_ids=lowercase__		,     add_special_tokens=lowercase__      )

									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]		,     encoded_processor[key]      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									self.assertListEqual(
									    list(inputs.keys()      )		,     ['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

									# test if it raises when no input is passed
									with pytest.raises(lowercase__      ):
														processor()


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									SCREAMING_SNAKE_CASE           =     [512, 1024, 2048, 4096]
									SCREAMING_SNAKE_CASE           =     [770, 770, 770, 770]
									# with text
									for i, max_patch in enumerate(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__		,     max_patches=lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[0]		,     lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[1]		,     expected_hidden_size[i]      )

									# without text input
									for i, max_patch in enumerate(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     processor(images=lowercase__		,     max_patches=lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[0]		,     lowercase__      )
														self.assertEqual(inputs['flattened_patches'][0].shape[1]		,     expected_hidden_size[i]      )


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE           =     processor.batch_decode(lowercase__      )
									SCREAMING_SNAKE_CASE           =     tokenizer.batch_decode(lowercase__      )

									self.assertListEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_image_processor()
									SCREAMING_SNAKE_CASE           =     self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =     PixaStructProcessor(tokenizer=lowercase__		,     image_processor=lowercase__      )

									SCREAMING_SNAKE_CASE           =     'lower newer'
									SCREAMING_SNAKE_CASE           =     self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__		,     images=lowercase__      )

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(
									    list(inputs.keys()      )		,     ['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

									SCREAMING_SNAKE_CASE           =     processor(text=lowercase__      )

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(list(inputs.keys()      )		,     ['input_ids', 'attention_mask']      )
