
'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_keras_nlp_available():
							from transformers.models.gpta import TFGPTaTokenizer


_lowercase :  Optional[int]    							=       ["gpt2"]
_lowercase :  Union[str, Any]    							=       "gpt2"

if is_tf_available():






							class  lowerCAmelCase__   (  tf.Module     ):






												def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		super().__init__()
																		lowercase_	:     int													=       tokenizer
																		lowercase_	:     List[str]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Optional[int]													=       TFGPTaLMHeadModel.from_config(__SCREAMING_SNAKE_CASE		)






												@tf.function(input_signature=(tf.TensorSpec((None,)				,							tf.string				,							name='''text'''		),)		)
												def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		lowercase_	:     Dict													=       self.tokenizer(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Dict													=       tokenized['''input_ids'''].to_tensor()

																		lowercase_	:     int													=       tf.cast(input_ids_dense > 0				,							tf.intaa		)
																		# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																		lowercase_	:     Any													=       self.model(input_ids=__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE		)['''logits''']

																		return outputs






@require_tf
@require_keras_nlp
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											lowercase_	:     List[str]													=       [GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											lowercase_	:     Dict													=       [TFGPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in TOKENIZER_CHECKPOINTS]
											assert len(self.tokenizers		) == len(self.tf_tokenizers		)

											lowercase_	:     Optional[Any]													=       [
											    '''This is a straightforward English test sentence.''',
											    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
											    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
											    '''And some much more rare Chinese: 齉 堃 齉堃''',
											    '''Je vais aussi écrire en français pour tester les accents''',
											    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
											]
											lowercase_	:     Optional[int]													=       list(zip(self.test_sentences				,							self.test_sentences[::-1]		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, tf_tokenizer in zip(self.tokenizers				,							self.tf_tokenizers		):
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tokenizer([test_inputs]				,							return_tensors='''tf'''		)
																							lowercase_	:     Tuple													=       tf_tokenizer([test_inputs]		)

																							for key in python_outputs.keys():
																													# convert them to numpy to avoid messing with ragged tensors
																													lowercase_	:     int													=       python_outputs[key].numpy()
																													lowercase_	:     List[str]													=       tf_outputs[key].numpy()

																													self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(__SCREAMING_SNAKE_CASE				,							tf.intaa		) == tf_outputs_values		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     str													=       tf.function(__SCREAMING_SNAKE_CASE		)
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tf.constant(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       compiled_tokenizer(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       ModelToSave(tokenizer=__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Tuple													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Union[str, Any]													=       model.serving(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							lowercase_	:     Union[str, Any]													=       Path(__SCREAMING_SNAKE_CASE		) / '''saved.model'''
																							tf.saved_model.save(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							signatures={'''serving_default''': model.serving}		)
																							lowercase_	:     List[str]													=       tf.saved_model.load(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Any													=       loaded_model.signatures['''serving_default'''](__SCREAMING_SNAKE_CASE		)['''output_0''']
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertTrue(tf.reduce_all(out == loaded_output		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Optional[int]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs

																	lowercase_	:     Tuple													=       tf_tokenizer.get_config()
																	lowercase_	:     int													=       TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     List[Any]													=       model_from_config(__SCREAMING_SNAKE_CASE		)

																	for key in from_config_output.keys():
																							self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	lowercase_	:     Union[str, Any]													=       12_31_23

																	for max_length in [3, 5, 10_24]:
																							lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																							lowercase_	:     Dict													=       tf_tokenizer(__SCREAMING_SNAKE_CASE				,							max_length=__SCREAMING_SNAKE_CASE		)

																							lowercase_	:     Optional[Any]													=       out['''input_ids'''].numpy().shape[1]

																							assert out_length == max_length







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=sys.maxsize		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					'''bilinear'''
         A_      :				Optional[Any]		     =					max_size
         A_      :				Optional[Any]		     =					short_edge_length







       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					[]
         for img in imgs:
           A_     ,       A_      :				List[str]		     =					img.shape[:2]
           # later: provide list and randomly choose index for resize
           A_      :				List[Any]		     =					np.random.randint(self.short_edge_length[0]				,       self.short_edge_length[1] + 1		)
           if size == 0:
             return img
           A_      :				int		     =					size * 1.0 / min(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
           if h < w:
             A_     ,       A_      :				Tuple		     =					size, scale * w
           else:
             A_     ,       A_      :				List[str]		     =					scale * h, size
           if max(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) > self.max_size:
             A_      :				List[Any]		     =					self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
             A_      :				Any		     =					newh * scale
             A_      :				List[str]		     =					neww * scale
           A_      :				List[Any]		     =					int(neww + 0.5		)
           A_      :				Tuple		     =					int(newh + 0.5		)

           if img.dtype == np.uinta:
             A_      :				List[str]		     =					Image.fromarray(_SCREAMING_SNAKE_CASE		)
             A_      :				Optional[Any]		     =					pil_image.resize((neww, newh)				,       PILImageResampling.BILINEAR		)
             A_      :				Dict		     =					np.asarray(_SCREAMING_SNAKE_CASE		)
           else:
             A_      :				Any		     =					img.permute(2				,       0				,       1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
             A_      :				List[str]		     =					nn.functional.interpolate(
                 _SCREAMING_SNAKE_CASE				,       (newh, neww)				,       mode=self.interp_method				,       align_corners=_SCREAMING_SNAKE_CASE		).squeeze(0		)
           img_augs.append(_SCREAMING_SNAKE_CASE		)

         return img_augs






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Tuple		     =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,       cfg.INPUT.MAX_SIZE_TEST		)
         A_      :				Union[str, Any]		     =					cfg.INPUT.FORMAT
         A_      :				int		     =					cfg.SIZE_DIVISIBILITY
         A_      :				Tuple		     =					cfg.PAD_VALUE
         A_      :				List[Any]		     =					cfg.INPUT.MAX_SIZE_TEST
         A_      :				List[str]		     =					cfg.MODEL.DEVICE
         A_      :				Dict		     =					torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)				,       1				,       1		)
         A_      :				List[Any]		     =					torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)				,       1				,       1		)
         A_      :				List[Any]		     =					lambda _SCREAMING_SNAKE_CASE		: (x - self.pixel_mean) / self.pixel_std



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         A_      :				Any		     =					tuple(max(_SCREAMING_SNAKE_CASE		) for s in zip(*[img.shape for img in images]		)		)
         A_      :				List[Any]		     =					[im.shape[-2:] for im in images]
         A_      :				Any		     =					[
             nn.functional.pad(
                 _SCREAMING_SNAKE_CASE				,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,       value=self.pad_value				,       )
             for size, im in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         ]

         return torch.stack(_SCREAMING_SNAKE_CASE		), torch.tensor(_SCREAMING_SNAKE_CASE		)







       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Dict:




         '''simple docstring'''





         with torch.no_grad():
           if not isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
             A_      :				Dict		     =					[images]
           if single_image:
             assert len(_SCREAMING_SNAKE_CASE		) == 1
           for i in range(len(_SCREAMING_SNAKE_CASE		)		):
             if isinstance(images[i]				,       torch.Tensor		):
               images.insert(_SCREAMING_SNAKE_CASE				,       images.pop(_SCREAMING_SNAKE_CASE		).to(self.device		).float()		)
             elif not isinstance(images[i]				,       torch.Tensor		):
               images.insert(
                   _SCREAMING_SNAKE_CASE				,       torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE		)				,       input_format=self.input_format		)		)
                   .to(self.device		)
                   .float()				,       )
            # resize smallest edge
           A_      :				List[str]		     =					torch.tensor([im.shape[:2] for im in images]		)
           A_      :				Union[str, Any]		     =					self.aug(_SCREAMING_SNAKE_CASE		)
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           A_      :				List[str]		     =					[self.normalizer(_SCREAMING_SNAKE_CASE		) for x in images]
           # now pad them to do the following operations
           A_     ,       A_      :				Any		     =					self.pad(_SCREAMING_SNAKE_CASE		)
           # Normalize

           if self.size_divisibility > 0:
             raise NotImplementedError()
           # pad
           A_      :				str		     =					torch.true_divide(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
           if single_image:
             return images[0], sizes[0], scales_yx[0]
           else:
             return images, sizes, scales_yx




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  assert torch.isfinite(SCREAMING_SNAKE_CASE       ).all(), "Box tensor contains infinite or NaN!"
  A_     ,       A_      :				int		     =					box_size
  tensor[:, 0].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 1].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 2].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 3].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )




