

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a		=logging.get_logger(__name__)


if is_vision_available():
					import PIL






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict										=				['''pixel_values''']
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Union[int, float] = 1 / 2_5_5       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			size if size is not None else {'shortest_edge': 2_2_4}
								__lowerCamelCase    :    List[str] 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase    :    List[str] 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__       ,param_name='crop_size')

								__lowerCamelCase    :    Any 	=			do_resize
								__lowerCamelCase    :    Tuple 	=			size
								__lowerCamelCase    :    Tuple 	=			resample
								__lowerCamelCase    :    Dict 	=			do_center_crop
								__lowerCamelCase    :    List[Any] 	=			crop_size
								__lowerCamelCase    :    Optional[Any] 	=			do_rescale
								__lowerCamelCase    :    Dict 	=			rescale_factor
								__lowerCamelCase    :    str 	=			do_normalize
								__lowerCamelCase    :    Optional[Any] 	=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCamelCase    :    int 	=			image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCamelCase    :    Dict 	=			do_convert_rgb
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int]       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								__lowerCamelCase    :    int 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__)
								if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
								__lowerCamelCase    :    Tuple 	=			get_resize_output_image_size(SCREAMING_SNAKE_CASE__       ,size=size['shortest_edge']       ,default_to_square=SCREAMING_SNAKE_CASE__)
								return resize(SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      Tuple       ,):
								__lowerCamelCase    :    Dict 	=			get_size_dict(SCREAMING_SNAKE_CASE__)
								if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
								return center_crop(SCREAMING_SNAKE_CASE__       ,size=(size['height'], size['width'])       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Union[int, float]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      Dict       ,):
								return rescale(SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__		:      Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__		:      List[str]       ,):
								return normalize(SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      ImageInput       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__		:      PILImageResampling = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      int = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      float = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__		:      bool = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[str, TensorType]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[ChannelDimension] = ChannelDimension.FIRST       ,**SCREAMING_SNAKE_CASE__		:      int       ,):
								__lowerCamelCase    :    str 	=			do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase    :    Optional[Any] 	=			size if size is not None else self.size
								__lowerCamelCase    :    Tuple 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,param_name='size'       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			resample if resample is not None else self.resample
								__lowerCamelCase    :    Optional[int] 	=			do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase    :    Optional[int] 	=			crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase    :    int 	=			get_size_dict(SCREAMING_SNAKE_CASE__       ,param_name='crop_size'       ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase    :    Dict 	=			rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase    :    List[str] 	=			do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase    :    int 	=			image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase    :    Tuple 	=			image_std if image_std is not None else self.image_std
								__lowerCamelCase    :    Optional[Any] 	=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCamelCase    :    Dict 	=			make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_SNAKE_CASE__):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.')

								if do_resize and size is None:
									raise ValueError('Size must be specified if do_resize is True.')

								if do_center_crop and crop_size is None:
									raise ValueError('Crop size must be specified if do_center_crop is True.')

								if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.')

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.')

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									__lowerCamelCase    :    Union[str, Any] 	=			[convert_to_rgb(SCREAMING_SNAKE_CASE__) for image in images]

								# All transformations expect numpy arrays.
								__lowerCamelCase    :    str 	=			[to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

								if do_resize:
									__lowerCamelCase    :    Optional[Any] 	=			[self.resize(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__) for image in images]

								if do_center_crop:
									__lowerCamelCase    :    List[str] 	=			[self.center_crop(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase    :    Optional[Any] 	=			[self.rescale(image=SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase    :    List[Any] 	=			[self.normalize(image=SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase    :    str 	=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase    :    List[Any] 	=			{'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,tensor_type=SCREAMING_SNAKE_CASE__)







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


a		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

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

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    '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',
								]
								__lowerCamelCase    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[Any] 	=			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    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			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    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			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    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    List[Any] 	=			out['input_ids'].numpy().shape[1]

										assert out_length == max_length




