





"""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


a						: Optional[Any]     	=			['''gpt2''']
a						: Union[str, Any]     	=			'''gpt2'''

if is_tf_available():


		class        __UpperCamelCase				(						tf.Module						):






									def __init__(   self      ,					lowerCAmelCase__      )	-> Optional[int]:
										super().__init__()
										a      :		str	=				tokenizer
										a      :		Dict	=				AutoConfig.from_pretrained(lowerCAmelCase__      )
										a      :		Optional[int]	=				TFGPTaLMHeadModel.from_config(lowerCAmelCase__      )






									@tf.function(input_signature=(tf.TensorSpec((None,)      ,					tf.string      ,					name="text"      ),)      )
									def     __a   (   self      ,					lowerCAmelCase__      )	-> int:
										a      :		str	=				self.tokenizer(lowerCAmelCase__      )
										a      :		Dict	=				tokenized["input_ids"].to_tensor()

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

										a      :		int	=				self.model(input_ids=lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      )["logits"]

										return outputs


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






							def     __a   (   self      )	-> List[str]:
								super().setUp()

								a      :		str	=				[GPTaTokenizer.from_pretrained(lowerCAmelCase__      ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								a      :		Tuple	=				[TFGPTaTokenizer.from_pretrained(lowerCAmelCase__      ) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

								a      :		List[str]	=				[
								    "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",
								]
								a      :		str	=				list(zip(self.test_sentences      ,					self.test_sentences[::-1]      )      )






							def     __a   (   self      )	-> List[str]:
								for tokenizer, tf_tokenizer in zip(self.tokenizers      ,					self.tf_tokenizers      ):
									for test_inputs in self.test_sentences:
										a      :		Optional[Any]	=				tokenizer([test_inputs]      ,					return_tensors="tf"      )
										a      :		Union[str, Any]	=				tf_tokenizer([test_inputs]      )

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											a      :		Dict	=				python_outputs[key].numpy()
											a      :		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(lowerCAmelCase__      ,					tf.intaa      ) == tf_outputs_values      )      )






							@slow
							def     __a   (   self      )	-> str:
								for tf_tokenizer in self.tf_tokenizers:
									a      :		Any	=				tf.function(lowerCAmelCase__      )
									for test_inputs in self.test_sentences:
										a      :		Optional[Any]	=				tf.constant(lowerCAmelCase__      )
										a      :		int	=				compiled_tokenizer(lowerCAmelCase__      )
										a      :		Any	=				tf_tokenizer(lowerCAmelCase__      )

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






							@slow
							def     __a   (   self      )	-> Dict:
								for tf_tokenizer in self.tf_tokenizers:
									a      :		List[str]	=				ModelToSave(tokenizer=lowerCAmelCase__      )
									a      :		str	=				tf.convert_to_tensor([self.test_sentences[0]]      )
									a      :		Optional[int]	=				model.serving(lowerCAmelCase__      )  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										a      :		int	=				Path(lowerCAmelCase__      ) / "saved.model"
										tf.saved_model.save(lowerCAmelCase__      ,					lowerCAmelCase__      ,					signatures={"serving_default": model.serving}      )
										a      :		Any	=				tf.saved_model.load(lowerCAmelCase__      )
									a      :		int	=				loaded_model.signatures["serving_default"](lowerCAmelCase__      )["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     __a   (   self      )	-> int:
								for tf_tokenizer in self.tf_tokenizers:
									a      :		Optional[int]	=				tf.convert_to_tensor([self.test_sentences[0]]      )
									a      :		int	=				tf_tokenizer(lowerCAmelCase__      )  # Build model with some sample inputs

									a      :		Union[str, Any]	=				tf_tokenizer.get_config()
									a      :		List[Any]	=				TFGPTaTokenizer.from_config(lowerCAmelCase__      )
									a      :		Dict	=				model_from_config(lowerCAmelCase__      )

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






							@slow
							def     __a   (   self      )	-> List[str]:
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									a      :		List[Any]	=				12_3123

									for max_length in [3, 5, 1024]:
										a      :		Dict	=				tf.convert_to_tensor([self.test_sentences[0]]      )
										a      :		List[Any]	=				tf_tokenizer(lowerCAmelCase__      ,					max_length=lowerCAmelCase__      )

										a      :		List[str]	=				out["input_ids"].numpy().shape[1]

										assert out_length == max_length










from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




