






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    lowercase__   (	tf.Module		):

		def __init__(							self						,			__UpperCAmelCase				)->    int:







								'''simple docstring'''





								super().__init__()
								lowerCAmelCase__       					=  tokenizer
								lowerCAmelCase__       					=  AutoConfig.from_pretrained(__a				)
								lowerCAmelCase__       					=  TFGPTaLMHeadModel.from_config(__a				)


		@tf.function(input_signature=(tf.TensorSpec((None,)						,			tf.string						,			name="text"				),)				)
		def 						UpperCAmelCase     (							self						,			__UpperCAmelCase				)->    Dict:







								'''simple docstring'''





								lowerCAmelCase__       					=  self.tokenizer(__a				)
								lowerCAmelCase__       					=  tokenized["input_ids"].to_tensor()

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

								lowerCAmelCase__       					=  self.model(input_ids=__a						,			attention_mask=__a				)["logits"]

								return outputs


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

	def 						UpperCAmelCase     (							self				)->    Any:







							'''simple docstring'''





							super().setUp()

							lowerCAmelCase__       					=  [GPTaTokenizer.from_pretrained(__a				) for checkpoint in (TOKENIZER_CHECKPOINTS)]
							lowerCAmelCase__       					=  [TFGPTaTokenizer.from_pretrained(__a				) for checkpoint in TOKENIZER_CHECKPOINTS]
							assert len(self.tokenizers				) == len(self.tf_tokenizers				)

							lowerCAmelCase__       					=  [
							    "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(zip(self.test_sentences						,			self.test_sentences[::-1]				)				)

	def 						UpperCAmelCase     (							self				)->    Dict:







							'''simple docstring'''





							for tokenizer, tf_tokenizer in zip(self.tokenizers						,			self.tf_tokenizers				):
													for test_inputs in self.test_sentences:
																			lowerCAmelCase__       					=  tokenizer([test_inputs]						,			return_tensors="tf"				)
																			lowerCAmelCase__       					=  tf_tokenizer([test_inputs]				)

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

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

	@slow
	def 						UpperCAmelCase     (							self				)->    List[Any]:







							'''simple docstring'''





							for tf_tokenizer in self.tf_tokenizers:
													lowerCAmelCase__       					=  tf.function(__a				)
													for test_inputs in self.test_sentences:
																			lowerCAmelCase__       					=  tf.constant(__a				)
																			lowerCAmelCase__       					=  compiled_tokenizer(__a				)
																			lowerCAmelCase__       					=  tf_tokenizer(__a				)

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

	@slow
	def 						UpperCAmelCase     (							self				)->    str:







							'''simple docstring'''





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







							'''simple docstring'''





							for tf_tokenizer in self.tf_tokenizers:
													lowerCAmelCase__       					=  tf.convert_to_tensor([self.test_sentences[0]]				)
													lowerCAmelCase__       					=  tf_tokenizer(__a				)  # Build model with some sample inputs

													lowerCAmelCase__       					=  tf_tokenizer.get_config()
													lowerCAmelCase__       					=  TFGPTaTokenizer.from_config(__a				)
													lowerCAmelCase__       					=  model_from_config(__a				)

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


	@slow
	def 						UpperCAmelCase     (							self				)->    int:







							'''simple docstring'''





							for tf_tokenizer in self.tf_tokenizers:
													# for the test to run
													lowerCAmelCase__       					=  123123

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

																			lowerCAmelCase__       					=  out["input_ids"].numpy().shape[1]

																			assert out_length == max_length











import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
