






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


__magic_name__:					int							=     ["gpt2"]
__magic_name__:					int							=     "gpt2"

if is_tf_available():





  class 	snake_case__ (							tf.Module				):
   def __init__(    self      ,       lowerCAmelCase__			)							->				str:
         super().__init__()
         __magic_name__      :       Optional[Any]       						= tokenizer
         __magic_name__      :       List[Any]       						= AutoConfig.from_pretrained(_UpperCAmelCase			)
         __magic_name__      :       List[str]       						= TFGPTaLMHeadModel.from_config(_UpperCAmelCase			)







   @tf.function(input_signature=(tf.TensorSpec((None,)      ,       tf.string      ,       name="""text"""			),)			)
   def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict:
         __magic_name__      :       Optional[int]       						= self.tokenizer(_UpperCAmelCase			)
         __magic_name__      :       List[Any]       						= tokenized['input_ids'].to_tensor()

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

         __magic_name__      :       Optional[int]       						= self.model(input_ids=_UpperCAmelCase      ,       attention_mask=_UpperCAmelCase			)['logits']

         return outputs





@require_tf
@require_keras_nlp
class 	snake_case__ (							unittest.TestCase				):
 def       __magic_name__    (    self			)							->				Tuple:
       super().setUp()

       __magic_name__      :       str       						= [GPTaTokenizer.from_pretrained(_UpperCAmelCase			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       __magic_name__      :       int       						= [TFGPTaTokenizer.from_pretrained(_UpperCAmelCase			) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers			) == len(self.tf_tokenizers			)

       __magic_name__      :       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',
       ]
       __magic_name__      :       Tuple       						= list(zip(self.test_sentences      ,       self.test_sentences[::-1]			)			)
 def       __magic_name__    (    self			)							->				Optional[Any]:
       for tokenizer, tf_tokenizer in zip(self.tokenizers      ,       self.tf_tokenizers			):
             for test_inputs in self.test_sentences:
                   __magic_name__      :       Union[str, Any]       						= tokenizer([test_inputs]      ,       return_tensors="""tf"""			)
                   __magic_name__      :       Any       						= tf_tokenizer([test_inputs]			)

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

                         self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape			)			)
                         self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase      ,       tf.intaa			) == tf_outputs_values			)			)
 @slow
 def       __magic_name__    (    self			)							->				List[Any]:
       for tf_tokenizer in self.tf_tokenizers:
             __magic_name__      :       str       						= tf.function(_UpperCAmelCase			)
             for test_inputs in self.test_sentences:
                   __magic_name__      :       List[str]       						= tf.constant(_UpperCAmelCase			)
                   __magic_name__      :       Optional[int]       						= compiled_tokenizer(_UpperCAmelCase			)
                   __magic_name__      :       Tuple       						= tf_tokenizer(_UpperCAmelCase			)

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

             __magic_name__      :       Tuple       						= tf_tokenizer.get_config()
             __magic_name__      :       List[str]       						= TFGPTaTokenizer.from_config(_UpperCAmelCase			)
             __magic_name__      :       int       						= model_from_config(_UpperCAmelCase			)

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







 @slow
 def       __magic_name__    (    self			)							->				List[Any]:
       for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             __magic_name__      :       List[str]       						= 12_31_23

             for max_length in [3, 5, 10_24]:
                   __magic_name__      :       List[Any]       						= tf.convert_to_tensor([self.test_sentences[0]]			)
                   __magic_name__      :       List[Any]       						= tf_tokenizer(_UpperCAmelCase      ,       max_length=_UpperCAmelCase			)

                   __magic_name__      :       Tuple       						= out['input_ids'].numpy().shape[1]

                   assert out_length == max_length








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
