





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


_UpperCAmelCase       : str           =       ["""gpt2"""]
_UpperCAmelCase       : Optional[int]           =       """gpt2"""

if is_tf_available():

    class      lowerCAmelCase							(      tf.Module    ):
     def __init__(   self						:   Dict    ,  UpperCAmelCase						:   int			)    ->			Union[str, Any]:
         super().__init__()
         lowerCamelCase__					:   Optional[int]      				=	tokenizer
         lowerCamelCase__					:   Tuple      				=	AutoConfig.from_pretrained(UpperCAmelCase			)
         lowerCamelCase__					:   List[str]      				=	TFGPTaLMHeadModel.from_config(UpperCAmelCase			)







     @tf.function(input_signature=(tf.TensorSpec((None,)    ,  tf.string    ,  name='text'			),)			)
     def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   Optional[Any]			)    ->			str:
         lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase			)
         lowerCamelCase__					:   int      				=	tokenized['input_ids'].to_tensor()

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

         lowerCamelCase__					:   List[Any]      				=	self.model(input_ids=UpperCAmelCase    ,  attention_mask=UpperCAmelCase			)['logits']

         return outputs

@require_tf
@require_keras_nlp
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Dict			)    ->			List[Any]:
     super().setUp()

     lowerCamelCase__					:   Tuple      				=	[GPTaTokenizer.from_pretrained(UpperCAmelCase			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
     lowerCamelCase__					:   str      				=	[TFGPTaTokenizer.from_pretrained(UpperCAmelCase			) 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__					:   Tuple      				=	list(zip(self.test_sentences    ,  self.test_sentences[::-1]			)			)
 def   A_			(   self						:   Union[str, Any]			)    ->			int:
     for tokenizer, tf_tokenizer in zip(self.tokenizers    ,  self.tf_tokenizers			):
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   Tuple      				=	tokenizer([test_inputs]    ,  return_tensors='tf'			)
             lowerCamelCase__					:   Optional[int]      				=	tf_tokenizer([test_inputs]			)

             for key in python_outputs.keys():
                 # convert them to numpy to avoid messing with ragged tensors
                 lowerCamelCase__					:   Union[str, Any]      				=	python_outputs[key].numpy()
                 lowerCamelCase__					:   Union[str, 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(UpperCAmelCase    ,  tf.intaa			) == tf_outputs_values			)			)
 @slow
 def   A_			(   self						:   Any			)    ->			Dict:
     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   List[Any]      				=	tf.function(UpperCAmelCase			)
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   Tuple      				=	tf.constant(UpperCAmelCase			)
             lowerCamelCase__					:   Optional[int]      				=	compiled_tokenizer(UpperCAmelCase			)
             lowerCamelCase__					:   List[Any]      				=	tf_tokenizer(UpperCAmelCase			)

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

         lowerCamelCase__					:   Optional[Any]      				=	tf_tokenizer.get_config()
         lowerCamelCase__					:   Any      				=	TFGPTaTokenizer.from_config(UpperCAmelCase			)
         lowerCamelCase__					:   str      				=	model_from_config(UpperCAmelCase			)

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







 @slow
 def   A_			(   self						:   Dict			)    ->			Optional[int]:
     for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         lowerCamelCase__					:   Dict      				=	123123

         for max_length in [3, 5, 1024]:
             lowerCamelCase__					:   List[str]      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
             lowerCamelCase__					:   List[Any]      				=	tf_tokenizer(UpperCAmelCase    ,  max_length=UpperCAmelCase			)

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

             assert out_length == max_length












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""OwlViTImageProcessor"""
 UpperCAmelCase__									=	("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   Any=None    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Dict:
     lowerCamelCase__					:   Any      				=	None
     if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'    ,  UpperCAmelCase    ,  )
         lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('feature_extractor'			)

     lowerCamelCase__					:   Dict      				=	image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.'			)
     if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.'			)

     super().__init__(UpperCAmelCase    ,  UpperCAmelCase			)
 def __call__(   self						:   List[Any]    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Tuple=None    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   Optional[int]="max_length"    ,  UpperCAmelCase						:   Dict="np"    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Union[str, Any]:

     if text is None and query_images is None and images is None:
         raise ValueError(
             'You have to specify at least one text or query image or image. All three cannot be none.'			)

     if text is not None:
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			) or (isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and not isinstance(text[0]    ,  UpperCAmelCase			)):
             lowerCamelCase__					:   Any      				=	[self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)]

         elif isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and isinstance(text[0]    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[str]      				=	[]

             # Maximum number of queries across batch
             lowerCamelCase__					:   List[Any]      				=	max([len(UpperCAmelCase			) for t in text]			)

             # Pad all batch samples to max number of text queries
             for t in text:
                 if len(UpperCAmelCase			) != max_num_queries:
                     lowerCamelCase__					:   int      				=	t + [' '] * (max_num_queries - len(UpperCAmelCase			))

                 lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)
                 encodings.append(UpperCAmelCase			)
         else:
             raise TypeError('Input text should be a string, a list of strings or a nested list of strings'			)

         if return_tensors == "np":
             lowerCamelCase__					:   Optional[Any]      				=	np.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   int      				=	np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "jax" and is_flax_available():
             import jax.numpy as jnp

             lowerCamelCase__					:   Optional[int]      				=	jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Any      				=	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         elif return_tensors == "pt" and is_torch_available():
             import torch

             lowerCamelCase__					:   Union[str, Any]      				=	torch.cat([encoding['input_ids'] for encoding in encodings]    ,  dim=0			)
             lowerCamelCase__					:   Dict      				=	torch.cat([encoding['attention_mask'] for encoding in encodings]    ,  dim=0			)

         elif return_tensors == "tf" and is_tf_available():
             import tensorflow as tf

             lowerCamelCase__					:   Any      				=	tf.stack([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Optional[int]      				=	tf.stack([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

         else:
             raise ValueError('Target return tensor type could not be returned'			)

         lowerCamelCase__					:   str      				=	BatchEncoding()
         lowerCamelCase__					:   int      				=	input_ids
         lowerCamelCase__					:   Any      				=	attention_mask

     if query_images is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	BatchEncoding()
         lowerCamelCase__					:   Optional[Any]      				=	self.image_processor(
             UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			).pixel_values
         lowerCamelCase__					:   List[Any]      				=	query_pixel_values

     if images is not None:
         lowerCamelCase__					:   List[Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if text is not None and images is not None:
         lowerCamelCase__					:   Tuple      				=	image_features.pixel_values
         return encoding
     elif query_images is not None and images is not None:
         lowerCamelCase__					:   Dict      				=	image_features.pixel_values
         return encoding
     elif text is not None or query_images is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCAmelCase			)    ,  tensor_type=UpperCAmelCase			)
 def   A_			(   self						:   str    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[Any]			)    ->			Any:
     return self.image_processor.post_process(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   int    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			str:
     return self.image_processor.post_process_object_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Tuple    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   List[Any]			)    ->			Tuple:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[Any]			)    ->			List[str]:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 @property
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor_class







 @property
 def   A_			(   self						:   int			)    ->			int:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor





