

'''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


__snake_case	: Optional[Any]       				=							["gpt2"]
__snake_case	: Any       				=							"gpt2"

if is_tf_available():
							class        A  (	tf.Module ):





													def __init__(	self	,    snake_case_			)			->							Any:
																	super().__init__()
																	_a        =   tokenizer
																	_a        =   AutoConfig.from_pretrained(lowerCamelCase__			)
																	_a        =   TFGPTaLMHeadModel.from_config(lowerCamelCase__			)





													@tf.function(input_signature=(tf.TensorSpec((None,)	,    tf.string	,    name="text"			),)			)
													def __lowerCAmelCase   (	self	,    snake_case_			)			->							Tuple:
																	_a        =   self.tokenizer(lowerCamelCase__			)
																	_a        =   tokenized["""input_ids"""].to_tensor()

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

																	_a        =   self.model(input_ids=lowerCamelCase__	,    attention_mask=lowerCamelCase__			)["""logits"""]

																	return outputs
@require_tf
@require_keras_nlp
class        A  (	unittest.TestCase ):





						def __lowerCAmelCase   (	self			)			->							int:
										super().setUp()

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

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





						def __lowerCAmelCase   (	self			)			->							List[str]:
										for tokenizer, tf_tokenizer in zip(self.tokenizers	,    self.tf_tokenizers			):
														for test_inputs in self.test_sentences:
																		_a        =   tokenizer([test_inputs]	,    return_tensors="tf"			)
																		_a        =   tf_tokenizer([test_inputs]			)

																		for key in python_outputs.keys():
																						# convert them to numpy to avoid messing with ragged tensors
																						_a        =   python_outputs[key].numpy()
																						_a        =   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 __lowerCAmelCase   (	self			)			->							Tuple:
										for tf_tokenizer in self.tf_tokenizers:
														_a        =   tf.function(lowerCamelCase__			)
														for test_inputs in self.test_sentences:
																		_a        =   tf.constant(lowerCamelCase__			)
																		_a        =   compiled_tokenizer(lowerCamelCase__			)
																		_a        =   tf_tokenizer(lowerCamelCase__			)

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





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

														_a        =   tf_tokenizer.get_config()
														_a        =   TFGPTaTokenizer.from_config(lowerCamelCase__			)
														_a        =   model_from_config(lowerCamelCase__			)

														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
														_a        =   1_2_3_1_2_3

														for max_length in [3, 5, 1_0_2_4]:
																		_a        =   tf.convert_to_tensor([self.test_sentences[0]]			)
																		_a        =   tf_tokenizer(lowerCamelCase__	,    max_length=lowerCamelCase__			)

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

																		assert out_length == max_length









"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)