

import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					a		=False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor






class 	A_		(	unittest.TestCase    ):
							def __init__(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=7       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_8       ,SCREAMING_SNAKE_CASE__		:      List[str]=3_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=4_0_0       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      Tuple=True       ,SCREAMING_SNAKE_CASE__		:      int=None       ,):
								__lowerCamelCase    :    Any 	=			size if size is not None else {'height': 2_0, 'width': 2_0}
								__lowerCamelCase    :    str 	=			parent
								__lowerCamelCase    :    List[str] 	=			batch_size
								__lowerCamelCase    :    int 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			image_size
								__lowerCamelCase    :    int 	=			min_resolution
								__lowerCamelCase    :    Union[str, Any] 	=			max_resolution
								__lowerCamelCase    :    List[Any] 	=			size
								__lowerCamelCase    :    str 	=			do_normalize
								__lowerCamelCase    :    Tuple 	=			do_convert_rgb
								__lowerCamelCase    :    List[Any] 	=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
								__lowerCamelCase    :    Tuple 	=			patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
							def    lowerCAmelCase					(		self		:      List[Any]):
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Dict 	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
								__lowerCamelCase    :    Dict 	=			Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,stream=SCREAMING_SNAKE_CASE__).raw).convert('RGB')
								return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,							)
@require_torch
@require_vision
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  str										=				PixaStructImageProcessor if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    List[str] 	=			PixaStructImageProcessingTester(self)
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_convert_rgb'))
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.image_processor_tester.prepare_dummy_image()

								__lowerCamelCase    :    int 	=			self.image_processing_class(**self.image_processor_dict)
								__lowerCamelCase    :    Optional[int] 	=			2_0_4_8

								__lowerCamelCase    :    Any 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,torch.tensor(0.0606)       ,atol=1E-3       ,rtol=1E-3))
							def    lowerCAmelCase					(		self		:      Tuple):
								# Initialize image_processor
								__lowerCamelCase    :    Any 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    int 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Union[str, Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    Optional[int] 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# Initialize image_processor
								__lowerCamelCase    :    str 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    List[str] 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    str 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								__lowerCamelCase    :    str 	=			True

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Optional[Any] 	=			image_processor(
										    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches

									__lowerCamelCase    :    Union[str, Any] 	=			'Hello'

									__lowerCamelCase    :    Optional[Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__       ,header_text=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    int 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__       ,header_text=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# Initialize image_processor
								__lowerCamelCase    :    Tuple 	=			self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,numpify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,np.ndarray)

								__lowerCamelCase    :    Any 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Tuple 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    str 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)


							def    lowerCAmelCase					(		self		:      Dict):
								# Initialize image_processor
								__lowerCamelCase    :    Any 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase    :    int 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor)

								# Test not batched input
								__lowerCamelCase    :    Optional[int] 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Union[str, Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    str 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,							)
@require_torch
@require_vision
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  List[str]										=				PixaStructImageProcessor if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    str 	=			PixaStructImageProcessingTester(self       ,num_channels=4)
								__lowerCamelCase    :    Optional[Any] 	=			3
							@property
							def    lowerCAmelCase					(		self		:      int):
								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    str 	=			self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_convert_rgb'))


							def    lowerCAmelCase					(		self		:      Dict):
								# Initialize image_processor
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    int 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    int 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Tuple 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    Optional[Any] 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)







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 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

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

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) 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    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[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(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

									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
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

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

										assert out_length == max_length




