


'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor, OwlViTProcessor


@require_vision
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				tempfile.mkdtemp()

										# fmt: off
										__lowerCAmelCase							       =				['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
										# fmt: on
										__lowerCAmelCase							       =				dict(zip(__lowercase       ,			range(len(__lowercase		)		)		)		)
										__lowerCAmelCase							       =				['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
										__lowerCAmelCase							       =				{'''unk_token''': '''<unk>'''}

										__lowerCAmelCase							       =				os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase							       =				os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['''merges_file''']		)
										with open(self.vocab_file       ,			'''w'''       ,			encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)
										with open(self.merges_file       ,			'''w'''       ,			encoding='''utf-8'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)

										__lowerCAmelCase							       =				{
										    '''do_resize''': True,
										    '''size''': 20,
										    '''do_center_crop''': True,
										    '''crop_size''': 18,
										    '''do_normalize''': True,
										    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										}
										__lowerCAmelCase							       =				os.path.join(self.tmpdirname       ,			__lowercase		)
										with open(self.image_processor_file       ,			'''w'''       ,			encoding='''utf-8'''		) as fp:
																json.dump(__lowercase       ,			__lowercase		)




				def 					_snake_case  (self       ,			**__lowercase		):
										return CLIPTokenizer.from_pretrained(self.tmpdirname       ,			pad_token='''!'''       ,			**__lowercase		)




				def 					_snake_case  (self       ,			**__lowercase		):
										return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,			pad_token='''!'''       ,			**__lowercase		)




				def 					_snake_case  (self       ,			**__lowercase		):
										return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,			**__lowercase		)




				def 					_snake_case  (self		):
										shutil.rmtree(self.tmpdirname		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				[np.random.randint(2_55       ,			size=(3, 30, 4_00)       ,			dtype=np.uinta		)]

										__lowerCAmelCase							       =				[Image.fromarray(np.moveaxis(__lowercase       ,			0       ,			-1		)		) for x in image_inputs]

										return image_inputs




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_tokenizer()
										__lowerCAmelCase							       =				self.get_rust_tokenizer()
										__lowerCAmelCase							       =				self.get_image_processor()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)
										processor_slow.save_pretrained(self.tmpdirname		)
										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(self.tmpdirname       ,			use_fast=__lowercase		)

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)
										processor_fast.save_pretrained(self.tmpdirname		)
										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(self.tmpdirname		)

										self.assertEqual(processor_slow.tokenizer.get_vocab()       ,			tokenizer_slow.get_vocab()		)
										self.assertEqual(processor_fast.tokenizer.get_vocab()       ,			tokenizer_fast.get_vocab()		)
										self.assertEqual(tokenizer_slow.get_vocab()       ,			tokenizer_fast.get_vocab()		)
										self.assertIsInstance(processor_slow.tokenizer       ,			__lowercase		)
										self.assertIsInstance(processor_fast.tokenizer       ,			__lowercase		)

										self.assertEqual(processor_slow.image_processor.to_json_string()       ,			image_processor.to_json_string()		)
										self.assertEqual(processor_fast.image_processor.to_json_string()       ,			image_processor.to_json_string()		)
										self.assertIsInstance(processor_slow.image_processor       ,			__lowercase		)
										self.assertIsInstance(processor_fast.image_processor       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=self.get_tokenizer()       ,			image_processor=self.get_image_processor()		)
										processor.save_pretrained(self.tmpdirname		)

										__lowerCAmelCase							       =				self.get_tokenizer(bos_token='''(BOS)'''       ,			eos_token='''(EOS)'''		)
										__lowerCAmelCase							       =				self.get_image_processor(do_normalize=__lowercase		)

										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(
										    self.tmpdirname       ,			bos_token='''(BOS)'''       ,			eos_token='''(EOS)'''       ,			do_normalize=__lowercase		)

										self.assertEqual(processor.tokenizer.get_vocab()       ,			tokenizer_add_kwargs.get_vocab()		)
										self.assertIsInstance(processor.tokenizer       ,			__lowercase		)

										self.assertEqual(processor.image_processor.to_json_string()       ,			image_processor_add_kwargs.to_json_string()		)
										self.assertIsInstance(processor.image_processor       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_image_processor()
										__lowerCAmelCase							       =				self.get_tokenizer()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)

										__lowerCAmelCase							       =				self.prepare_image_inputs()

										__lowerCAmelCase							       =				image_processor(__lowercase       ,			return_tensors='''np'''		)
										__lowerCAmelCase							       =				processor(images=__lowercase       ,			return_tensors='''np'''		)

										for key in input_image_proc.keys():
																self.assertAlmostEqual(input_image_proc[key].sum()       ,			input_processor[key].sum()       ,			delta=1e-2		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_image_processor()
										__lowerCAmelCase							       =				self.get_tokenizer()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)

										__lowerCAmelCase							       =				'''lower newer'''

										__lowerCAmelCase							       =				processor(text=__lowercase       ,			return_tensors='''np'''		)

										__lowerCAmelCase							       =				tokenizer(__lowercase       ,			return_tensors='''np'''		)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key][0].tolist()       ,			encoded_processor[key][0].tolist()		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_image_processor()
										__lowerCAmelCase							       =				self.get_tokenizer()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)

										__lowerCAmelCase							       =				'''lower newer'''
										__lowerCAmelCase							       =				self.prepare_image_inputs()

										__lowerCAmelCase							       =				processor(text=__lowercase       ,			images=__lowercase		)

										self.assertListEqual(list(inputs.keys()		)       ,			['''input_ids''', '''attention_mask''', '''pixel_values''']		)

										# test if it raises when no input is passed
										with pytest.raises(__lowercase		):
																processor()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''google/owlvit-base-patch32'''
										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(__lowercase		)

										__lowerCAmelCase							       =				['''cat''', '''nasa badge''']
										__lowerCAmelCase							       =				processor(text=__lowercase		)

										__lowerCAmelCase							       =				16
										self.assertListEqual(list(inputs.keys()		)       ,			['''input_ids''', '''attention_mask''']		)
										self.assertEqual(inputs['''input_ids'''].shape       ,			(2, seq_length)		)

										# test if it raises when no input is passed
										with pytest.raises(__lowercase		):
																processor()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''google/owlvit-base-patch32'''
										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(__lowercase		)

										__lowerCAmelCase							       =				[['''cat''', '''nasa badge'''], ['''person''']]
										__lowerCAmelCase							       =				processor(text=__lowercase		)

										__lowerCAmelCase							       =				16
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				max([len(__lowercase		) for texts in input_texts]		)

										self.assertListEqual(list(inputs.keys()		)       ,			['''input_ids''', '''attention_mask''']		)
										self.assertEqual(inputs['''input_ids'''].shape       ,			(batch_size * num_max_text_queries, seq_length)		)

										# test if it raises when no input is passed
										with pytest.raises(__lowercase		):
																processor()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''google/owlvit-base-patch32'''
										__lowerCAmelCase							       =				OwlViTProcessor.from_pretrained(__lowercase		)

										__lowerCAmelCase							       =				['''cat''', '''nasa badge''']
										__lowerCAmelCase							       =				processor(text=__lowercase		)

										__lowerCAmelCase							       =				16
										__lowerCAmelCase							       =				inputs['''input_ids''']
										__lowerCAmelCase							       =				[
										    [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
										    [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
										]

										self.assertListEqual(list(inputs.keys()		)       ,			['''input_ids''', '''attention_mask''']		)
										self.assertEqual(inputs['''input_ids'''].shape       ,			(2, seq_length)		)
										self.assertListEqual(list(input_ids[0]		)       ,			predicted_ids[0]		)
										self.assertListEqual(list(input_ids[1]		)       ,			predicted_ids[1]		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_image_processor()
										__lowerCAmelCase							       =				self.get_tokenizer()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)

										__lowerCAmelCase							       =				self.prepare_image_inputs()
										__lowerCAmelCase							       =				self.prepare_image_inputs()

										__lowerCAmelCase							       =				processor(images=__lowercase       ,			query_images=__lowercase		)

										self.assertListEqual(list(inputs.keys()		)       ,			['''query_pixel_values''', '''pixel_values''']		)

										# test if it raises when no input is passed
										with pytest.raises(__lowercase		):
																processor()




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.get_image_processor()
										__lowerCAmelCase							       =				self.get_tokenizer()

										__lowerCAmelCase							       =				OwlViTProcessor(tokenizer=__lowercase       ,			image_processor=__lowercase		)

										__lowerCAmelCase							       =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__lowerCAmelCase							       =				processor.batch_decode(__lowercase		)
										__lowerCAmelCase							       =				tokenizer.batch_decode(__lowercase		)

										self.assertListEqual(__lowercase       ,			__lowercase		)





'''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


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

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__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=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


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







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) 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 					_snake_case  (self		):
										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(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

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




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

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

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




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length

