



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 CLIPSegProcessor, ViTImageProcessor







@require_vision
class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     tempfile.mkdtemp()

						# fmt: off
						_UpperCAmelCase	 =     ['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
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))
						_UpperCAmelCase	 =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
						_UpperCAmelCase	 =     {'unk_token': '<unk>'}

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

						_UpperCAmelCase	 =     {
						    '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],
						}
						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	A)
						with open(self.image_processor_file				,	'w'				,	encoding='utf-8') as fp:
									json.dump(A				,	A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	**A     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						return CLIPTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[Any]				,	**A     :						Union[str, Any])     ->					str:



						"""simple docstring"""






						return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						Any)     ->					Dict:



						"""simple docstring"""






						return ViTImageProcessor.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						Dict)     ->					List[Any]:



						"""simple docstring"""






						shutil.rmtree(self.tmpdirname)



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [np.random.randint(2_55				,	size=(3, 30, 4_00)				,	dtype=np.uinta)]

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

						return image_inputs



			def 					_lowerCamelCase (		self     :						List[str])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizer()
						_UpperCAmelCase	 =     self.get_rust_tokenizer()
						_UpperCAmelCase	 =     self.get_image_processor()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)
						processor_slow.save_pretrained(self.tmpdirname)
						_UpperCAmelCase	 =     CLIPSegProcessor.from_pretrained(self.tmpdirname				,	use_fast=A)

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)
						processor_fast.save_pretrained(self.tmpdirname)
						_UpperCAmelCase	 =     CLIPSegProcessor.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				,	A)
						self.assertIsInstance(processor_fast.tokenizer				,	A)

						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				,	A)
						self.assertIsInstance(processor_fast.image_processor				,	A)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=self.get_tokenizer()				,	image_processor=self.get_image_processor())
						processor.save_pretrained(self.tmpdirname)

						_UpperCAmelCase	 =     self.get_tokenizer(bos_token='(BOS)'				,	eos_token='(EOS)')
						_UpperCAmelCase	 =     self.get_image_processor(do_normalize=A				,	padding_value=1.0)

						_UpperCAmelCase	 =     CLIPSegProcessor.from_pretrained(
						    self.tmpdirname				,	bos_token='(BOS)'				,	eos_token='(EOS)'				,	do_normalize=A				,	padding_value=1.0)

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

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



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_image_processor()
						_UpperCAmelCase	 =     self.get_tokenizer()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)

						_UpperCAmelCase	 =     self.prepare_image_inputs()

						_UpperCAmelCase	 =     image_processor(A				,	return_tensors='np')
						_UpperCAmelCase	 =     processor(images=A				,	return_tensors='np')

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



			def 					_lowerCamelCase (		self     :						List[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_image_processor()
						_UpperCAmelCase	 =     self.get_tokenizer()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)

						_UpperCAmelCase	 =     'lower newer'

						_UpperCAmelCase	 =     processor(text=A)

						_UpperCAmelCase	 =     tokenizer(A)

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



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_image_processor()
						_UpperCAmelCase	 =     self.get_tokenizer()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)

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

						_UpperCAmelCase	 =     processor(text=A				,	images=A)

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

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



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_image_processor()
						_UpperCAmelCase	 =     self.get_tokenizer()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)

						_UpperCAmelCase	 =     self.prepare_image_inputs()
						_UpperCAmelCase	 =     self.prepare_image_inputs()

						_UpperCAmelCase	 =     processor(images=A				,	visual_prompt=A)

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

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



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_image_processor()
						_UpperCAmelCase	 =     self.get_tokenizer()

						_UpperCAmelCase	 =     CLIPSegProcessor(tokenizer=A				,	image_processor=A)

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

						_UpperCAmelCase	 =     processor.batch_decode(A)
						_UpperCAmelCase	 =     tokenizer.batch_decode(A)

						self.assertListEqual(A				,	A)





from typing import TYPE_CHECKING

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


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
