





'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		A   : int             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












'''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 CLIPImageProcessor, CLIPProcessor







@require_vision
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		tempfile.mkdtemp()

											# fmt: off
											_snake_case:   Union[str, Any]  					=		['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
											_snake_case:   str  					=		dict(zip(__snake_case       ,     range(len(__snake_case       )       )       )       )
											_snake_case:   str  					=		['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
											_snake_case:   Optional[int]  					=		{'unk_token': '<unk>'}

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

											_snake_case:   Dict  					=		{
											    'do_resize': True,
											    'size': 20,
											    'do_center_crop': True,
											    'crop_size': 18,
											    'do_normalize': True,
											    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											_snake_case:   Union[str, Any]  					=		os.path.join(self.tmpdirname       ,     __snake_case       )
											with open(self.image_processor_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	json.dump(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											shutil.rmtree(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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

											_snake_case:   Any  					=		[Image.fromarray(np.moveaxis(__snake_case       ,     0       ,     -1       )       ) for x in image_inputs]

											return image_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_tokenizer()
											_snake_case:   List[str]  					=		self.get_rust_tokenizer()
											_snake_case:   List[str]  					=		self.get_image_processor()

											_snake_case:   Union[str, Any]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(self.tmpdirname       ,     use_fast=__snake_case       )

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											_snake_case:   int  					=		CLIPProcessor.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       ,     __snake_case       )
											self.assertIsInstance(processor_fast.tokenizer       ,     __snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		CLIPProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											_snake_case:   List[Any]  					=		self.get_tokenizer(bos_token='(BOS)'       ,     eos_token='(EOS)'       )
											_snake_case:   List[Any]  					=		self.get_image_processor(do_normalize=__snake_case       ,     padding_value=1.0       )

											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(
											    self.tmpdirname       ,     bos_token='(BOS)'       ,     eos_token='(EOS)'       ,     do_normalize=__snake_case       ,     padding_value=1.0       )

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   List[str]  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   List[str]  					=		self.prepare_image_inputs()

											_snake_case:   List[str]  					=		image_processor(__snake_case       ,     return_tensors='np'       )
											_snake_case:   Dict  					=		processor(images=__snake_case       ,     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    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   List[str]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   int  					=		'lower newer'

											_snake_case:   str  					=		processor(text=__snake_case       )

											_snake_case:   List[Any]  					=		tokenizer(__snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.get_image_processor()
											_snake_case:   List[Any]  					=		self.get_tokenizer()

											_snake_case:   Tuple  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Optional[int]  					=		'lower newer'
											_snake_case:   Union[str, Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_image_processor()
											_snake_case:   Dict  					=		self.get_tokenizer()

											_snake_case:   str  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   str  					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_snake_case:   List[Any]  					=		processor.batch_decode(__snake_case       )
											_snake_case:   Any  					=		tokenizer.batch_decode(__snake_case       )

											self.assertListEqual(__snake_case       ,     __snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Any  					=		'lower newer'
											_snake_case:   Optional[Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

											self.assertListEqual(list(inputs.keys()       )       ,     processor.model_input_names       )





