
"""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        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






    '''simple docstring'''



    def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[str]:







           """simple docstring"""




           _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(UpperCAmelCase_				,     range(len(UpperCAmelCase_  )  )  )  )
           _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(UpperCAmelCase_  ) + '\n'  )
           with open(self.merges_file				,     'w'				,     encoding='utf-8'  ) as fp:
                  fp.write('\n'.join(UpperCAmelCase_  )  )

           _lowerCAmelCase  	=			{
               'do_resize': True,
               'size': 20,
               'do_center_crop': True,
               'crop_size': 18,
               'do_normalize': True,
               'image_mean': [0.48145466, 0.4578275, 0.40821073],
               'image_std': [0.26862954, 0.26130258, 0.27577711],
           }
           _lowerCAmelCase  	=			os.path.join(self.tmpdirname				,     UpperCAmelCase_  )
           with open(self.image_processor_file				,     'w'				,     encoding='utf-8'  ) as fp:
                  json.dump(UpperCAmelCase_				,     UpperCAmelCase_  )


    def 				__lowerCamelCase	(					self	:			str				,     **UpperCAmelCase_	:			str  )		->  Any:







           """simple docstring"""




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


    def 				__lowerCamelCase	(					self	:			List[str]				,     **UpperCAmelCase_	:			Any  )		->  Union[str, Any]:







           """simple docstring"""




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


    def 				__lowerCamelCase	(					self	:			str				,     **UpperCAmelCase_	:			Dict  )		->  Any:







           """simple docstring"""




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


    def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Tuple:







           """simple docstring"""




           shutil.rmtree(self.tmpdirname  )


    def 				__lowerCamelCase	(					self	:			Dict  )		->  List[str]:







           """simple docstring"""




           _lowerCAmelCase  	=			[np.random.randint(255				,     size=(3, 30, 400)				,     dtype=np.uinta  )]

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

           return image_inputs


    def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Union[str, Any]:







           """simple docstring"""




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

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )
           processor_slow.save_pretrained(self.tmpdirname  )
           _lowerCAmelCase  	=			CLIPProcessor.from_pretrained(self.tmpdirname				,     use_fast=UpperCAmelCase_  )

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )
           processor_fast.save_pretrained(self.tmpdirname  )
           _lowerCAmelCase  	=			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				,     UpperCAmelCase_  )
           self.assertIsInstance(processor_fast.tokenizer				,     UpperCAmelCase_  )

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


    def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







           """simple docstring"""




           _lowerCAmelCase  	=			CLIPProcessor(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=UpperCAmelCase_				,     padding_value=1.0  )

           _lowerCAmelCase  	=			CLIPProcessor.from_pretrained(
               self.tmpdirname				,     bos_token='(BOS)'				,     eos_token='(EOS)'				,     do_normalize=UpperCAmelCase_				,     padding_value=1.0  )

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

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


    def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  str:







           """simple docstring"""




           _lowerCAmelCase  	=			self.get_image_processor()
           _lowerCAmelCase  	=			self.get_tokenizer()

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )

           _lowerCAmelCase  	=			self.prepare_image_inputs()

           _lowerCAmelCase  	=			image_processor(UpperCAmelCase_				,     return_tensors='np'  )
           _lowerCAmelCase  	=			processor(images=UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			List[str]  )		->  str:







           """simple docstring"""




           _lowerCAmelCase  	=			self.get_image_processor()
           _lowerCAmelCase  	=			self.get_tokenizer()

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )

           _lowerCAmelCase  	=			'lower newer'

           _lowerCAmelCase  	=			processor(text=UpperCAmelCase_  )

           _lowerCAmelCase  	=			tokenizer(UpperCAmelCase_  )

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


    def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[str]:







           """simple docstring"""




           _lowerCAmelCase  	=			self.get_image_processor()
           _lowerCAmelCase  	=			self.get_tokenizer()

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )

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

           _lowerCAmelCase  	=			processor(text=UpperCAmelCase_				,     images=UpperCAmelCase_  )

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

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


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







           """simple docstring"""




           _lowerCAmelCase  	=			self.get_image_processor()
           _lowerCAmelCase  	=			self.get_tokenizer()

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )

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

           _lowerCAmelCase  	=			processor.batch_decode(UpperCAmelCase_  )
           _lowerCAmelCase  	=			tokenizer.batch_decode(UpperCAmelCase_  )

           self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )







    def 				__lowerCamelCase	(					self	:			Dict  )		->  Union[str, Any]:







           """simple docstring"""




           _lowerCAmelCase  	=			self.get_image_processor()
           _lowerCAmelCase  	=			self.get_tokenizer()

           _lowerCAmelCase  	=			CLIPProcessor(tokenizer=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )

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

           _lowerCAmelCase  	=			processor(text=UpperCAmelCase_				,     images=UpperCAmelCase_  )

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








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =						{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						['''RoFormerTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RoFormerForCausalLM''',
	    '''RoFormerForMaskedLM''',
	    '''RoFormerForMultipleChoice''',
	    '''RoFormerForQuestionAnswering''',
	    '''RoFormerForSequenceClassification''',
	    '''RoFormerForTokenClassification''',
	    '''RoFormerLayer''',
	    '''RoFormerModel''',
	    '''RoFormerPreTrainedModel''',
	    '''load_tf_weights_in_roformer''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRoFormerForCausalLM''',
	    '''TFRoFormerForMaskedLM''',
	    '''TFRoFormerForMultipleChoice''',
	    '''TFRoFormerForQuestionAnswering''',
	    '''TFRoFormerForSequenceClassification''',
	    '''TFRoFormerForTokenClassification''',
	    '''TFRoFormerLayer''',
	    '''TFRoFormerModel''',
	    '''TFRoFormerPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FlaxRoFormerForMaskedLM''',
	    '''FlaxRoFormerForMultipleChoice''',
	    '''FlaxRoFormerForQuestionAnswering''',
	    '''FlaxRoFormerForSequenceClassification''',
	    '''FlaxRoFormerForTokenClassification''',
	    '''FlaxRoFormerModel''',
	    '''FlaxRoFormerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	_snake_case          =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






