




'''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 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   tempfile.mkdtemp()

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

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

							__snake_case						 =   {
							    'do_resize': True,
							    'size': 2_0,
							    'do_center_crop': True,
							    'crop_size': 1_8,
							    'do_normalize': True,
							    'image_mean': [0.48145466, 0.4578275, 0.40821073],
							    'image_std': [0.26862954, 0.26130258, 0.27577711],
							}
							__snake_case						 =   os.path.join(self.tmpdirname       ,		__lowerCAmelCase      )
							with open(self.image_processor_file       ,		'w'       ,		encoding='utf-8'      ) as fp:
													json.dump(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict       ,		**__lowerCAmelCase     :  Union[str, Any]      ):
							return CLIPTokenizer.from_pretrained(self.tmpdirname       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any       ,		**__lowerCAmelCase     :  Union[str, Any]      ):
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  Any      ):
							return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   [np.random.randint(2_5_5       ,		size=(3, 3_0, 4_0_0)       ,		dtype=np.uinta      )]

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

							return image_inputs




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.get_tokenizer()
							__snake_case						 =   self.get_rust_tokenizer()
							__snake_case						 =   self.get_image_processor()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )
							processor_slow.save_pretrained(self.tmpdirname      )
							__snake_case						 =   CLIPProcessor.from_pretrained(self.tmpdirname       ,		use_fast=__lowerCAmelCase      )

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )
							processor_fast.save_pretrained(self.tmpdirname      )
							__snake_case						 =   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       ,		__lowerCAmelCase      )
							self.assertIsInstance(processor_fast.tokenizer       ,		__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   CLIPProcessor(tokenizer=self.get_tokenizer()       ,		image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

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

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

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

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




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.get_image_processor()
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )

							__snake_case						 =   self.prepare_image_inputs()

							__snake_case						 =   image_processor(__lowerCAmelCase       ,		return_tensors='np'      )
							__snake_case						 =   processor(images=__lowerCAmelCase       ,		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   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.get_image_processor()
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )

							__snake_case						 =   'lower newer'

							__snake_case						 =   processor(text=__lowerCAmelCase      )

							__snake_case						 =   tokenizer(__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.get_image_processor()
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )

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

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

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

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




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.get_image_processor()
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )

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

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

							self.assertListEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.get_image_processor()
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   CLIPProcessor(tokenizer=__lowerCAmelCase       ,		image_processor=__lowerCAmelCase      )

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

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

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







'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

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

