





'''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			):






       """simple docstring"""





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

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





       def snake_case__						(							self       :		int	,			**lowerCAmelCase__       :		Any					)   ->					int:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		str					)   ->					str:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Tuple	,			**lowerCAmelCase__       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




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





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

            '''simple docstring'''




            shutil.rmtree(self.tmpdirname					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[int]:

            '''simple docstring'''




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

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

            return image_inputs





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

            '''simple docstring'''




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

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)
            processor_slow.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CLIPProcessor.from_pretrained(self.tmpdirname	,			use_fast=SCREAMING_SNAKE_CASE_					)

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)
            processor_fast.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	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	,			SCREAMING_SNAKE_CASE_					)
            self.assertIsInstance(processor_fast.tokenizer	,			SCREAMING_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	,			SCREAMING_SNAKE_CASE_					)
            self.assertIsInstance(processor_fast.image_processor	,			SCREAMING_SNAKE_CASE_					)





       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	CLIPProcessor(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=SCREAMING_SNAKE_CASE_	,			padding_value=1.0					)

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

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

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





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)

            _UpperCamelCase       =	self.prepare_image_inputs()

            _UpperCamelCase       =	image_processor(SCREAMING_SNAKE_CASE_	,			return_tensors='''np'''					)
            _UpperCamelCase       =	processor(images=SCREAMING_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 snake_case__						(							self       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)

            _UpperCamelCase       =	'lower newer'

            _UpperCamelCase       =	processor(text=SCREAMING_SNAKE_CASE_					)

            _UpperCamelCase       =	tokenizer(SCREAMING_SNAKE_CASE_					)

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





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)

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

            _UpperCamelCase       =	processor(text=SCREAMING_SNAKE_CASE_	,			images=SCREAMING_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(SCREAMING_SNAKE_CASE_					):
                 processor()





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)

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

            _UpperCamelCase       =	processor.batch_decode(SCREAMING_SNAKE_CASE_					)
            _UpperCamelCase       =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					)

            self.assertListEqual(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_					)







       def snake_case__						(							self       :		List[Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,			image_processor=SCREAMING_SNAKE_CASE_					)

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

            _UpperCamelCase       =	processor(text=SCREAMING_SNAKE_CASE_	,			images=SCREAMING_SNAKE_CASE_					)

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













'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

lowercase__						:      List[Any]	=    {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   'biogpt'





       def __init__(							self       :		Optional[Any]	,			lowerCAmelCase__       :		List[str]=42384	,			lowerCAmelCase__       :		Optional[int]=1024	,			lowerCAmelCase__       :		List[str]=24	,			lowerCAmelCase__       :		List[Any]=16	,			lowerCAmelCase__       :		Optional[int]=4096	,			lowerCAmelCase__       :		Optional[int]="gelu"	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		Optional[int]=0.1	,			lowerCAmelCase__       :		Union[str, Any]=1024	,			lowerCAmelCase__       :		List[str]=0.02	,			lowerCAmelCase__       :		Tuple=1e-1_2	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Union[str, Any]=0.0	,			lowerCAmelCase__       :		Optional[int]=1	,			lowerCAmelCase__       :		Union[str, Any]=0	,			lowerCAmelCase__       :		Optional[Any]=2	,			**lowerCAmelCase__       :		Optional[Any]	,			)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	scale_embedding
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	layerdrop
            _UpperCamelCase       =	activation_dropout
            super().__init__(pad_token_id=lowerCAmelCase__	,			bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			**lowerCAmelCase__					)



