



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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    tempfile.mkdtemp()

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

           __a							=    os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES['''vocab_file'''])
           __a							=    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))

           __a							=    {
               '''do_resize''': True,
               '''size''': 20,
               '''do_center_crop''': True,
               '''crop_size''': 18,
               '''do_normalize''': True,
               '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
               '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
           }
           __a							=    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 		_lowerCamelCase      (		self							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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

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

           return image_inputs



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
           processor_slow.save_pretrained(self.tmpdirname)
           __a							=    CLIPSegProcessor.from_pretrained(self.tmpdirname					,		use_fast=__SCREAMING_SNAKE_CASE)

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
           processor_fast.save_pretrained(self.tmpdirname)
           __a							=    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					,		__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 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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

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

           __a							=    CLIPSegProcessor.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 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    self.prepare_image_inputs()

           __a							=    image_processor(__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')
           __a							=    processor(images=__SCREAMING_SNAKE_CASE					,		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							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    '''lower newer'''

           __a							=    processor(text=__SCREAMING_SNAKE_CASE)

           __a							=    tokenizer(__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

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

           __a							=    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 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    self.prepare_image_inputs()
           __a							=    self.prepare_image_inputs()

           __a							=    processor(images=__SCREAMING_SNAKE_CASE					,		visual_prompt=__SCREAMING_SNAKE_CASE)

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

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPSegProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

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

           __a							=    processor.batch_decode(__SCREAMING_SNAKE_CASE)
           __a							=    tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

           self.assertListEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)










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


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[Any]						=					{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''gptsan-japanese'''
       UpperCamelCase__  :   Dict						 =						[
           '''past_key_values''',
       ]
       UpperCamelCase__  :   Dict						 =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=36_000					,		__SCREAMING_SNAKE_CASE							:					Tuple=1_280					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=8_192					,		__SCREAMING_SNAKE_CASE							:					str=4_096					,		__SCREAMING_SNAKE_CASE							:					Any=128					,		__SCREAMING_SNAKE_CASE							:					int=10					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=128					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1E-5					,		__SCREAMING_SNAKE_CASE							:					str=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]="float32"					,		__SCREAMING_SNAKE_CASE							:					List[str]=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					int=0.0_02					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=35_998					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=35_995					,		__SCREAMING_SNAKE_CASE							:					List[str]=35_999					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    max_position_embeddings
           __a							=    d_model
           __a							=    d_ff
           __a							=    d_ext
           __a							=    d_spout
           __a							=    num_switch_layers
           __a							=    num_ext_layers
           __a							=    num_switch_layers + num_ext_layers
           __a							=    num_heads
           __a							=    num_experts
           __a							=    expert_capacity
           __a							=    dropout_rate
           __a							=    layer_norm_epsilon
           __a							=    router_bias
           __a							=    router_jitter_noise
           __a							=    router_dtype
           __a							=    router_ignore_padding_tokens
           __a							=    output_hidden_states
           __a							=    output_attentions
           __a							=    initializer_factor
           __a							=    output_router_logits
           __a							=    use_cache

           super().__init__(
               separator_token_id=__SCREAMING_SNAKE_CASE					,		pad_token_id=__SCREAMING_SNAKE_CASE					,		eos_token_id=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





