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    snake_case       (   unittest.TestCase    ):




  def 						__lowercase(   self  :						Optional[Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tempfile.mkdtemp()

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

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

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		{
           'do_resize': True,
           'size': 20,
           'do_center_crop': True,
           'crop_size': 18,
           'do_normalize': True,
           'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
           'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
       }
       SCREAMING_SNAKE_CASE__					:					Dict      =		os.path.join(self.tmpdirname  ,			a_					)
       with open(self.image_processor_file  ,			'w'  ,			encoding='utf-8'					) as fp:
            json.dump(a_  ,			a_					)




  def 						__lowercase(   self  :						Any  ,			**a_  :						Any					)->							str:




       """simple docstring"""
       return CLIPTokenizer.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Union[str, Any]  ,			**a_  :						Optional[int]					)->							List[str]:




       """simple docstring"""
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,			**a_					)




  def 						__lowercase(   self  :						Any  ,			**a_  :						List[str]					)->							int:




       """simple docstring"""
       return ViTImageProcessor.from_pretrained(self.tmpdirname  ,			**a_					)




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




       """simple docstring"""
       shutil.rmtree(self.tmpdirname					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		[np.random.randint(255  ,			size=(3, 30, 400)  ,			dtype=np.uinta					)]

       SCREAMING_SNAKE_CASE__					:					Dict      =		[Image.fromarray(np.moveaxis(a_  ,			0  ,			-1					)					) for x in image_inputs]

       return image_inputs




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_image_processor()

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)
       processor_slow.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		CLIPSegProcessor.from_pretrained(self.tmpdirname  ,			use_fast=a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)
       processor_fast.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		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  ,			a_					)
       self.assertIsInstance(processor_fast.tokenizer  ,			a_					)

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




  def 						__lowercase(   self  :						Tuple					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,			image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_tokenizer(bos_token='(BOS)'  ,			eos_token='(EOS)'					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_image_processor(do_normalize=a_  ,			padding_value=1.0					)

       SCREAMING_SNAKE_CASE__					:					str      =		CLIPSegProcessor.from_pretrained(
           self.tmpdirname  ,			bos_token='(BOS)'  ,			eos_token='(EOS)'  ,			do_normalize=a_  ,			padding_value=1.0					)

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

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_processor(a_  ,			return_tensors='np'					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		processor(images=a_  ,			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 						__lowercase(   self  :						Tuple					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		'lower newer'

       SCREAMING_SNAKE_CASE__					:					List[str]      =		processor(text=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		tokenizer(a_					)

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




  def 						__lowercase(   self  :						Optional[Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

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

       SCREAMING_SNAKE_CASE__					:					List[str]      =		processor(text=a_  ,			images=a_					)

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

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




  def 						__lowercase(   self  :						Any					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.prepare_image_inputs()
       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__					:					int      =		processor(images=a_  ,			visual_prompt=a_					)

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

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_image_processor()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPSegProcessor(tokenizer=a_  ,			image_processor=a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       SCREAMING_SNAKE_CASE__					:					Dict      =		processor.batch_decode(a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		tokenizer.batch_decode(a_					)

       self.assertListEqual(a_  ,			a_					)











"""simple docstring"""



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


_lowerCAmelCase	:	Optional[int]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


