






"""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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							tempfile.mkdtemp()

        # fmt: off
        UpperCAmelCase__						:    Optional[int]							=							["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							=							dict(zip(snake_case__							,			range(len(snake_case__							)							)							)							)
        UpperCAmelCase__						:    Union[str, Any]							=							["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        UpperCAmelCase__						:    Optional[int]							=							{"unk_token": "<unk>"}

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

        UpperCAmelCase__						:    Optional[int]							=							{
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "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],
        }
        UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(self.tmpdirname							,			snake_case__							)
        with open(self.image_processor_file							,			"w"							,			encoding="utf-8"							) as fp:
             json.dump(snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[str]							,			**snake_case__      :	Tuple							):


        '''simple docstring'''
        return CLIPTokenizer.from_pretrained(self.tmpdirname							,			**snake_case__							)
   def       __a							(		self      :	str							,			**snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,			**snake_case__							)
   def       __a							(		self      :	Dict							,			**snake_case__      :	Any							):


        '''simple docstring'''
        return ViTImageProcessor.from_pretrained(self.tmpdirname							,			**snake_case__							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        shutil.rmtree(self.tmpdirname							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							[np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta							)]

        UpperCAmelCase__						:    str							=							[Image.fromarray(np.moveaxis(snake_case__							,			0							,			-1							)							) for x in image_inputs]

        return image_inputs
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.get_tokenizer()
        UpperCAmelCase__						:    List[Any]							=							self.get_rust_tokenizer()
        UpperCAmelCase__						:    str							=							self.get_image_processor()

        UpperCAmelCase__						:    Optional[Any]							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        UpperCAmelCase__						:    Optional[Any]							=							CLIPSegProcessor.from_pretrained(self.tmpdirname							,			use_fast=snake_case__							)

        UpperCAmelCase__						:    Any							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        UpperCAmelCase__						:    List[Any]							=							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							,			snake_case__							)
        self.assertIsInstance(processor_fast.tokenizer							,			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							,			snake_case__							)
        self.assertIsInstance(processor_fast.image_processor							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							):


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

        UpperCAmelCase__						:    str							=							self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"							)
        UpperCAmelCase__						:    int							=							self.get_image_processor(do_normalize=snake_case__							,			padding_value=1.0							)

        UpperCAmelCase__						:    List[str]							=							CLIPSegProcessor.from_pretrained(
            self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=snake_case__							,			padding_value=1.0							)

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

        self.assertEqual(processor.image_processor.to_json_string()							,			image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor							,			snake_case__							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.get_image_processor()
        UpperCAmelCase__						:    Optional[Any]							=							self.get_tokenizer()

        UpperCAmelCase__						:    Tuple							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)

        UpperCAmelCase__						:    Optional[Any]							=							self.prepare_image_inputs()

        UpperCAmelCase__						:    Optional[int]							=							image_processor(snake_case__							,			return_tensors="np"							)
        UpperCAmelCase__						:    Optional[Any]							=							processor(images=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       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.get_image_processor()
        UpperCAmelCase__						:    Optional[int]							=							self.get_tokenizer()

        UpperCAmelCase__						:    Tuple							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"lower newer"

        UpperCAmelCase__						:    Dict							=							processor(text=snake_case__							)

        UpperCAmelCase__						:    Any							=							tokenizer(snake_case__							)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							self.get_image_processor()
        UpperCAmelCase__						:    Any							=							self.get_tokenizer()

        UpperCAmelCase__						:    Tuple							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)

        UpperCAmelCase__						:    int							=							"lower newer"
        UpperCAmelCase__						:    Union[str, Any]							=							self.prepare_image_inputs()

        UpperCAmelCase__						:    List[Any]							=							processor(text=snake_case__							,			images=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(snake_case__							):
             processor()
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							self.get_image_processor()
        UpperCAmelCase__						:    int							=							self.get_tokenizer()

        UpperCAmelCase__						:    Optional[Any]							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)

        UpperCAmelCase__						:    Any							=							self.prepare_image_inputs()
        UpperCAmelCase__						:    int							=							self.prepare_image_inputs()

        UpperCAmelCase__						:    Any							=							processor(images=snake_case__							,			visual_prompt=snake_case__							)

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

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




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.get_image_processor()
        UpperCAmelCase__						:    Tuple							=							self.get_tokenizer()

        UpperCAmelCase__						:    List[str]							=							CLIPSegProcessor(tokenizer=snake_case__							,			image_processor=snake_case__							)

        UpperCAmelCase__						:    Dict							=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__						:    int							=							processor.batch_decode(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							tokenizer.batch_decode(snake_case__							)

        self.assertListEqual(snake_case__							,			snake_case__							)










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


