



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 		_lowercase		(       unittest.TestCase	):

			def 		A							(      self			:       Optional[int]							)       ->				Optional[Any]:


				"""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(_a							,   range(len(_a							)							)							)							)
				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(_a							) + "\n"							)
				with open(self.merges_file							,   "w"							,   encoding="utf-8"							) as fp:
					fp.write("\n".join(_a							)							)

				a						=		{
				    "do_resize": True,
				    "size": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
				    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
				}
				a						=		os.path.join(self.tmpdirname							,   _a							)
				with open(self.image_processor_file							,   "w"							,   encoding="utf-8"							) as fp:
					json.dump(_a							,   _a							)

			def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				List[Any]:


				"""simple docstring"""






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

			def 		A							(      self			:       Dict							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				Dict:


				"""simple docstring"""






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

			def 		A							(      self			:       Optional[int]							,   **__lowerCAmelCase			:       int							)       ->				List[Any]:


				"""simple docstring"""






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

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


				"""simple docstring"""






				shutil.rmtree(self.tmpdirname							)

			def 		A							(      self			:       List[str]							)       ->				str:


				"""simple docstring"""






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

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

				return image_inputs

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


				"""simple docstring"""






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

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)
				processor_slow.save_pretrained(self.tmpdirname							)
				a						=		CLIPSegProcessor.from_pretrained(self.tmpdirname							,   use_fast=_a							)

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)
				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							,   _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 		A							(      self			:       Optional[int]							)       ->				Optional[Any]:


				"""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=_a							,   padding_value=1.0							)

				a						=		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 		A							(      self			:       List[str]							)       ->				List[str]:


				"""simple docstring"""






				a						=		self.get_image_processor()
				a						=		self.get_tokenizer()

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)

				a						=		self.prepare_image_inputs()

				a						=		image_processor(_a							,   return_tensors="np"							)
				a						=		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 		A							(      self			:       Optional[Any]							)       ->				Tuple:


				"""simple docstring"""






				a						=		self.get_image_processor()
				a						=		self.get_tokenizer()

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)

				a						=		"lower newer"

				a						=		processor(text=_a							)

				a						=		tokenizer(_a							)

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

			def 		A							(      self			:       Dict							)       ->				Dict:


				"""simple docstring"""






				a						=		self.get_image_processor()
				a						=		self.get_tokenizer()

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)

				a						=		"lower newer"
				a						=		self.prepare_image_inputs()

				a						=		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 		A							(      self			:       List[str]							)       ->				List[Any]:


				"""simple docstring"""






				a						=		self.get_image_processor()
				a						=		self.get_tokenizer()

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)

				a						=		self.prepare_image_inputs()
				a						=		self.prepare_image_inputs()

				a						=		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 		A							(      self			:       Optional[int]							)       ->				Union[str, Any]:


				"""simple docstring"""






				a						=		self.get_image_processor()
				a						=		self.get_tokenizer()

				a						=		CLIPSegProcessor(tokenizer=_a							,   image_processor=_a							)

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

				a						=		processor.batch_decode(_a							)
				a						=		tokenizer.batch_decode(_a							)

				self.assertListEqual(_a							,   _a							)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

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


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




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


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

