


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class      lowerCAmelCase__       (				a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= MvpTokenizer
     __SCREAMING_SNAKE_CASE  			= MvpTokenizerFast
     __SCREAMING_SNAKE_CASE  			= True
     __SCREAMING_SNAKE_CASE  			= filter_roberta_detectors


     def  _lowerCamelCase (						self)			->				str:
            super().setUp()
            _A      :       Any				    = [
                "l",
                "o",
                "w",
                "e",
                "r",
                "s",
                "t",
                "i",
                "d",
                "n",
                "\u0120",
                "\u0120l",
                "\u0120n",
                "\u0120lo",
                "\u0120low",
                "er",
                "\u0120lowest",
                "\u0120newer",
                "\u0120wider",
                "<unk>",
            ]
            _A      :       Tuple				    = dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase))))
            _A      :       Any				    = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
            _A      :       Tuple				    = {"unk_token": "<unk>"}

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


     def  _lowerCamelCase (						self ,						**__lowerCamelCase)			->				int:
            kwargs.update(self.special_tokens_map)
            return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						**__lowerCamelCase)			->				Any:
            kwargs.update(self.special_tokens_map)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            return "lower newer", "lower newer"


     @cached_property
     def  _lowerCamelCase (						self)			->				Tuple:
            return MvpTokenizer.from_pretrained("RUCAIBox/mvp")


     @cached_property
     def  _lowerCamelCase (						self)			->				Optional[int]:
            return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")


     @require_torch
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       int				    = ["A long paragraph for summarization.", "Another paragraph for summarization."]
            _A      :       Any				    = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A      :       str				    = tokenizer(__lowerCamelCase ,						max_length=len(__lowerCamelCase) ,						padding=__lowerCamelCase ,						return_tensors="pt")
                   self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase)

                   self.assertEqual((2, 9) ,						batch.input_ids.shape)
                   self.assertEqual((2, 9) ,						batch.attention_mask.shape)
                   _A      :       Optional[int]				    = batch.input_ids.tolist()[0]
                   self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase)
                   # Test that special tokens are reset


     @require_torch
     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Any				    = ["A long paragraph for summarization.", "Another paragraph for summarization."]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A      :       Any				    = tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors="pt")
                   # check if input_ids are returned and no labels
                   self.assertIn("input_ids" ,						__lowerCamelCase)
                   self.assertIn("attention_mask" ,						__lowerCamelCase)
                   self.assertNotIn("labels" ,						__lowerCamelCase)
                   self.assertNotIn("decoder_attention_mask" ,						__lowerCamelCase)


     @require_torch
     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Optional[Any]				    = [
                "Summary of the text.",
                "Another summary.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A      :       Tuple				    = tokenizer(text_target=__lowerCamelCase ,						max_length=3_2 ,						padding="max_length" ,						return_tensors="pt")
                   self.assertEqual(3_2 ,						targets["input_ids"].shape[1])


     @require_torch
     def  _lowerCamelCase (						self)			->				str:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A      :       str				    = tokenizer(
                       ["I am a small frog" * 1_0_2_4, "I am a small frog"] ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors="pt")
                   self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase)
                   self.assertEqual(batch.input_ids.shape ,						(2, 1_0_2_4))


     @require_torch
     def  _lowerCamelCase (						self)			->				Tuple:
            _A      :       Union[str, Any]				    = ["A long paragraph for summarization."]
            _A      :       Dict				    = [
                "Summary of the text.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A      :       Dict				    = tokenizer(__lowerCamelCase ,						text_target=__lowerCamelCase ,						return_tensors="pt")
                   _A      :       Dict				    = inputs["input_ids"]
                   _A      :       List[str]				    = inputs["labels"]
                   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
                   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
                   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
                   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())


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


     def  _lowerCamelCase (						self)			->				List[Any]:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
                          _A      :       Tuple				    = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase ,						**__lowerCamelCase)
                          _A      :       Union[str, Any]				    = self.tokenizer_class.from_pretrained(__lowerCamelCase ,						**__lowerCamelCase)
                          _A      :       Dict				    = "A, <mask> AllenNLP sentence."
                          _A      :       List[str]				    = tokenizer_r.encode_plus(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase)
                          _A      :       List[Any]				    = tokenizer_p.encode_plus(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase)

                          # token_type_ids should put 0 everywhere
                          self.assertEqual(sum(tokens_r["token_type_ids"]) ,						sum(tokens_p["token_type_ids"]))

                          # attention_mask should put 1 everywhere, so sum over length should be 1
                          self.assertEqual(
                              sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]) ,						sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]) ,						)

                          _A      :       Optional[int]				    = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                          _A      :       str				    = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

                          # Rust correctly handles the space before the mask while python doesnt
                          self.assertSequenceEqual(tokens_p["input_ids"] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
                          self.assertSequenceEqual(tokens_r["input_ids"] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

                          self.assertSequenceEqual(
                              __lowerCamelCase ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
                          self.assertSequenceEqual(
                              __lowerCamelCase ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])




import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VQModel
     __SCREAMING_SNAKE_CASE  			= "sample"


     @property
     def  _lowerCamelCase (						self ,						__lowerCamelCase=(3_2, 3_2))			->				Optional[Any]:
            _A      :       Optional[int]				    = 4
            _A      :       Tuple				    = 3

            _A      :       List[Any]				    = floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

            return {"sample": image}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return (3, 3_2, 3_2)


     @property
     def  _lowerCamelCase (						self)			->				List[Any]:
            return (3, 3_2, 3_2)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[Any]				    = {
                "block_out_channels": [3_2, 6_4],
                "in_channels": 3,
                "out_channels": 3,
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                "latent_channels": 3,
            }
            _A      :       int				    = self.dummy_input
            return init_dict, inputs_dict


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


     def  _lowerCamelCase (						self)			->				Any:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy" ,						output_loading_info=__lowerCamelCase)
            self.assertIsNotNone(__lowerCamelCase)
            self.assertEqual(len(loading_info["missing_keys"]) ,						0)

            model.to(__lowerCamelCase)
            _A      :       str				    = model(**self.dummy_input)

            assert image is not None, "Make sure output is not None"


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy")
            model.to(__lowerCamelCase).eval()

            torch.manual_seed(0)
            if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

            _A      :       Tuple				    = torch.randn(1 ,						model.config.in_channels ,						model.config.sample_size ,						model.config.sample_size)
            _A      :       Optional[int]				    = image.to(__lowerCamelCase)
            with torch.no_grad():
                   _A      :       List[str]				    = model(__lowerCamelCase).sample

            _A      :       int				    = output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            _A      :       Optional[Any]				    = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3])
            # fmt: on
            self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3))
