
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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     (   lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Union[str, Any]      =							BartTokenizer
							lowerCamelCase__:				Union[str, Any]      =							BartTokenizerFast
							lowerCamelCase__:				int      =							True
							lowerCamelCase__:				Union[str, Any]      =							filter_roberta_detectors



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



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

								UpperCAmelCase:					Optional[Any] 						=     os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["vocab_file"]							)
								UpperCAmelCase:					Optional[Any] 						=     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							)							)



							def     A__ (     self	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								kwargs.update(self.special_tokens_map							)
								return self.tokenizer_class.from_pretrained(self.tmpdirname	,				**__snake_case							)



							def     A__ (     self	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								kwargs.update(self.special_tokens_map							)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,				**__snake_case							)



							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								return "lower newer", "lower newer"



							@cached_property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return BartTokenizer.from_pretrained("facebook/bart-large"							)



							@cached_property
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								return BartTokenizerFast.from_pretrained("facebook/bart-large"							)



							@require_torch
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
								UpperCAmelCase:					Dict 						=     [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]:
									UpperCAmelCase:					Optional[Any] 						=     tokenizer(__snake_case	,				max_length=len(__snake_case							)	,				padding=__snake_case	,				return_tensors="pt"							)
									self.assertIsInstance(__snake_case	,				__snake_case							)

									self.assertEqual((2, 9)	,				batch.input_ids.shape							)
									self.assertEqual((2, 9)	,				batch.attention_mask.shape							)
									UpperCAmelCase:					List[str] 						=     batch.input_ids.tolist()[0]
									self.assertListEqual(__snake_case	,				__snake_case							)
									# Test that special tokens are reset



							@require_torch
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase:					Optional[int] 						=     tokenizer(__snake_case	,				padding=__snake_case	,				return_tensors="pt"							)
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"	,				__snake_case							)
									self.assertIn("attention_mask"	,				__snake_case							)
									self.assertNotIn("labels"	,				__snake_case							)
									self.assertNotIn("decoder_attention_mask"	,				__snake_case							)



							@require_torch
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     [
								    "Summary of the text.",
								    "Another summary.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase:					Union[str, Any] 						=     tokenizer(text_target=__snake_case	,				max_length=3_2	,				padding="max_length"	,				return_tensors="pt"							)
									self.assertEqual(3_2	,				targets["input_ids"].shape[1]							)



							@require_torch
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase:					List[str] 						=     tokenizer(
									    ["I am a small frog" * 1_0_2_4, "I am a small frog"]	,				padding=__snake_case	,				truncation=__snake_case	,				return_tensors="pt"							)
									self.assertIsInstance(__snake_case	,				__snake_case							)
									self.assertEqual(batch.input_ids.shape	,				(2, 1_0_2_4)							)



							@require_torch
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     ["A long paragraph for summarization."]
								UpperCAmelCase:					Optional[int] 						=     [
								    "Summary of the text.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase:					str 						=     tokenizer(__snake_case	,				return_tensors="pt"							)
									UpperCAmelCase:					Optional[int] 						=     tokenizer(text_target=__snake_case	,				return_tensors="pt"							)
									UpperCAmelCase:					List[Any] 						=     inputs["input_ids"]
									UpperCAmelCase:					Tuple 						=     targets["input_ids"]
									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     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								pass




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






								"""simple docstring"""



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
										UpperCAmelCase:					Union[str, Any] 						=     self.rust_tokenizer_class.from_pretrained(__snake_case	,				**__snake_case							)
										UpperCAmelCase:					int 						=     self.tokenizer_class.from_pretrained(__snake_case	,				**__snake_case							)
										UpperCAmelCase:					List[str] 						=     "A, <mask> AllenNLP sentence."
										UpperCAmelCase:					Optional[Any] 						=     tokenizer_r.encode_plus(__snake_case	,				add_special_tokens=__snake_case	,				return_token_type_ids=__snake_case							)
										UpperCAmelCase:					Optional[int] 						=     tokenizer_p.encode_plus(__snake_case	,				add_special_tokens=__snake_case	,				return_token_type_ids=__snake_case							)

										# 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"]							)	,				)

										UpperCAmelCase:					Optional[Any] 						=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
										UpperCAmelCase:					int 						=     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(
										    __snake_case	,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)
										self.assertSequenceEqual(
										    __snake_case	,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								pass



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_output()



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






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "time_embedding_type": "positional",
								    "use_timestep_embedding": True,
								    "flip_sin_to_cos": False,
								    "freq_shift": 1.0,
								    "out_block_type": "OutConv1DBlock",
								    "mid_block_type": "MidResTemporalBlock1D",
								    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
								    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="unet"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(loading_info["missing_keys"]							)	,				0							)

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     model(**self.dummy_input							)

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



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,				subfolder="unet"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1)



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

								self.assertIsNotNone(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     torch.Size((inputs_dict["sample"].shape[0], 1)							)
								self.assertEqual(output.shape	,				__snake_case	,				"Input and output shapes do not match"							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "layers_per_block": 1,
								    "downsample_each_block": True,
								    "use_timestep_embedding": True,
								    "freq_shift": 1.0,
								    "flip_sin_to_cos": False,
								    "time_embedding_type": "positional",
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(vf_loading_info["missing_keys"]							)	,				0							)

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     value_function(**self.dummy_input							)

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



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




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






								"""simple docstring"""



								pass
