
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							:Dict										=    get_tests_dir('fixtures/test_sentencepiece.model')





@require_sentencepiece
@require_tokenizers
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					XLMRobertaTokenizer
	snake_case_    =					XLMRobertaTokenizerFast
	snake_case_    =					True
	snake_case_    =					True






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					super().setUp()

					# We have a SentencePiece fixture for testing
					__A             =			XLMRobertaTokenizer(A							,keep_accents=A			)
					tokenizer.save_pretrained(self.tmpdirname			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			"<pad>"
					__A             =			1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(A			)							,A			)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(A			)							,A			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			list(self.get_tokenizer().get_vocab().keys()			)

					self.assertEqual(vocab_keys[0]							,"<s>"			)
					self.assertEqual(vocab_keys[1]							,"<pad>"			)
					self.assertEqual(vocab_keys[-1]							,"<mask>"			)
					self.assertEqual(len(A			)							,10_02			)






	def     UpperCamelCase_  (	self :			int			):
					self.assertEqual(self.get_tokenizer().vocab_size							,10_02			)






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			XLMRobertaTokenizer(A							,keep_accents=A			)

					__A             =			tokenizer.tokenize("This is a test"			)
					self.assertListEqual(A							,["▁This", "▁is", "▁a", "▁t", "est"]			)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(A			)							,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]							,)

					__A             =			tokenizer.tokenize("I was born in 92000, and this is falsé."			)
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "9",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "é",
					        ".",
					    ]							,)
					__A             =			tokenizer.convert_tokens_to_ids(A			)
					self.assertListEqual(
					    A							,[
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]							,)

					__A             =			tokenizer.convert_ids_to_tokens(A			)
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "<unk>",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "<unk>",
					        ".",
					    ]							,)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

					__A             =			(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
													__A             =			self.rust_tokenizer_class.from_pretrained(A							,**A			)
													__A             =			self.tokenizer_class.from_pretrained(A							,**A			)

													__A             =			tempfile.mkdtemp()

													__A             =			tokenizer_r.save_pretrained(A			)
													__A             =			tokenizer_p.save_pretrained(A			)

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)
													__A             =			tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f			)
													self.assertSequenceEqual(A							,A			)

													# Checks everything loads correctly in the same way
													__A             =			tokenizer_r.from_pretrained(A			)
													__A             =			tokenizer_p.from_pretrained(A			)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A							,A			)			)
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(A			)

													# Save tokenizer rust, legacy_format=True
													__A             =			tempfile.mkdtemp()

													__A             =			tokenizer_r.save_pretrained(A							,legacy_format=A			)
													__A             =			tokenizer_p.save_pretrained(A			)

													# Checks it save with the same files
													self.assertSequenceEqual(A							,A			)

													# Checks everything loads correctly in the same way
													__A             =			tokenizer_r.from_pretrained(A			)
													__A             =			tokenizer_p.from_pretrained(A			)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A							,A			)			)

													shutil.rmtree(A			)

													# Save tokenizer rust, legacy_format=False
													__A             =			tempfile.mkdtemp()

													__A             =			tokenizer_r.save_pretrained(A							,legacy_format=A			)
													__A             =			tokenizer_p.save_pretrained(A			)

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)

													# Checks everything loads correctly in the same way
													__A             =			tokenizer_r.from_pretrained(A			)
													__A             =			tokenizer_p.from_pretrained(A			)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A							,A			)			)

													shutil.rmtree(A			)






	@cached_property
	def     UpperCamelCase_  (	self :			List[str]			):
					return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"			)






	def     UpperCamelCase_  (	self :			int			):
					with tempfile.NamedTemporaryFile() as f:
									shutil.copyfile(A							,f.name			)
									__A             =			XLMRobertaTokenizer(f.name							,keep_accents=A			)
									__A             =			pickle.dumps(A			)
					pickle.loads(A			)






	def     UpperCamelCase_  (	self :			List[Any]			):
					if not self.test_rust_tokenizer:
									return

					__A             =			self.get_tokenizer()
					__A             =			self.get_rust_tokenizer()

					__A             =			"I was born in 92000, and this is falsé."

					__A             =			tokenizer.tokenize(A			)
					__A             =			rust_tokenizer.tokenize(A			)
					self.assertListEqual(A							,A			)

					__A             =			tokenizer.encode(A							,add_special_tokens=A			)
					__A             =			rust_tokenizer.encode(A							,add_special_tokens=A			)
					self.assertListEqual(A							,A			)

					__A             =			self.get_rust_tokenizer()
					__A             =			tokenizer.encode(A			)
					__A             =			rust_tokenizer.encode(A			)
					self.assertListEqual(A							,A			)






	@slow
	def     UpperCamelCase_  (	self :			Any			):
					__A             =			"Hello World!"
					__A             =			[0, 3_53_78, 66_61, 38, 2]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(A							,self.big_tokenizer.encode(A			)			)






	@slow
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			(
					    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
					    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
					)
					__A             =			[
					    0,
					    32_93,
					    83,
					    10,
					    45_52,
					    49_89,
					    79_86,
					    6_78,
					    10,
					    59_15,
					    1_11,
					    17_94_59,
					    12_48_50,
					    4,
					    60_44,
					    2_37,
					    12,
					    6,
					    5,
					    6,
					    4,
					    67_80,
					    7_05,
					    15,
					    13_88,
					    44,
					    3_78,
					    1_01_14,
					    7_11,
					    1_52,
					    20,
					    6,
					    5,
					    2_23_76,
					    6_42,
					    12_21,
					    1_51_90,
					    3_41_53,
					    4_50,
					    56_08,
					    9_59,
					    11_19,
					    5_77_02,
					    1_36,
					    1_86,
					    47,
					    10_98,
					    2_93_67,
					    47,
					    # 4426, # What fairseq tokenizes from "<unk>": "_<"
					    # 3678, # What fairseq tokenizes from "<unk>": "unk"
					    # 2740, # What fairseq tokenizes from "<unk>": ">"
					    3,  # What we tokenize from "<unk>": "<unk>"
					    6,  # Residue from the tokenization: an extra sentencepiece underline
					    4,
					    60_44,
					    2_37,
					    62_84,
					    5_09_01,
					    5_28,
					    31,
					    90,
					    34,
					    9_27,
					    2,
					]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(A							,self.big_tokenizer.encode(A			)			)







	@slow
	def     UpperCamelCase_  (	self :			Tuple			):
					# fmt: off
					__A             =			{"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=A							,model_name="xlm-roberta-base"							,revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"							,)


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
