




'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      List[str]						       =    LongformerTokenizer
			lowerCAmelCase_     :      Optional[Any]						       =    True
			lowerCAmelCase_     :      List[str]						       =    LongformerTokenizerFast
			lowerCAmelCase_     :      Any						       =    True




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

										'''simple docstring'''




										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										snake_case_						=      [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										snake_case_						=      dict(zip(a__				,							range(len(a__							)							)							)							)
										snake_case_						=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										snake_case_						=      {"unk_token": "<unk>"}

										snake_case_						=      os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES["vocab_file"]							)
										snake_case_						=      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__							)							)




			def    lowerCAmelCase__				(					self				,							**a__							)				->				List[Any]:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							**a__							)				->				Union[str, Any]:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      "lower newer"
										snake_case_						=      "lower newer"
										return input_text, output_text




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.tokenizer_class(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map							)
										snake_case_						=      "lower newer"
										snake_case_						=      ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										snake_case_						=      tokenizer.tokenize(a__							)  # , add_prefix_space=True)
										self.assertListEqual(a__				,							a__							)

										snake_case_						=      tokens + [tokenizer.unk_token]
										snake_case_						=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a__							)				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("Hello world!"				,							add_special_tokens=a__							)				,							[0, 31_414, 232, 328, 2]							)
										self.assertListEqual(
										    tokenizer.encode("Hello world! cécé herlolip 418"				,							add_special_tokens=a__							)				,							[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]				,							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"							)

										snake_case_						=      tokenizer.encode("sequence builders"				,							add_special_tokens=a__							)
										snake_case_						=      tokenizer.encode("multi-sequence build"				,							add_special_tokens=a__							)

										snake_case_						=      tokenizer.encode(
										    "sequence builders"				,							add_special_tokens=a__				,							add_prefix_space=a__							)
										snake_case_						=      tokenizer.encode(
										    "sequence builders"				,							"multi-sequence build"				,							add_special_tokens=a__				,							add_prefix_space=a__							)

										snake_case_						=      tokenizer.build_inputs_with_special_tokens(a__							)
										snake_case_						=      tokenizer.build_inputs_with_special_tokens(a__				,							a__							)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode




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

										'''simple docstring'''




										snake_case_						=      self.get_tokenizer()

										snake_case_						=      "Encode this sequence."
										snake_case_						=      tokenizer.byte_encoder[" ".encode("utf-8"							)[0]]

										# Testing encoder arguments
										snake_case_						=      tokenizer.encode(a__				,							add_special_tokens=a__				,							add_prefix_space=a__							)
										snake_case_						=      tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
										self.assertNotEqual(a__				,							a__							)

										snake_case_						=      tokenizer.encode(a__				,							add_special_tokens=a__				,							add_prefix_space=a__							)
										snake_case_						=      tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
										self.assertEqual(a__				,							a__							)

										tokenizer.add_special_tokens({"bos_token": "<s>"}							)
										snake_case_						=      tokenizer.encode(a__				,							add_special_tokens=a__							)
										snake_case_						=      tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
										self.assertNotEqual(a__				,							a__							)

										# Testing spaces after special tokens
										snake_case_						=      "<mask>"
										tokenizer.add_special_tokens(
										    {"mask_token": AddedToken(a__				,							lstrip=a__				,							rstrip=a__							)}							)  # mask token has a left space
										snake_case_						=      tokenizer.convert_tokens_to_ids(a__							)

										snake_case_						=      "Encode <mask> sequence"
										snake_case_						=      "Encode <mask>sequence"

										snake_case_						=      tokenizer.encode(a__							)
										snake_case_						=      encoded.index(a__							)
										snake_case_						=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
										self.assertEqual(a__				,							a__							)

										snake_case_						=      tokenizer.encode(a__							)
										snake_case_						=      encoded.index(a__							)
										snake_case_						=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
										self.assertNotEqual(a__				,							a__							)




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

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(a__				,							**a__							)
																								snake_case_						=      self.tokenizer_class.from_pretrained(a__				,							**a__							)
																								snake_case_						=      "A, <mask> AllenNLP sentence."
																								snake_case_						=      tokenizer_r.encode_plus(a__				,							add_special_tokens=a__				,							return_token_type_ids=a__							)
																								snake_case_						=      tokenizer_p.encode_plus(a__				,							add_special_tokens=a__				,							return_token_type_ids=a__							)

																								# 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"]							)				,							)

																								snake_case_						=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
																								snake_case_						=      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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
																								self.assertSequenceEqual(tokens_r["input_ids"]				,							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)

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




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										for trim_offsets, add_prefix_space in itertools.product([True, False]				,							repeat=2							):
																	snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																	    self.tmpdirname				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)

																	snake_case_						=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
																	snake_case_						=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

																	self.assertEqual(pre_tokenizer_state["add_prefix_space"]				,							a__							)

																	self.assertEqual(post_processor_state["add_prefix_space"]				,							a__							)
																	self.assertEqual(post_processor_state["trim_offsets"]				,							a__							)




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

										'''simple docstring'''




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																								snake_case_						=      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																								snake_case_						=      F'{text_of_1_token} {text_of_1_token}'

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(len(a__							) + 1, len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(len(a__							) + 1, len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(len(a__							), len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(len(a__							), len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      F' {text}'

																								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																								# )
																								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																								# self.assertEqual(
																								#     encoding.offset_mapping[1],
																								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																								# )

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(1 + len(a__							) + 1, 1 + len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(1 + len(a__							), 1 + len(a__							) + 1 + len(a__							))				,							)

																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(
																								    a__				,							use_fast=a__				,							add_prefix_space=a__				,							trim_offsets=a__							)
																								snake_case_						=      tokenizer_r(a__				,							return_offsets_mapping=a__				,							add_special_tokens=a__							)
																								self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(a__							))							)
																								self.assertEqual(
																								    encoding.offset_mapping[1]				,							(1 + len(a__							), 1 + len(a__							) + 1 + len(a__							))				,							)





'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  UNetaDModel(
					    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,    )
					return model







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2




