






"""simple docstring"""



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							None
							snake_case_     =							BloomTokenizerFast
							snake_case_     =							BloomTokenizerFast
							snake_case_     =							True
							snake_case_     =							False
							snake_case_     =							"tokenizer_file"
							snake_case_     =							{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													super().setUp()
													A__						:  List[Any]		  =  BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""							)
													tokenizer.save_pretrained(self.tmpdirname							)


							def 						_UpperCamelCase       (			self      :						str			,    **snake_case      :						Optional[int]							):
													'''simple docstring'''





													kwargs.update(self.special_tokens_map							)
													return BloomTokenizerFast.from_pretrained(self.tmpdirname			,    **UpperCamelCase__							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  List[str]		  =  self.get_rust_tokenizer()

													A__						:  Dict		  =  ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
													A__						:  Dict		  =  [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

													A__						:  Optional[int]		  =  tokenizer.batch_encode_plus(UpperCamelCase__							)["""input_ids"""]
													self.assertListEqual(UpperCamelCase__			,    UpperCamelCase__							)

													A__						:  List[str]		  =  tokenizer.batch_decode(UpperCamelCase__							)
													self.assertListEqual(UpperCamelCase__			,    UpperCamelCase__							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Tuple=6							):
													'''simple docstring'''





													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																									A__						:  List[Any]		  =  self.rust_tokenizer_class.from_pretrained(UpperCamelCase__			,    **UpperCamelCase__							)
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									A__						:  Any		  =  """This is a simple input"""
																									A__						:  List[str]		  =  ["""This is a simple input 1""", """This is a simple input 2"""]
																									A__						:  List[Any]		  =  ("""This is a simple input""", """This is a pair""")
																									A__						:  Dict		  =  [
																									    ("""This is a simple input 1""", """This is a simple input 2"""),
																									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																									]

																									# Simple input tests
																									try:
																															tokenizer_r.encode(UpperCamelCase__			,    max_length=UpperCamelCase__							)
																															tokenizer_r.encode_plus(UpperCamelCase__			,    max_length=UpperCamelCase__							)

																															tokenizer_r.batch_encode_plus(UpperCamelCase__			,    max_length=UpperCamelCase__							)
																															tokenizer_r.encode(UpperCamelCase__			,    max_length=UpperCamelCase__							)
																															tokenizer_r.batch_encode_plus(UpperCamelCase__			,    max_length=UpperCamelCase__							)
																									except ValueError:
																															self.fail("""Bloom Tokenizer should be able to deal with padding"""							)

																									A__						:  Optional[int]		  =  None  # Hotfixing padding = None
																									self.assertRaises(UpperCamelCase__			,    tokenizer_r.encode			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""							)

																									# Simple input
																									self.assertRaises(UpperCamelCase__			,    tokenizer_r.encode_plus			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""							)

																									# Simple input
																									self.assertRaises(
																									    UpperCamelCase__			,    tokenizer_r.batch_encode_plus			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""			,    )

																									# Pair input
																									self.assertRaises(UpperCamelCase__			,    tokenizer_r.encode			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""							)

																									# Pair input
																									self.assertRaises(UpperCamelCase__			,    tokenizer_r.encode_plus			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""							)

																									# Pair input
																									self.assertRaises(
																									    UpperCamelCase__			,    tokenizer_r.batch_encode_plus			,    UpperCamelCase__			,    max_length=UpperCamelCase__			,    padding="""max_length"""			,    )


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Any		  =  self.get_rust_tokenizer()
													A__						:  int		  =  load_dataset("""xnli"""			,    """all_languages"""			,    split="""test"""			,    streaming=UpperCamelCase__							)

													A__						:  Any		  =  next(iter(UpperCamelCase__							)							)["""premise"""]  # pick up one data
													A__						:  List[Any]		  =  list(sample_data.values()							)

													A__						:  Optional[int]		  =  list(map(tokenizer.encode			,    UpperCamelCase__							)							)
													A__						:  Union[str, Any]		  =  [tokenizer.decode(UpperCamelCase__			,    clean_up_tokenization_spaces=UpperCamelCase__							) for x in output_tokens]
													self.assertListEqual(UpperCamelCase__			,    UpperCamelCase__							)


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map							)			,    1							)
													self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()							)[0]							)			,    1							)







"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






