

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


A											=     get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  XLMRobertaTokenizer
			__A						  =  XLMRobertaTokenizerFast
			__A						  =  True
			__A						  =  True


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().setUp()

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= '<pad>'
				snake_case_      		= 1

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 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(__UpperCamelCase							) ,				10_02							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.assertEqual(self.get_tokenizer().vocab_size ,				10_02							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= XLMRobertaTokenizer(__UpperCamelCase ,				keep_accents=__UpperCamelCase							)

				snake_case_      		= tokenizer.tokenize('This is a test'							)
				self.assertListEqual(__UpperCamelCase ,				['▁This', '▁is', '▁a', '▁t', 'est']							)

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

				snake_case_      		= tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
				self.assertListEqual(
				    __UpperCamelCase ,				[
				        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',
				        '.',
				    ] ,				)
				snake_case_      		= tokenizer.convert_tokens_to_ids(__UpperCamelCase							)
				self.assertListEqual(
				    __UpperCamelCase ,				[
				        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 ^
				    ] ,				)

				snake_case_      		= tokenizer.convert_ids_to_tokens(__UpperCamelCase							)
				self.assertListEqual(
				    __UpperCamelCase ,				[
				        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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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

				snake_case_      		= (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})"""							):
						snake_case_      		= self.rust_tokenizer_class.from_pretrained(__UpperCamelCase ,				**__UpperCamelCase							)
						snake_case_      		= self.tokenizer_class.from_pretrained(__UpperCamelCase ,				**__UpperCamelCase							)

						snake_case_      		= tempfile.mkdtemp()

						snake_case_      		= tokenizer_r.save_pretrained(__UpperCamelCase							)
						snake_case_      		= tokenizer_p.save_pretrained(__UpperCamelCase							)

						# 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							)							)
						snake_case_      		= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f							)
						self.assertSequenceEqual(__UpperCamelCase ,				__UpperCamelCase							)

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

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

						shutil.rmtree(__UpperCamelCase							)

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

						snake_case_      		= tokenizer_r.save_pretrained(__UpperCamelCase ,				legacy_format=__UpperCamelCase							)
						snake_case_      		= tokenizer_p.save_pretrained(__UpperCamelCase							)

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

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

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

						shutil.rmtree(__UpperCamelCase							)

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

						snake_case_      		= tokenizer_r.save_pretrained(__UpperCamelCase ,				legacy_format=__UpperCamelCase							)
						snake_case_      		= tokenizer_p.save_pretrained(__UpperCamelCase							)

						# 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
						snake_case_      		= tokenizer_r.from_pretrained(__UpperCamelCase							)
						snake_case_      		= tokenizer_p.from_pretrained(__UpperCamelCase							)

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

						shutil.rmtree(__UpperCamelCase							)


			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				with tempfile.NamedTemporaryFile() as f:
					shutil.copyfile(__UpperCamelCase ,				f.name							)
					snake_case_      		= XLMRobertaTokenizer(f.name ,				keep_accents=__UpperCamelCase							)
					snake_case_      		= pickle.dumps(__UpperCamelCase							)
				pickle.loads(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if not self.test_rust_tokenizer:
					return

				snake_case_      		= self.get_tokenizer()
				snake_case_      		= self.get_rust_tokenizer()

				snake_case_      		= 'I was born in 92000, and this is falsé.'

				snake_case_      		= tokenizer.tokenize(__UpperCamelCase							)
				snake_case_      		= rust_tokenizer.tokenize(__UpperCamelCase							)
				self.assertListEqual(__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= tokenizer.encode(__UpperCamelCase ,				add_special_tokens=__UpperCamelCase							)
				snake_case_      		= rust_tokenizer.encode(__UpperCamelCase ,				add_special_tokens=__UpperCamelCase							)
				self.assertListEqual(__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= self.get_rust_tokenizer()
				snake_case_      		= tokenizer.encode(__UpperCamelCase							)
				snake_case_      		= rust_tokenizer.encode(__UpperCamelCase							)
				self.assertListEqual(__UpperCamelCase ,				__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'Hello World!'
				snake_case_      		= [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(__UpperCamelCase ,				self.big_tokenizer.encode(__UpperCamelCase							)							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= (
				    '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'
				)
				snake_case_      		= [
				    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(__UpperCamelCase ,				self.big_tokenizer.encode(__UpperCamelCase							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {'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=__UpperCamelCase ,				model_name='xlm-roberta-base' ,				revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' ,				)



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A											=     logging.get_logger(__name__)  # pylint: disable=invalid-name


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= learnable

				if self.learnable:
					assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
					assert length is not None, "learnable=True requires `length` to be set"

					snake_case_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

				self.register_modules(
				    vqvae=__UpperCamelCase ,				transformer=__UpperCamelCase ,				text_encoder=__UpperCamelCase ,				tokenizer=__UpperCamelCase ,				scheduler=__UpperCamelCase ,				learned_classifier_free_sampling_embeddings=__UpperCamelCase ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

				# get prompt text embeddings
				snake_case_      		= self.tokenizer(
				    __UpperCamelCase ,				padding='max_length' ,				max_length=self.tokenizer.model_max_length ,				return_tensors='pt' ,				)
				snake_case_      		= text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_      		= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
					logger.warning(
					    'The following part of your input was truncated because CLIP can only handle sequences up to'
					    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
					snake_case_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				snake_case_      		= self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				snake_case_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

				# duplicate text embeddings for each generation per prompt
				snake_case_      		= prompt_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)

				if do_classifier_free_guidance:
					if self.learned_classifier_free_sampling_embeddings.learnable:
						snake_case_      		= self.learned_classifier_free_sampling_embeddings.embeddings
						snake_case_      		= negative_prompt_embeds.unsqueeze(0							).repeat(__UpperCamelCase ,				1 ,				1							)
					else:
						snake_case_      		= [''] * batch_size

						snake_case_      		= text_input_ids.shape[-1]
						snake_case_      		= self.tokenizer(
						    __UpperCamelCase ,				padding='max_length' ,				max_length=__UpperCamelCase ,				truncation=__UpperCamelCase ,				return_tensors='pt' ,				)
						snake_case_      		= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
						# See comment for normalizing text embeddings
						snake_case_      		= negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					snake_case_      		= negative_prompt_embeds.shape[1]
					snake_case_      		= negative_prompt_embeds.repeat(1 ,				__UpperCamelCase ,				1							)
					snake_case_      		= negative_prompt_embeds.view(batch_size * num_images_per_prompt ,				__UpperCamelCase ,				-1							)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					snake_case_      		= torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= 1
				elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= len(__UpperCamelCase							)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				snake_case_      		= self._encode_prompt(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(__UpperCamelCase ,				__UpperCamelCase							) or callback_steps <= 0)
				):
					raise ValueError(
					    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
					    f""" {type(__UpperCamelCase							)}."""							)

				# get the initial completely masked latents unless the user supplied it

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					snake_case_      		= torch.full(__UpperCamelCase ,				__UpperCamelCase							).to(self.device							)
				else:
					if latents.shape != latents_shape:
						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
					if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
						raise ValueError(
						    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
						    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""							)
					snake_case_      		= latents.to(self.device							)

				# set timesteps
				self.scheduler.set_timesteps(__UpperCamelCase ,				device=self.device							)

				snake_case_      		= self.scheduler.timesteps.to(self.device							)

				snake_case_      		= latents

				for i, t in enumerate(self.progress_bar(__UpperCamelCase							)							):
					# expand the sample if we are doing classifier free guidance
					snake_case_      		= torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

					# predict the un-noised image
					# model_output == `log_p_x_0`
					snake_case_      		= self.transformer(__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				timestep=__UpperCamelCase							).sample

					if do_classifier_free_guidance:
						snake_case_		,       snake_case_      		= model_output.chunk(2							)
						snake_case_      		= model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
						model_output -= torch.logsumexp(__UpperCamelCase ,				dim=1 ,				keepdim=__UpperCamelCase							)

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

					# remove `log(0)`'s (`-inf`s)
					snake_case_      		= model_output.clamp(-70							)

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_      		= self.scheduler.step(__UpperCamelCase ,				timestep=__UpperCamelCase ,				sample=__UpperCamelCase ,				generator=__UpperCamelCase							).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= self.vqvae.config.vq_embed_dim
				snake_case_      		= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				snake_case_      		= self.vqvae.quantize.get_codebook_entry(__UpperCamelCase ,				shape=__UpperCamelCase							)
				snake_case_      		= self.vqvae.decode(__UpperCamelCase ,				force_not_quantize=__UpperCamelCase							).sample

				snake_case_      		= (image / 2 + 0.5).clamp(0 ,				1							)
				snake_case_      		= image.cpu().permute(0 ,				2 ,				3 ,				1							).numpy()

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				snake_case_      		= torch.full_like(keep_mask[:, 0:1, :] ,				__UpperCamelCase							)
				snake_case_      		= torch.cat((all_true, keep_mask) ,				dim=1							)
				snake_case_      		= keep_mask[:, :-1, :]

				snake_case_      		= keep_mask.gather(1 ,				indices.argsort(1							)							)

				snake_case_      		= log_p_x_0.clone()

				snake_case_      		= -torch.inf  # -inf = log(0)

				return rv
