






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__UpperCAmelCase     							=						'platform'

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]=None					,	__snake_case   :							List[Any]=None					,	__snake_case   :							Optional[int]=None					,	__snake_case   :							List[str]=None					,	__snake_case   :							Optional[Any]=None					,	__snake_case   :							List[Any]=None					,	):


					'''simple docstring'''


					if attention_mask is None:
										UpperCAmelCase_   :				Optional[int]						=						np.where(input_ids != config.pad_token_id					,	1					,	0  )
					if decoder_attention_mask is None:
										UpperCAmelCase_   :				Tuple						=						np.where(decoder_input_ids != config.pad_token_id					,	1					,	0  )
					if head_mask is None:
										UpperCAmelCase_   :				Union[str, Any]						=						np.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										UpperCAmelCase_   :				Union[str, Any]						=						np.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										UpperCAmelCase_   :				Optional[Any]						=						np.ones((config.decoder_layers, config.decoder_attention_heads)  )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					}



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=2    ,		_UpperCamelCase=4    ,		_UpperCamelCase=4    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=0.02    ,		)     ->							Tuple:
							UpperCAmelCase_   :				List[Any]						=						parent
							UpperCAmelCase_   :				Dict						=						batch_size
							UpperCAmelCase_   :				str						=						seq_length
							UpperCAmelCase_   :				Tuple						=						is_training
							UpperCAmelCase_   :				Any						=						use_labels
							UpperCAmelCase_   :				Any						=						vocab_size
							UpperCAmelCase_   :				Tuple						=						hidden_size
							UpperCAmelCase_   :				Union[str, Any]						=						num_hidden_layers
							UpperCAmelCase_   :				Optional[Any]						=						num_attention_heads
							UpperCAmelCase_   :				Optional[int]						=						intermediate_size
							UpperCAmelCase_   :				Union[str, Any]						=						hidden_act
							UpperCAmelCase_   :				Tuple						=						hidden_dropout_prob
							UpperCAmelCase_   :				Optional[int]						=						attention_probs_dropout_prob
							UpperCAmelCase_   :				Union[str, Any]						=						max_position_embeddings
							UpperCAmelCase_   :				Tuple						=						eos_token_id
							UpperCAmelCase_   :				Tuple						=						pad_token_id
							UpperCAmelCase_   :				Any						=						bos_token_id
							UpperCAmelCase_   :				int						=						initializer_range



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Optional[int]						=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size       )    ,		3    ,		self.vocab_size       )
							UpperCAmelCase_   :				int						=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,		dtype=np.intaa       ))    ,		-1       )

							UpperCAmelCase_   :				Dict						=						shift_tokens_right(_UpperCamelCase    ,		1    ,		2       )

							UpperCAmelCase_   :				Optional[int]						=						BlenderbotConfig(
							    vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		initializer_range=self.initializer_range    ,		use_cache=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Tuple						=						prepare_blenderbot_inputs_dict(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							return config, inputs_dict



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self.prepare_config_and_inputs()
							return config, inputs_dict



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						2_0
							UpperCAmelCase_   :				int						=						model_class_name(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model.encode(inputs_dict['input_ids']       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							UpperCAmelCase_   :				int						=						model.init_cache(decoder_input_ids.shape[0]    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Tuple						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype='i4'       )

							UpperCAmelCase_   :				Tuple						=						jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
							UpperCAmelCase_   :				str						=						model.decode(
							    decoder_input_ids[:, :-1]    ,		_UpperCamelCase    ,		decoder_attention_mask=_UpperCamelCase    ,		past_key_values=_UpperCamelCase    ,		decoder_position_ids=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				str						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='i4'       )
							UpperCAmelCase_   :				Optional[int]						=						model.decode(
							    decoder_input_ids[:, -1:]    ,		_UpperCamelCase    ,		decoder_attention_mask=_UpperCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Dict						=						model.decode(_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
							self.parent.assertTrue(diff < 1E-3    ,		msg=f"Max diff is {diff}"       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				Union[str, Any]						=						2_0
							UpperCAmelCase_   :				Dict						=						model_class_name(_UpperCamelCase       )

							UpperCAmelCase_   :				int						=						model.encode(inputs_dict['input_ids']       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							UpperCAmelCase_   :				Any						=						jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
							    ]    ,		axis=-1    ,		)

							UpperCAmelCase_   :				List[Any]						=						model.init_cache(decoder_input_ids.shape[0]    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)

							UpperCAmelCase_   :				Tuple						=						model.decode(
							    decoder_input_ids[:, :-1]    ,		_UpperCamelCase    ,		decoder_attention_mask=_UpperCamelCase    ,		past_key_values=_UpperCamelCase    ,		decoder_position_ids=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				int						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='i4'       )
							UpperCAmelCase_   :				Dict						=						model.decode(
							    decoder_input_ids[:, -1:]    ,		_UpperCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=_UpperCamelCase    ,		decoder_position_ids=_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Optional[int]						=						model.decode(_UpperCamelCase    ,		_UpperCamelCase    ,		decoder_attention_mask=_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
							self.parent.assertTrue(diff < 1E-3    ,		msg=f"Max diff is {diff}"       )





@require_flax
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Any							=  9_9



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						np.array(
							    [
							        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
							        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
							        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
							        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
							        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
							        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
							        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
							        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
							        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
							        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
							        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
							        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
							        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
							    ]    ,		dtype=np.intaa    ,		)

							UpperCAmelCase_   :				str						=						input_ids.shape[0]
							UpperCAmelCase_   :				int						=						BlenderbotConfig(
							    vocab_size=self.vocab_size    ,		d_model=2_4    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=3_2    ,		decoder_ffn_dim=3_2    ,		max_position_embeddings=4_8    ,		eos_token_id=2    ,		pad_token_id=1    ,		bos_token_id=0    ,		)
							return config, input_ids, batch_size



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						self._get_config_and_data()
							UpperCAmelCase_   :				Dict						=						FlaxBlenderbotForConditionalGeneration(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						lm_model(input_ids=_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						BlenderbotConfig(
							    vocab_size=self.vocab_size    ,		d_model=1_4    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=8    ,		decoder_ffn_dim=8    ,		max_position_embeddings=4_8    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						FlaxBlenderbotForConditionalGeneration(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				Union[str, Any]						=						np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				List[Any]						=						lm_model(input_ids=_UpperCamelCase    ,		decoder_input_ids=_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Tuple						=						np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]    ,		dtype=np.intaa       )
							UpperCAmelCase_   :				Dict						=						shift_tokens_right(_UpperCamelCase    ,		1    ,		2       )
							UpperCAmelCase_   :				Dict						=						np.equal(_UpperCamelCase    ,		1       ).astype(np.floataa       ).sum()
							UpperCAmelCase_   :				Tuple						=						np.equal(_UpperCamelCase    ,		1       ).astype(np.floataa       ).sum()
							self.assertEqual(shifted.shape    ,		input_ids.shape       )
							self.assertEqual(_UpperCamelCase    ,		n_pad_before - 1       )
							self.assertTrue(np.equal(shifted[:, 0]    ,		2       ).all()       )





@require_flax
class   lowerCamelCase				(_snake_case			,  unittest.TestCase			,  _snake_case      ):







		'''simple docstring'''



		_snake_case   :   Optional[Any]							=  True
		_snake_case   :   List[str]							=  (
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		_snake_case   :   Union[str, Any]							=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Union[str, Any]						=						FlaxBlenderbotModelTester(self       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__       ):
																	UpperCAmelCase_   :				Optional[Any]						=						self._prepare_for_class(_UpperCamelCase    ,		_UpperCamelCase       )
																	UpperCAmelCase_   :				Optional[int]						=						model_class(_UpperCamelCase       )

																	@jax.jit
																	def encode_jitted(_UpperCamelCase    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       ):
																						return model.encode(input_ids=_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )

																	with self.subTest('JIT Enabled'       ):
																						UpperCAmelCase_   :				Optional[int]						=						encode_jitted(**_UpperCamelCase       ).to_tuple()

																	with self.subTest('JIT Disabled'       ):
																						with jax.disable_jit():
																											UpperCAmelCase_   :				Dict						=						encode_jitted(**_UpperCamelCase       ).to_tuple()

																	self.assertEqual(len(_UpperCamelCase       )    ,		len(_UpperCamelCase       )       )
																	for jitted_output, output in zip(_UpperCamelCase    ,		_UpperCamelCase       ):
																						self.assertEqual(jitted_output.shape    ,		output.shape       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__       ):
																	UpperCAmelCase_   :				List[str]						=						model_class(_UpperCamelCase       )
																	UpperCAmelCase_   :				Optional[int]						=						model.encode(inputs_dict['input_ids']    ,		inputs_dict['attention_mask']       )

																	UpperCAmelCase_   :				int						=						{
																	    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																	    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																	    'encoder_outputs': encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
																						return model.decode(
																						    decoder_input_ids=_UpperCamelCase    ,		decoder_attention_mask=_UpperCamelCase    ,		encoder_outputs=_UpperCamelCase    ,		)

																	with self.subTest('JIT Enabled'       ):
																						UpperCAmelCase_   :				List[str]						=						decode_jitted(**_UpperCamelCase       ).to_tuple()

																	with self.subTest('JIT Disabled'       ):
																						with jax.disable_jit():
																											UpperCAmelCase_   :				int						=						decode_jitted(**_UpperCamelCase       ).to_tuple()

																	self.assertEqual(len(_UpperCamelCase       )    ,		len(_UpperCamelCase       )       )
																	for jitted_output, output in zip(_UpperCamelCase    ,		_UpperCamelCase       ):
																						self.assertEqual(jitted_output.shape    ,		output.shape       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for model_class_name in self.all_model_classes:
												UpperCAmelCase_   :				Optional[int]						=						model_class_name.from_pretrained('facebook/blenderbot-400M-distill'       )
												# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
												UpperCAmelCase_   :				Union[str, Any]						=						np.ones((1, 1)       ) * model.config.eos_token_id
												UpperCAmelCase_   :				Dict						=						model(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )



		@unittest.skipUnless(jax_device != 'cpu'    ,		'3B test too slow on CPU.'       )
		@slow
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				List[str]						=						{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
							UpperCAmelCase_   :				List[str]						=						{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

							UpperCAmelCase_   :				str						=						FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'    ,		from_pt=_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'       )

							UpperCAmelCase_   :				Optional[Any]						=						['Sam']
							UpperCAmelCase_   :				Any						=						tokenizer(_UpperCamelCase    ,		return_tensors='jax'       )

							UpperCAmelCase_   :				List[str]						=						model.generate(**_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						'Sam is a great name. It means "sun" in Gaelic.'

							UpperCAmelCase_   :				Any						=						tokenizer.batch_decode(_UpperCamelCase    ,		**_UpperCamelCase       )
							assert generated_txt[0].strip() == tgt_text














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


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



class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		@register_to_config
		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None       )     ->							int:
							super().__init__()

							UpperCAmelCase_   :				str						=						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"

												UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(_UpperCamelCase    ,		_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Any						=						None

							UpperCAmelCase_   :				Any						=						torch.nn.Parameter(_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   VQModel
		_snake_case   :   CLIPTextModel
		_snake_case   :   CLIPTokenizer
		_snake_case   :   TransformeraDModel
		_snake_case   :   LearnedClassifierFreeSamplingEmbeddings
		_snake_case   :   VQDiffusionScheduler



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__()

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else 1

							# get prompt text embeddings
							UpperCAmelCase_   :				str						=						self.tokenizer(
							    _UpperCamelCase    ,		padding='max_length'    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='pt'    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_   :				List[Any]						=						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}"       )
												UpperCAmelCase_   :				str						=						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_   :				str						=						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
							UpperCAmelCase_   :				Dict						=						prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_   :				Dict						=						prompt_embeds.repeat_interleave(_UpperCamelCase    ,		dim=0       )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_   :				List[str]						=						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_   :				List[str]						=						negative_prompt_embeds.unsqueeze(0       ).repeat(_UpperCamelCase    ,		1    ,		1       )
												else:
																	UpperCAmelCase_   :				List[Any]						=						[''] * batch_size

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

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCAmelCase_   :				List[Any]						=						negative_prompt_embeds.shape[1]
												UpperCAmelCase_   :				Dict						=						negative_prompt_embeds.repeat(1    ,		_UpperCamelCase    ,		1       )
												UpperCAmelCase_   :				Any						=						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
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([negative_prompt_embeds, prompt_embeds]       )

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 5.0    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		)     ->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						1
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase       )}"       )

							UpperCAmelCase_   :				Union[str, Any]						=						batch_size * num_images_per_prompt

							UpperCAmelCase_   :				Optional[int]						=						guidance_scale > 1.0

							UpperCAmelCase_   :				Any						=						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

							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_   :				Tuple						=						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_   :				List[Any]						=						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)."       )
												UpperCAmelCase_   :				Any						=						latents.to(self.device       )

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

							UpperCAmelCase_   :				List[str]						=						self.scheduler.timesteps.to(self.device       )

							UpperCAmelCase_   :				Union[str, Any]						=						latents

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

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCAmelCase_   :				Dict						=						self.transformer(_UpperCamelCase    ,		encoder_hidden_states=_UpperCamelCase    ,		timestep=_UpperCamelCase       ).sample

												if do_classifier_free_guidance:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						model_output.chunk(2       )
																	UpperCAmelCase_   :				Optional[int]						=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase    ,		dim=1    ,		keepdim=_UpperCamelCase       )

												UpperCAmelCase_   :				str						=						self.truncate(_UpperCamelCase    ,		_UpperCamelCase       )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_   :				Optional[int]						=						model_output.clamp(-7_0       )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_   :				Union[str, Any]						=						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       )

							UpperCAmelCase_   :				str						=						self.vqvae.config.vq_embed_dim
							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCAmelCase_   :				int						=						self.vqvae.quantize.get_codebook_entry(_UpperCamelCase    ,		shape=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						self.vqvae.decode(_UpperCamelCase    ,		force_not_quantize=_UpperCamelCase       ).sample

							UpperCAmelCase_   :				List[str]						=						(image / 2 + 0.5).clamp(0    ,		1       )
							UpperCAmelCase_   :				Any						=						image.cpu().permute(0    ,		2    ,		3    ,		1       ).numpy()

							if output_type == "pil":
												UpperCAmelCase_   :				int						=						self.numpy_to_pil(_UpperCamelCase       )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							torch.FloatTensor:
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						torch.sort(_UpperCamelCase    ,		1    ,		descending=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						torch.exp(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							UpperCAmelCase_   :				Tuple						=						torch.full_like(keep_mask[:, 0:1, :]    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						torch.cat((all_true, keep_mask)    ,		dim=1       )
							UpperCAmelCase_   :				int						=						keep_mask[:, :-1, :]

							UpperCAmelCase_   :				Any						=						keep_mask.gather(1    ,		indices.argsort(1       )       )

							UpperCAmelCase_   :				str						=						log_p_x_0.clone()

							UpperCAmelCase_   :				Any						=						-torch.inf  # -inf = log(0)

							return rv






