





"""simple docstring"""




import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    ErnieMForInformationExtraction,
						    ErnieMForMultipleChoice,
						    ErnieMForQuestionAnswering,
						    ErnieMForSequenceClassification,
						    ErnieMForTokenClassification,
						    ErnieMModel,
						)
						from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST




class 					_a		:






				def __init__(					self						:       Optional[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Any=1_3,					lowerCAmelCase__						:       Dict=7,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       Union[str, Any]=9_9,					lowerCAmelCase__						:       int=3_2,					lowerCAmelCase__						:       Optional[Any]=5,					lowerCAmelCase__						:       List[Any]=4,					lowerCAmelCase__						:       List[Any]=3_7,					lowerCAmelCase__						:       Dict="gelu",					lowerCAmelCase__						:       Optional[Any]=0.1,					lowerCAmelCase__						:       Dict=0.1,					lowerCAmelCase__						:       List[Any]=5_1_2,					lowerCAmelCase__						:       int=1_6,					lowerCAmelCase__						:       Optional[Any]=2,					lowerCAmelCase__						:       List[Any]=0.02,					lowerCAmelCase__						:       int=3,					lowerCAmelCase__						:       List[Any]=4,					lowerCAmelCase__						:       Any=None,					)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       parent
										_UpperCamelCase     :   str      =       batch_size
										_UpperCamelCase     :   str      =       seq_length
										_UpperCamelCase     :   List[str]      =       is_training
										_UpperCamelCase     :   List[str]      =       use_input_mask
										_UpperCamelCase     :   Optional[int]      =       use_labels
										_UpperCamelCase     :   Any      =       vocab_size
										_UpperCamelCase     :   int      =       hidden_size
										_UpperCamelCase     :   Any      =       num_hidden_layers
										_UpperCamelCase     :   List[Any]      =       num_attention_heads
										_UpperCamelCase     :   Any      =       intermediate_size
										_UpperCamelCase     :   Any      =       hidden_act
										_UpperCamelCase     :   Any      =       hidden_dropout_prob
										_UpperCamelCase     :   Dict      =       attention_probs_dropout_prob
										_UpperCamelCase     :   Union[str, Any]      =       max_position_embeddings
										_UpperCamelCase     :   int      =       type_vocab_size
										_UpperCamelCase     :   Union[str, Any]      =       type_sequence_label_size
										_UpperCamelCase     :   Dict      =       initializer_range
										_UpperCamelCase     :   List[Any]      =       num_labels
										_UpperCamelCase     :   List[str]      =       num_choices
										_UpperCamelCase     :   Optional[int]      =       scope






				def 							snake_case      (					self						:       Union[str, Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ids_tensor([self.batch_size, self.seq_length],					self.vocab_size				)

										_UpperCamelCase     :   List[str]      =       None
										if self.use_input_mask:
																_UpperCamelCase     :   Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length]				)

										_UpperCamelCase     :   int      =       None
										_UpperCamelCase     :   Tuple      =       None
										_UpperCamelCase     :   Optional[int]      =       None
										if self.use_labels:
																_UpperCamelCase     :   List[Any]      =       ids_tensor([self.batch_size],					self.type_sequence_label_size				)
																_UpperCamelCase     :   Dict      =       ids_tensor([self.batch_size, self.seq_length],					self.num_labels				)
																_UpperCamelCase     :   List[Any]      =       ids_tensor([self.batch_size],					self.num_choices				)

										_UpperCamelCase     :   Dict      =       self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






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




										'''simple docstring'''



										_UpperCamelCase     :   int      =       ids_tensor([self.batch_size, self.seq_length],					self.vocab_size				)

										_UpperCamelCase     :   Dict      =       None
										if self.use_input_mask:
																_UpperCamelCase     :   Dict      =       random_attention_mask([self.batch_size, self.seq_length]				)
										_UpperCamelCase     :   Tuple      =       self.get_config()

										return config, input_ids, input_mask






				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



										return ErnieMConfig(
										    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					initializer_range=self.initializer_range,					)






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       ErnieMModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__,					return_dict=lowerCAmelCase__				)
										self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ErnieMForQuestionAnswering(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Union[str, Any]      =       model(
										    lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					start_positions=lowerCAmelCase__,					end_positions=lowerCAmelCase__,					)
										self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)				)
										self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)				)






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Union[str, Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       ErnieMForInformationExtraction(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Dict      =       torch.ones_like(lowerCAmelCase__,					dtype=torch.floataa				)
										_UpperCamelCase     :   str      =       model(
										    lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					start_positions=lowerCAmelCase__,					end_positions=lowerCAmelCase__,					)
										self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)				)
										self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       List[str]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.num_labels
										_UpperCamelCase     :   int      =       ErnieMForSequenceClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[int]      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					labels=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.num_labels
										_UpperCamelCase     :   List[str]      =       ErnieMForTokenClassification(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										input_ids.to(lowerCAmelCase__				)
										input_mask.to(lowerCAmelCase__				)
										token_labels.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					labels=lowerCAmelCase__				)

										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Tuple				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.num_choices
										_UpperCamelCase     :   Union[str, Any]      =       ErnieMForMultipleChoice(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       input_ids.unsqueeze(1				).expand(-1,					self.num_choices,					-1				).contiguous()
										_UpperCamelCase     :   Optional[int]      =       input_mask.unsqueeze(1				).expand(-1,					self.num_choices,					-1				).contiguous()
										_UpperCamelCase     :   Optional[int]      =       model(
										    lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					labels=lowerCAmelCase__,					)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)				)






				def 							snake_case      (					self						:       Optional[int]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				
										)     :   Tuple      =       config_and_inputs
										_UpperCamelCase     :   Tuple      =       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_torch
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        ErnieMModel,
				        ErnieMForMultipleChoice,
				        ErnieMForQuestionAnswering,
				        ErnieMForSequenceClassification,
				        ErnieMForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase								=       ()
				UpperCamelCase								=       (
				    {
				        '''feature-extraction''': ErnieMModel,
				        '''question-answering''': ErnieMForQuestionAnswering,
				        '''text-classification''': ErnieMForSequenceClassification,
				        '''token-classification''': ErnieMForTokenClassification,
				        '''zero-shot''': ErnieMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase								=       False






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Dict				)      -> Any:




										'''simple docstring'''



										if pipeline_test_casse_name == "QAPipelineTests":
																return True

										return False






				def 							snake_case      (					self						:       List[str]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       ErnieMModelTester(self				)
										_UpperCamelCase     :   Tuple      =       ConfigTester(self,					config_class=lowerCAmelCase__,					hidden_size=3_7				)






				def 							snake_case      (					self						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def 							snake_case      (					self						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																_UpperCamelCase     :   Union[str, Any]      =       type
																self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_information_extraction(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       List[Any]				)      -> str:




										'''simple docstring'''



										for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase     :   List[str]      =       ErnieMModel.from_pretrained(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)




@require_torch
class 					_a		( unittest.TestCase							):






				@slow
				def 							snake_case      (					self						:       Tuple				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''				)
										model.eval()
										_UpperCamelCase     :   Dict      =       torch.tensor([[0, 1, 2, 3, 4, 5]]				)
										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)[0]

										# TODO Replace vocab size
										_UpperCamelCase     :   List[Any]      =       7_6_8

										_UpperCamelCase     :   List[Any]      =       torch.Size((1, 6, hidden_size)				)
										self.assertEqual(output.shape,					lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       torch.tensor(
										    [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]				)

										self.assertTrue(torch.allclose(output[:, :3, :3],					lowerCAmelCase__,					atol=1e-3				)				)










"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

UpperCamelCase_	 ="""
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""




@dataclass
class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       42




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       int,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       HeunDiscreteScheduler,					lowerCAmelCase__						:       ShapERenderer,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__()

										self.register_modules(
										    prior=lowerCAmelCase__,					text_encoder=lowerCAmelCase__,					tokenizer=lowerCAmelCase__,					scheduler=lowerCAmelCase__,					renderer=lowerCAmelCase__,					)






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Any      =       randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__,					dtype=lowerCAmelCase__				)
										else:
																if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
																_UpperCamelCase     :   List[Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       str=0				)      -> Dict:




										'''simple docstring'''



										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

										_UpperCamelCase     :   List[str]      =       torch.device(f"""cuda:{gpu_id}"""				)

										_UpperCamelCase     :   List[str]      =       [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Tuple=0				)      -> List[Any]:




										'''simple docstring'''



										if is_accelerate_available() and is_accelerate_version('''>=''',					'''0.17.0.dev0'''				):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

										_UpperCamelCase     :   str      =       torch.device(f"""cuda:{gpu_id}"""				)

										if self.device.type != "cpu":
																self.to('''cpu''',					silence_dtype_warnings=lowerCAmelCase__				)
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										_UpperCamelCase     :   Tuple      =       None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(lowerCAmelCase__,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										if self.safety_checker is not None:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(self.safety_checker,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										# We'll offload the last model manually.
										_UpperCamelCase     :   Union[str, Any]      =       hook






				@property
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										if self.device != torch.device('''meta'''				) or not hasattr(self.text_encoder,					'''_hf_hook'''				):
																return self.device
										for module in self.text_encoder.modules():
																if (
																    hasattr(lowerCAmelCase__,					'''_hf_hook'''				)
																    and hasattr(module._hf_hook,					'''execution_device'''				)
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device				)
										return self.device






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''simple docstring'''



										len(lowerCAmelCase__				) if isinstance(lowerCAmelCase__,					lowerCAmelCase__				) else 1

										# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
										_UpperCamelCase     :   Optional[Any]      =       0
										# get prompt text embeddings
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(
										    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
										_UpperCamelCase     :   Union[str, Any]      =       text_inputs.input_ids
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(lowerCAmelCase__,					padding='''longest''',					return_tensors='''pt'''				).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase__,					lowerCAmelCase__				):
																_UpperCamelCase     :   Tuple      =       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
																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     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)
										_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds

										_UpperCamelCase     :   Any      =       prompt_embeds.repeat_interleave(lowerCAmelCase__,					dim=0				)
										# in Shap-E it normalize the prompt_embeds and then later rescale it
										_UpperCamelCase     :   str      =       prompt_embeds / torch.linalg.norm(lowerCAmelCase__,					dim=-1,					keepdim=lowerCAmelCase__				)

										if do_classifier_free_guidance:
																_UpperCamelCase     :   Any      =       torch.zeros_like(lowerCAmelCase__				)

																# 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     :   Optional[Any]      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

										# Rescale the features to have unit variance
										_UpperCamelCase     :   Union[str, Any]      =       math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds

										return prompt_embeds






				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__				)
				def __call__(					self						:       Optional[int],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int = 1,					lowerCAmelCase__						:       int = 2_5,					lowerCAmelCase__						:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,					lowerCAmelCase__						:       Optional[torch.FloatTensor] = None,					lowerCAmelCase__						:       float = 4.0,					lowerCAmelCase__						:       int = 6_4,					lowerCAmelCase__						:       Optional[str] = "pil",					lowerCAmelCase__						:       bool = True,					)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       self._execution_device

										_UpperCamelCase     :   List[Any]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[str]      =       guidance_scale > 1.0
										_UpperCamelCase     :   str      =       self._encode_prompt(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       self.scheduler.timesteps

										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.num_embeddings
										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.embedding_dim

										_UpperCamelCase     :   Tuple      =       self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim),					prompt_embeds.dtype,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					self.scheduler,					)

										# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
										_UpperCamelCase     :   Dict      =       latents.reshape(latents.shape[0],					lowerCAmelCase__,					lowerCAmelCase__				)

										for i, t in enumerate(self.progress_bar(lowerCAmelCase__				)				):
																# expand the latents if we are doing classifier free guidance
																_UpperCamelCase     :   Optional[int]      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																_UpperCamelCase     :   Dict      =       self.scheduler.scale_model_input(lowerCAmelCase__,					lowerCAmelCase__				)

																_UpperCamelCase     :   Tuple      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					).predicted_image_embedding

																# remove the variance
																_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       noise_pred.split(
																    scaled_model_input.shape[2],					dim=2				)  # batch_size, num_embeddings, embedding_dim

																if do_classifier_free_guidance is not None:
																						_UpperCamelCase  ,				_UpperCamelCase     :   List[str]      =       noise_pred.chunk(2				)
																						_UpperCamelCase     :   Optional[Any]      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																_UpperCamelCase     :   Dict      =       self.scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					).prev_sample

										if output_type == "latent":
																return ShapEPipelineOutput(images=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       []
										for i, latent in enumerate(lowerCAmelCase__				):
																_UpperCamelCase     :   Union[str, Any]      =       self.renderer.decode(
																    latent[None, :],					lowerCAmelCase__,					size=lowerCAmelCase__,					ray_batch_size=4_0_9_6,					n_coarse_samples=6_4,					n_fine_samples=1_2_8,					)
																images.append(lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       torch.stack(lowerCAmelCase__				)

										if output_type not in ["np", "pil"]:
																raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}"""				)

										_UpperCamelCase     :   Dict      =       images.cpu().numpy()

										if output_type == "pil":
																_UpperCamelCase     :   Any      =       [self.numpy_to_pil(lowerCAmelCase__				) for image in images]

										# Offload last model to CPU
										if hasattr(self,					'''final_offload_hook'''				) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

										if not return_dict:
																return (images,)

										return ShapEPipelineOutput(images=lowerCAmelCase__				)



