import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
  )
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
  UpperCAmelCase							   =      False






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=14							,     lowerCAmelCase_=7							,     lowerCAmelCase_=True							,     lowerCAmelCase_=False							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_=99							,     lowerCAmelCase_=32							,     lowerCAmelCase_=5							,     lowerCAmelCase_=37							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=5_12							,     lowerCAmelCase_=16							,     lowerCAmelCase_=2							,     lowerCAmelCase_=3							,     lowerCAmelCase_=4							,     lowerCAmelCase_=None							,     )	->int:
        a  :    Optional[int]				=						parent
        a  :    Optional[Any]				=						batch_size
        a  :    Union[str, Any]				=						seq_length
        a  :    int				=						is_training
        a  :    List[Any]				=						use_token_type_ids
        a  :    Tuple				=						use_input_mask
        a  :    Optional[int]				=						use_labels
        a  :    List[str]				=						use_mc_token_ids
        a  :    str				=						vocab_size
        a  :    Any				=						hidden_size
        a  :    List[Any]				=						num_hidden_layers
        a  :    Optional[int]				=						intermediate_size
        a  :    Dict				=						hidden_act
        a  :    Tuple				=						hidden_dropout_prob
        a  :    str				=						attention_probs_dropout_prob
        a  :    Optional[Any]				=						max_position_embeddings
        a  :    Optional[int]				=						type_vocab_size
        a  :    Any				=						type_sequence_label_size
        a  :    Any				=						num_labels
        a  :    int				=						num_choices
        a  :    List[str]				=						scope
        a  :    List[Any]				=						vocab_size - 1
        a  :    Optional[Any]				=						vocab_size - 1
        a  :    Optional[Any]				=						vocab_size - 1
   def _lowercase(    self    )	->Optional[int]:
        return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''    )
   def _lowercase(    self							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False    )	->Tuple:
        a  :    int				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )

        a  :    List[str]				=						None
        if self.use_input_mask:
             a  :    Optional[int]				=						random_attention_mask([self.batch_size, self.seq_length]    )

        a  :    Union[str, Any]				=						None
        if self.use_token_type_ids:
             a  :    str				=						ids_tensor([self.batch_size, self.seq_length]							,     self.type_vocab_size    )

        a  :    Optional[Any]				=						None
        if self.use_mc_token_ids:
             a  :    Any				=						ids_tensor([self.batch_size, self.num_choices]							,     self.seq_length    )

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

        a  :    Optional[int]				=						self.get_config(
            gradient_checkpointing=lowerCAmelCase_							,     scale_attn_by_inverse_layer_idx=lowerCAmelCase_							,     reorder_and_upcast_attn=lowerCAmelCase_							,     )

        return (
            config,
            input_ids,
            input_mask,
            None,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )
   def _lowercase(    self							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False    )	->Dict:
        return RwkvConfig(
            vocab_size=self.vocab_size							,     hidden_size=self.hidden_size							,     num_hidden_layers=self.num_hidden_layers							,     intermediate_size=self.intermediate_size							,     activation_function=self.hidden_act							,     resid_pdrop=self.hidden_dropout_prob							,     attn_pdrop=self.attention_probs_dropout_prob							,     n_positions=self.max_position_embeddings							,     type_vocab_size=self.type_vocab_size							,     use_cache=lowerCAmelCase_							,     bos_token_id=self.bos_token_id							,     eos_token_id=self.eos_token_id							,     pad_token_id=self.pad_token_id							,     gradient_checkpointing=lowerCAmelCase_							,     scale_attn_by_inverse_layer_idx=lowerCAmelCase_							,     reorder_and_upcast_attn=lowerCAmelCase_							,     )
   def _lowercase(    self    )	->Optional[Any]:
        a  :    Optional[Any]				=						self.get_config()
        a  :    Any				=						3_00
        return config
   def _lowercase(    self    )	->int:
        (
            a							
        )  :    Any				=						self.prepare_config_and_inputs()

        a  :    str				=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
        a  :    str				=						ids_tensor([self.batch_size, self.seq_length]							,     vocab_size=2    )

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Optional[int]:
        a  :    List[str]				=						True
        a  :    Union[str, Any]				=						RwkvModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    Tuple				=						model(lowerCAmelCase_    )

        self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)    )
        self.parent.assertEqual(len(result.hidden_states    )							,     config.num_hidden_layers + 1    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Optional[int]:
        a  :    List[Any]				=						RwkvForCausalLM(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    List[Any]				=						model(lowerCAmelCase_							,     labels=lowerCAmelCase_    )
        self.parent.assertEqual(result.loss.shape							,     ()    )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Tuple:
        a  :    int				=						RwkvModel(config=lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        model.eval()

        a  :    Dict				=						model(lowerCAmelCase_    )
        a  :    Union[str, Any]				=						outputs.last_hidden_state

        a  :    str				=						model(input_ids[:, :2]    )
        a  :    List[str]				=						outputs.last_hidden_state

        # Using the state computed on the first inputs, we will get the same output
        a  :    List[str]				=						model(input_ids[:, 2:]							,     state=outputs.state    )
        a  :    str				=						outputs.last_hidden_state

        self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							,     dim=1    )							,     lowerCAmelCase_							,     atol=1e-5    )    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_							,     lowerCAmelCase_=False    )	->Dict:
        a  :    Dict				=						RwkvForCausalLM(lowerCAmelCase_    )
        model.to(lowerCAmelCase_    )
        if gradient_checkpointing:
             model.gradient_checkpointing_enable()

        a  :    List[Any]				=						model(lowerCAmelCase_							,     labels=lowerCAmelCase_    )
        self.parent.assertEqual(result.loss.shape							,     ()    )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)    )
        result.loss.backward()


   def _lowercase(    self    )	->Tuple:
        a  :    str				=						self.prepare_config_and_inputs()

        (
            a							
        )  :    Any				=						config_and_inputs

        a  :    List[str]				=						{'''input_ids''': input_ids}

        return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0       ,      reason='See https://github.com/huggingface/transformers/pull/24204'	)
@require_torch
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__       ,      snake_case__       ,      unittest.TestCase	):
   UpperCamelCase_				:						Dict 			=   (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
   UpperCamelCase_				:						List[Any] 			=   (
       {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
   )
   # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
   UpperCamelCase_				:						Optional[Any] 			=   False
   UpperCamelCase_				:						Dict 			=   False
   UpperCamelCase_				:						Optional[int] 			=   False
   UpperCamelCase_				:						int 			=   False
   UpperCamelCase_				:						Optional[int] 			=   False  # Rwkv does not support head masking
   def _lowercase(    self    )	->List[str]:
        a  :    Optional[int]				=						RwkvModelTester(self    )
        a  :    Dict				=						ConfigTester(
            self							,     config_class=lowerCAmelCase_							,     n_embd=37							,     common_properties=['''hidden_size''', '''num_hidden_layers''']    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->Dict:
        if isinstance(lowerCAmelCase_							,     torch.Tensor    ):
             a  :    Union[str, Any]				=						member.max().item(), member.min().item()
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) or isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Optional[Any]				=						max(lowerCAmelCase_    ), min(lowerCAmelCase_    )

        if not isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             raise TypeError('''container should be a list or tuple'''    )
        elif len(lowerCAmelCase_    ) != 2:
             raise ValueError('''container should have 2 elements'''    )

        a  :    Optional[int]				=						container

        a  :    Dict				=						(min_value >= expected_min) and (max_value <= expected_max)

        if not is_inside_interval:
             a  :    Tuple				=						'''%s not found in %s''' % (safe_repr(lowerCAmelCase_    ), safe_repr(lowerCAmelCase_    ))
             self.fail(self._formatMessage(lowerCAmelCase_							,     lowerCAmelCase_    )    )
   def _lowercase(    self    )	->str:
        self.config_tester.run_common_tests()
   def _lowercase(    self    )	->Tuple:
        a  :    int				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_rwkv_model(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Dict:
        a  :    Dict				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causl_lm(*lowerCAmelCase_    )
   def _lowercase(    self    )	->List[str]:
        a  :    str				=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_state_equivalency(*lowerCAmelCase_    )
   def _lowercase(    self    )	->Optional[Any]:
        a  :    str				=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             a  :    List[str]				=						model_class(config=lowerCAmelCase_    )
             for name, param in model.named_parameters():
                  if "time_decay" in name:
                       if param.requires_grad:
                            self.assertTrue(param.data.max().item() == 3.0    )
                            self.assertTrue(param.data.min().item() == -5.0    )
                  elif "time_first" in name:
                       if param.requires_grad:
                            # check if it's a ones like
                            self.assertTrue(torch.allclose(param.data							,     torch.ones_like(param.data    )							,     atol=1e-5							,     rtol=1e-5    )    )
                  elif any(x in name for x in ['''time_mix_key''', '''time_mix_receptance''']    ):
                       if param.requires_grad:
                            self.assertInterval(
                                param.data							,     [0.0, 1.0]							,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,     )
                  elif "time_mix_value" in name:
                       if param.requires_grad:
                            self.assertInterval(
                                param.data							,     [0.0, 1.3]							,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,     )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.model_tester.prepare_config_and_inputs_for_common()
        a  :    List[str]				=						True

        a  :    Optional[int]				=						getattr(self.model_tester							,     '''seq_length'''							,     lowerCAmelCase_    )

        for model_class in self.all_model_classes:
             a  :    Tuple				=						True
             a  :    str				=						False
             a  :    str				=						True
             a  :    Union[str, Any]				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Optional[int]				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    Dict				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    int				=						model(**lowerCAmelCase_    )
             a  :    str				=						outputs.attentions
             self.assertEqual(len(lowerCAmelCase_    )							,     self.model_tester.num_hidden_layers    )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             a  :    str				=						True
             a  :    Any				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Tuple				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    Tuple				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    Any				=						model(**lowerCAmelCase_    )
             a  :    Union[str, Any]				=						outputs.attentions
             self.assertEqual(len(lowerCAmelCase_    )							,     self.model_tester.num_hidden_layers    )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]    )							,     [batch_size, seq_len, config.hidden_size]							,     )
             a  :    Optional[Any]				=						len(lowerCAmelCase_    )

             # Check attention is always last and order is fine
             a  :    List[str]				=						True
             a  :    Union[str, Any]				=						True
             a  :    Tuple				=						model_class(lowerCAmelCase_    )
             model.to(lowerCAmelCase_    )
             model.eval()

             a  :    Optional[Any]				=						self._prepare_for_class(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    int				=						inputs['''input_ids'''].shape[0]
             with torch.no_grad():
                  a  :    List[Any]				=						model(**lowerCAmelCase_    )

             a  :    List[str]				=						1
             self.assertEqual(out_len + added_hidden_states							,     len(lowerCAmelCase_    )    )

             a  :    str				=						outputs.attentions

             self.assertEqual(len(lowerCAmelCase_    )							,     self.model_tester.num_hidden_layers    )
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]    )							,     [batch_size, seq_len, config.hidden_size]							,     )


   @slow
   def _lowercase(    self    )	->str:
        for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a  :    List[str]				=						RwkvModel.from_pretrained(lowerCAmelCase_    )
             self.assertIsNotNone(lowerCAmelCase_    )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0       ,      reason='See https://github.com/huggingface/transformers/pull/24204'	)
@slow
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   def _lowercase(    self    )	->List[Any]:
        a  :    List[Any]				=						'''RWKV/rwkv-4-169m-pile'''
        a  :    Union[str, Any]				=						AutoTokenizer.from_pretrained(self.model_id    )
   def _lowercase(    self    )	->Tuple:
        a  :    Tuple				=						'''Hello my name is Jasmine and I am a newbie to the'''
        a  :    List[Any]				=						RwkvForCausalLM.from_pretrained(self.model_id    ).to(lowerCAmelCase_    )

        a  :    Optional[int]				=						self.tokenizer('''Hello my name is'''							,     return_tensors='''pt'''    ).input_ids.to(lowerCAmelCase_    )
        a  :    Optional[int]				=						model.generate(lowerCAmelCase_							,     max_new_tokens=10    )
        a  :    Dict				=						self.tokenizer.decode(output[0].tolist()    )

        self.assertEqual(lowerCAmelCase_							,     lowerCAmelCase_    )


   def _lowercase(    self    )	->Union[str, Any]:
        a  :    Any				=						'''Hello my name is Jasmine and I am a newbie to the'''

        a  :    str				=						self.tokenizer('''Hello my name is'''							,     return_tensors='''pt'''    ).input_ids.to(lowerCAmelCase_    )
        a  :    Any				=						RwkvForCausalLM.from_pretrained(self.model_id							,     torch_dtype=torch.bfloataa    ).to(lowerCAmelCase_    )

        a  :    Optional[Any]				=						model.generate(lowerCAmelCase_							,     max_new_tokens=10    )
        a  :    Optional[Any]				=						self.tokenizer.decode(output[0].tolist()    )

        self.assertEqual(lowerCAmelCase_							,     lowerCAmelCase_    )
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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->int:
        super().__init__()

        self.register_modules(
            prior=lowerCAmelCase_							,     text_encoder=lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     scheduler=lowerCAmelCase_							,     renderer=lowerCAmelCase_							,     )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->str:
        if latents is None:
             a  :    List[str]				=						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}"""    )
             a  :    str				=						latents.to(lowerCAmelCase_    )

        a  :    Tuple				=						latents * scheduler.init_noise_sigma
        return latents
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->List[Any]:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

        a  :    Dict				=						torch.device(F"""cuda:{gpu_id}"""    )

        a  :    Union[str, Any]				=						[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(lowerCAmelCase_							,     lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->str:
        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.'''    )

        a  :    Union[str, Any]				=						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)

        a  :    Dict				=						None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
             a			,							a  :    str				=						cpu_offload_with_hook(lowerCAmelCase_							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        if self.safety_checker is not None:
             a			,							a  :    Optional[Any]				=						cpu_offload_with_hook(self.safety_checker							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        # We'll offload the last model manually.
        a  :    Any				=						hook
   @property
   def _lowercase(    self    )	->Any:
        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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Optional[Any]:
        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
        a  :    Dict				=						0
        # get prompt text embeddings
        a  :    Any				=						self.tokenizer(
            lowerCAmelCase_							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )
        a  :    List[str]				=						text_inputs.input_ids
        a  :    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_    ):
             a  :    Union[str, Any]				=						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}"""    )

        a  :    Optional[int]				=						self.text_encoder(text_input_ids.to(lowerCAmelCase_    )    )
        a  :    Optional[Any]				=						text_encoder_output.text_embeds

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

        if do_classifier_free_guidance:
             a  :    Optional[int]				=						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
             a  :    Dict				=						torch.cat([negative_prompt_embeds, prompt_embeds]    )

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

        return prompt_embeds


   @torch.no_grad()
   @replace_example_docstring(lowerCAmelCase_    )
   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 25							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 4.0							,     lowerCAmelCase_ = 64							,     lowerCAmelCase_ = "pil"							,     lowerCAmelCase_ = True							,     )	->Dict:

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Tuple				=						1
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Dict				=						len(lowerCAmelCase_    )
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_    )}"""    )

        a  :    Union[str, Any]				=						self._execution_device

        a  :    List[str]				=						batch_size * num_images_per_prompt

        a  :    Optional[int]				=						guidance_scale > 1.0
        a  :    Union[str, Any]				=						self._encode_prompt(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # prior

        self.scheduler.set_timesteps(lowerCAmelCase_							,     device=lowerCAmelCase_    )
        a  :    str				=						self.scheduler.timesteps

        a  :    Any				=						self.prior.config.num_embeddings
        a  :    Dict				=						self.prior.config.embedding_dim

        a  :    Any				=						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
        a  :    Any				=						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
             a  :    int				=						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
             a  :    List[str]				=						self.scheduler.scale_model_input(lowerCAmelCase_							,     lowerCAmelCase_    )

             a  :    Union[str, Any]				=						self.prior(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     proj_embedding=lowerCAmelCase_							,     ).predicted_image_embedding

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

             if do_classifier_free_guidance is not None:
                  a			,							a  :    Tuple				=						noise_pred.chunk(2    )
                  a  :    Tuple				=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             a  :    List[Any]				=						self.scheduler.step(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     sample=lowerCAmelCase_							,     ).prev_sample

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

        a  :    int				=						[]
        for i, latent in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						self.renderer.decode(
                 latent[None, :]							,     lowerCAmelCase_							,     size=lowerCAmelCase_							,     ray_batch_size=40_96							,     n_coarse_samples=64							,     n_fine_samples=1_28							,     )
             images.append(lowerCAmelCase_    )

        a  :    Optional[Any]				=						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}"""    )

        a  :    Any				=						images.cpu().numpy()

        if output_type == "pil":
             a  :    Dict				=						[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_    )