


"""simple docstring"""


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
   _snake_case          =							"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   snake_case						(				_a:     Any		,   _a:     Optional[Any]		,   _a:     Union[str, Any]=None		,   _a:     List[Any]=None		,   _a:     Tuple=None		,   _a:     Union[str, Any]=None		,   _a:     List[Any]=None		,   _a:     Dict=None		,   )->		List[Any]:
      '''simple docstring'''


      if attention_mask is None:
            lowerCamelCase__           =		np.where(input_ids != config.pad_token_id		,   1		,   0							)
      if decoder_attention_mask is None:
            lowerCamelCase__           =		np.where(decoder_input_ids != config.pad_token_id		,   1		,   0							)
      if head_mask is None:
            lowerCamelCase__           =		np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            lowerCamelCase__           =		np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            lowerCamelCase__           =		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   _a      :
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict=13							,					SCREAMING_SNAKE_CASE__							:					int=7							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					List[str]=False							,					SCREAMING_SNAKE_CASE__							:					int=99							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=16							,					SCREAMING_SNAKE_CASE__							:					Any=2							,					SCREAMING_SNAKE_CASE__							:					List[Any]=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=4							,					SCREAMING_SNAKE_CASE__							:					Dict="gelu"							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.1							,					SCREAMING_SNAKE_CASE__							:					Any=32							,					SCREAMING_SNAKE_CASE__							:					List[Any]=2							,					SCREAMING_SNAKE_CASE__							:					str=1							,					SCREAMING_SNAKE_CASE__							:					Any=0							,					SCREAMING_SNAKE_CASE__							:					int=0.02							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		seq_length
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		eos_token_id
          lowerCamelCase__           =		pad_token_id
          lowerCamelCase__           =		bos_token_id
          lowerCamelCase__           =		initializer_range
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size				)							,					3							,					self.vocab_size				)
          lowerCamelCase__           =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,					dtype=np.intaa				))							,					-1				)

          lowerCamelCase__           =		shift_tokens_right(_UpperCamelCase							,					1							,					2				)

          lowerCamelCase__           =		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							,					)
          lowerCamelCase__           =		prepare_blenderbot_inputs_dict(_UpperCamelCase							,					_UpperCamelCase							,					_UpperCamelCase				)
          return config, inputs_dict
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          return config, inputs_dict
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		20
          lowerCamelCase__           =		model_class_name(_UpperCamelCase				)

          lowerCamelCase__           =		model.encode(inputs_dict['input_ids']				)

          lowerCamelCase__           =		(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

          lowerCamelCase__           =		model.init_cache(decoder_input_ids.shape[0]							,					_UpperCamelCase							,					_UpperCamelCase				)
          lowerCamelCase__           =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,					dtype='i4'				)

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

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

          lowerCamelCase__           =		model.decode(_UpperCamelCase							,					_UpperCamelCase				)

          lowerCamelCase__           =		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							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]				):
          lowerCamelCase__           =		20
          lowerCamelCase__           =		model_class_name(_UpperCamelCase				)

          lowerCamelCase__           =		model.encode(inputs_dict['input_ids']				)

          lowerCamelCase__           =		(
              inputs_dict["""decoder_input_ids"""],
              inputs_dict["""decoder_attention_mask"""],
          )

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

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

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

          lowerCamelCase__           =		model.decode(_UpperCamelCase							,					_UpperCamelCase							,					decoder_attention_mask=_UpperCamelCase				)

          lowerCamelCase__           =		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   _a      (      unittest.TestCase ):
    a_    :				List[str]             =    99
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ]							,					dtype=np.intaa							,					)

          lowerCamelCase__           =		input_ids.shape[0]
          lowerCamelCase__           =		BlenderbotConfig(
              vocab_size=self.vocab_size							,					d_model=24							,					encoder_layers=2							,					decoder_layers=2							,					encoder_attention_heads=2							,					decoder_attention_heads=2							,					encoder_ffn_dim=32							,					decoder_ffn_dim=32							,					max_position_embeddings=48							,					eos_token_id=2							,					pad_token_id=1							,					bos_token_id=0							,					)
          return config, input_ids, batch_size
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self._get_config_and_data()
          lowerCamelCase__           =		FlaxBlenderbotForConditionalGeneration(_UpperCamelCase				)
          lowerCamelCase__           =		lm_model(input_ids=_UpperCamelCase				)
          lowerCamelCase__           =		(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape							,					_UpperCamelCase				)
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		BlenderbotConfig(
              vocab_size=self.vocab_size							,					d_model=14							,					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=48							,					)
          lowerCamelCase__           =		FlaxBlenderbotForConditionalGeneration(_UpperCamelCase				)
          lowerCamelCase__           =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,					dtype=np.intaa				)
          lowerCamelCase__           =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,					dtype=np.intaa				)
          lowerCamelCase__           =		lm_model(input_ids=_UpperCamelCase							,					decoder_input_ids=_UpperCamelCase				)
          lowerCamelCase__           =		(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape							,					_UpperCamelCase				)


    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,					dtype=np.intaa				)
          lowerCamelCase__           =		shift_tokens_right(_UpperCamelCase							,					1							,					2				)
          lowerCamelCase__           =		np.equal(_UpperCamelCase							,					1				).astype(np.floataa				).sum()
          lowerCamelCase__           =		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   _a      (      __lowerCAmelCase		,		unittest.TestCase		,		__lowerCAmelCase ):
    a_    :				List[Any]             =    True
    a_    :				Union[str, Any]             =    (
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    a_    :				Optional[Any]             =    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		FlaxBlenderbotModelTester(self				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		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							:					Optional[Any]				):
          lowerCamelCase__           =		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							:					List[str]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__				):
                      lowerCamelCase__           =		self._prepare_for_class(_UpperCamelCase							,					_UpperCamelCase				)
                      lowerCamelCase__           =		model_class(_UpperCamelCase				)

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

                      with self.subTest('JIT Enabled'				):
                            lowerCamelCase__           =		encode_jitted(**_UpperCamelCase				).to_tuple()

                      with self.subTest('JIT Disabled'				):
                            with jax.disable_jit():
                                  lowerCamelCase__           =		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]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__				):
                      lowerCamelCase__           =		model_class(_UpperCamelCase				)
                      lowerCamelCase__           =		model.encode(inputs_dict['input_ids']							,					inputs_dict['attention_mask']				)

                      lowerCamelCase__           =		{
                          """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(SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
                            return model.decode(
                                decoder_input_ids=_UpperCamelCase							,					decoder_attention_mask=_UpperCamelCase							,					encoder_outputs=_UpperCamelCase							,					)

                      with self.subTest('JIT Enabled'				):
                            lowerCamelCase__           =		decode_jitted(**_UpperCamelCase				).to_tuple()

                      with self.subTest('JIT Disabled'				):
                            with jax.disable_jit():
                                  lowerCamelCase__           =		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							:					str				):


          for model_class_name in self.all_model_classes:
                lowerCamelCase__           =		model_class_name.from_pretrained('facebook/blenderbot-400M-distill'				)
                # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                lowerCamelCase__           =		np.ones((1, 1)				) * model.config.eos_token_id
                lowerCamelCase__           =		model(_UpperCamelCase				)
                self.assertIsNotNone(_UpperCamelCase				)


    @unittest.skipUnless(jax_device != 'cpu'							,					'3B test too slow on CPU.'				)
    @slow
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
          lowerCamelCase__           =		{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

          lowerCamelCase__           =		FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'							,					from_pt=_UpperCamelCase				)
          lowerCamelCase__           =		BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'				)

          lowerCamelCase__           =		["""Sam"""]
          lowerCamelCase__           =		tokenizer(_UpperCamelCase							,					return_tensors='jax'				)

          lowerCamelCase__           =		model.generate(**_UpperCamelCase							,					**_UpperCamelCase				)
          lowerCamelCase__           =		"""Sam is a great name. It means \"sun\" in Gaelic."""

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







"""simple docstring"""


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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

_snake_case          =							"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		randn_tensor(SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          else:
                if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          if self.device != torch.device('meta'				) or not hasattr(self.image_encoder							,					'_hf_hook'				):
                return self.device
          for module in self.image_encoder.modules():
                if (
                    hasattr(SCREAMING_SNAKE_CASE__							,					'_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      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

          if not isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.image_encoder(SCREAMING_SNAKE_CASE__				)['last_hidden_state']
          lowerCamelCase__           =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # 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
                lowerCamelCase__           =		torch.cat([negative_image_embeds, image_embeds]				)

          return image_embeds


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          else:
                raise ValueError(
                    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,					image_embeds.dtype							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					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
          lowerCamelCase__           =		latents.reshape(latents.shape[0]							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

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

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

                lowerCamelCase__           =		self.scheduler.step(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

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

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[self.numpy_to_pil(SCREAMING_SNAKE_CASE__				) 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=SCREAMING_SNAKE_CASE__				)



