"""simple docstring"""




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
	import torch





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=1_4		,     lowercase=7		,     lowercase=True		,     lowercase=True		,     lowercase=False		,     lowercase=True		,     lowercase=9_9		,     lowercase=3_2		,     lowercase=4		,     lowercase=4		,     lowercase=4		,     lowercase=3_7		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=0.02		,     ):



						"""simple docstring"""





						A_		:    Dict         =				parent
						A_		:    Union[str, Any]         =				batch_size
						A_		:    Tuple         =				seq_length
						A_		:    Tuple         =				is_training
						A_		:    Optional[Any]         =				use_input_mask
						A_		:    Any         =				use_token_type_ids
						A_		:    List[str]         =				use_labels
						A_		:    List[str]         =				vocab_size
						A_		:    int         =				hidden_size
						A_		:    Any         =				rotary_dim
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Dict         =				num_attention_heads
						A_		:    List[Any]         =				intermediate_size
						A_		:    List[str]         =				hidden_act
						A_		:    Dict         =				hidden_dropout_prob
						A_		:    int         =				attention_probs_dropout_prob
						A_		:    str         =				max_position_embeddings
						A_		:    Optional[int]         =				initializer_range
						A_		:    Tuple         =				None
						A_		:    Optional[Any]         =				vocab_size - 1
						A_		:    Any         =				vocab_size - 1
						A_		:    Union[str, Any]         =				vocab_size - 1
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size )

						A_		:    int         =				None
						if self.use_input_mask:
								A_		:    Optional[Any]         =				random_attention_mask([self.batch_size, self.seq_length] )

						A_		:    List[str]         =				GPTJConfig(
						    vocab_size=self.vocab_size		,     n_embd=self.hidden_size		,     n_layer=self.num_hidden_layers		,     n_head=self.num_attention_heads		,     n_positions=self.max_position_embeddings		,     use_cache=lowercase		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     rotary_dim=self.rotary_dim		,     )

						return (config, input_ids, input_mask)
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.prepare_config_and_inputs()
						A_		:    List[Any]         =				config_and_inputs
						A_		:    List[str]         =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
						return config, inputs_dict
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				2_0
						A_		:    int         =				model_class_name(lowercase )

						A_		:    List[Any]         =				model.init_cache(input_ids.shape[0]		,     lowercase )
						A_		:    Tuple         =				jnp.ones((input_ids.shape[0], max_decoder_length)		,     dtype='i4' )

						A_		:    List[Any]         =				jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,     (input_ids.shape[0], input_ids.shape[-1] - 1) )
						A_		:    Tuple         =				model(
						    input_ids[:, :-1]		,     attention_mask=lowercase		,     past_key_values=lowercase		,     position_ids=lowercase		,     )

						A_		:    Any         =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype='i4' )
						A_		:    Tuple         =				model(
						    input_ids[:, -1:]		,     attention_mask=lowercase		,     past_key_values=outputs_cache.past_key_values		,     position_ids=lowercase		,     )

						A_		:    Tuple         =				model(lowercase )

						A_		:    Tuple         =				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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				2_0
						A_		:    List[str]         =				model_class_name(lowercase )

						A_		:    int         =				jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]		,     axis=-1		,     )

						A_		:    Any         =				model.init_cache(input_ids.shape[0]		,     lowercase )
						A_		:    List[str]         =				jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,     (input_ids.shape[0], input_ids.shape[-1] - 1) )

						A_		:    int         =				model(
						    input_ids[:, :-1]		,     attention_mask=lowercase		,     past_key_values=lowercase		,     position_ids=lowercase		,     )
						A_		:    str         =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype='i4' )
						A_		:    Optional[Any]         =				model(
						    input_ids[:, -1:]		,     past_key_values=outputs_cache.past_key_values		,     attention_mask=lowercase		,     position_ids=lowercase		,     )

						A_		:    str         =				model(lowercase		,     attention_mask=lowercase )

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





@require_flax
class UpperCAmelCase						( __lowercase ,	__lowercase ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				lowerCamelCase_    = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				FlaxGPTJModelTester(self )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_class_name in self.all_model_classes:
								A_		:    int         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(lowercase		,     lowercase		,     lowercase		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_class_name in self.all_model_classes:
								A_		:    Optional[int]         =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    lowercase		,     lowercase		,     lowercase		,     lowercase )
				@tooslow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				GPTaTokenizer.from_pretrained('gpt2'		,     pad_token='<|endoftext|>'		,     padding_side='left' )
						A_		:    Dict         =				tokenizer(['Hello this is a long string', 'Hey']		,     return_tensors='np'		,     padding=lowercase		,     truncation=lowercase )

						A_		:    Union[str, Any]         =				FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
						A_		:    str         =				False
						A_		:    Optional[Any]         =				model.config.eos_token_id

						A_		:    Optional[Any]         =				jax.jit(model.generate )

						A_		:    Any         =				jit_generate(
						    inputs['input_ids']		,     attention_mask=inputs['attention_mask']		,     pad_token_id=tokenizer.pad_token_id ).sequences

						A_		:    Optional[int]         =				tokenizer.batch_decode(lowercase		,     skip_special_tokens=lowercase )

						A_		:    int         =				[
						    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
						    """Hey, I'm a little late to the party. I'm going to""",
						]

						self.assertListEqual(lowercase		,     lowercase )
				@is_pt_flax_cross_test
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__ ):
										# prepare inputs
										A_		:    Union[str, Any]         =				self._prepare_for_class(lowercase		,     lowercase )
										A_		:    List[Any]         =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										A_		:    Optional[Any]         =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
										A_		:    int         =				getattr(lowercase		,     lowercase )

										A_		:    Dict         =				pt_inputs["""input_ids"""].shape
										A_		:    str         =				np.random.randint(0		,     seq_length - 1		,     size=(batch_size,) )
										for batch_idx, start_index in enumerate(lowercase ):
												A_		:    Tuple         =				0
												A_		:    Union[str, Any]         =				1
												A_		:    Any         =				0
												A_		:    int         =				1
										A_		:    Optional[Any]         =				pt_model_class(lowercase ).eval()
										A_		:    List[str]         =				model_class(lowercase		,     dtype=jnp.floataa )

										A_		:    Optional[int]         =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,     lowercase )
										A_		:    Tuple         =				fx_state

										with torch.no_grad():
												A_		:    Tuple         =				pt_model(**lowercase ).to_tuple()

										A_		:    Dict         =				fx_model(**lowercase ).to_tuple()
										self.assertEqual(len(lowercase )		,     len(lowercase )		,     'Output lengths differ between Flax and PyTorch' )
										for fx_output, pt_output in zip(lowercase		,     lowercase ):
												self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4E-2 )

										with tempfile.TemporaryDirectory() as tmpdirname:
												pt_model.save_pretrained(lowercase )
												A_		:    int         =				model_class.from_pretrained(lowercase		,     from_pt=lowercase )

										A_		:    Tuple         =				fx_model_loaded(**lowercase ).to_tuple()
										self.assertEqual(
										    len(lowercase )		,     len(lowercase )		,     'Output lengths differ between Flax and PyTorch' )
										for fx_output_loaded, pt_output in zip(lowercase		,     lowercase ):
												self.assert_almost_equals(fx_output_loaded[:, -1]		,     pt_output[:, -1].numpy()		,     4E-2 )
				@is_pt_flax_cross_test
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__ ):
										# prepare inputs
										A_		:    Any         =				self._prepare_for_class(lowercase		,     lowercase )
										A_		:    Tuple         =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										A_		:    Optional[Any]         =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
										A_		:    int         =				getattr(lowercase		,     lowercase )

										A_		:    Tuple         =				pt_model_class(lowercase ).eval()
										A_		:    Union[str, Any]         =				model_class(lowercase		,     dtype=jnp.floataa )

										A_		:    Union[str, Any]         =				load_flax_weights_in_pytorch_model(lowercase		,     fx_model.params )
										A_		:    Any         =				pt_inputs["""input_ids"""].shape
										A_		:    str         =				np.random.randint(0		,     seq_length - 1		,     size=(batch_size,) )
										for batch_idx, start_index in enumerate(lowercase ):
												A_		:    Dict         =				0
												A_		:    str         =				1
												A_		:    Tuple         =				0
												A_		:    Union[str, Any]         =				1

										# make sure weights are tied in PyTorch
										pt_model.tie_weights()

										with torch.no_grad():
												A_		:    str         =				pt_model(**lowercase ).to_tuple()

										A_		:    List[Any]         =				fx_model(**lowercase ).to_tuple()
										self.assertEqual(len(lowercase )		,     len(lowercase )		,     'Output lengths differ between Flax and PyTorch' )
										for fx_output, pt_output in zip(lowercase		,     lowercase ):
												self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4E-2 )

										with tempfile.TemporaryDirectory() as tmpdirname:
												fx_model.save_pretrained(lowercase )
												A_		:    Optional[int]         =				pt_model_class.from_pretrained(lowercase		,     from_flax=lowercase )

										with torch.no_grad():
												A_		:    str         =				pt_model_loaded(**lowercase ).to_tuple()

										self.assertEqual(
										    len(lowercase )		,     len(lowercase )		,     'Output lengths differ between Flax and PyTorch' )
										for fx_output, pt_output in zip(lowercase		,     lowercase ):
												self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4E-2 )







				@tooslow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_class_name in self.all_model_classes:
								A_		:    Any         =				model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
								A_		:    List[Any]         =				model(np.ones((1, 1) ) )
								self.assertIsNotNone(lowercase )







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


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

_UpperCAmelCase            =		"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

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

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

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

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""





@dataclass
class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = 42





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





						super().__init__()

						self.register_modules(
						    prior=lowercase		,     image_encoder=lowercase		,     image_processor=lowercase		,     scheduler=lowercase		,     renderer=lowercase		,     )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						if latents is None:
								A_		:    Optional[Any]         =				randn_tensor(lowercase		,     generator=lowercase		,     device=lowercase		,     dtype=lowercase )
						else:
								if latents.shape != shape:
										raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
								A_		:    Optional[int]         =				latents.to(lowercase )

						A_		:    List[Any]         =				latents * scheduler.init_noise_sigma
						return latents
				def   lowerCAmelCase_       (						self		,     lowercase=0 ):



						"""simple docstring"""





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

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

						A_		:    Dict         =				[self.image_encoder, self.prior]
						for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
										cpu_offload(lowercase		,     lowercase )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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(lowercase		,     '_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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





						if isinstance(lowercase		,     lowercase ) and isinstance(image[0]		,     torch.Tensor ):
								A_		:    Tuple         =				torch.cat(lowercase		,     axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase		,     axis=0 )

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

						A_		:    List[str]         =				image.to(dtype=self.image_encoder.dtype		,     device=lowercase )

						A_		:    Tuple         =				self.image_encoder(lowercase )['last_hidden_state']
						A_		:    Dict         =				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

						A_		:    List[str]         =				image_embeds.repeat_interleave(lowercase		,     dim=0 )

						if do_classifier_free_guidance:
								A_		:    str         =				torch.zeros_like(lowercase )

								# 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_		:    Optional[Any]         =				torch.cat([negative_image_embeds, image_embeds] )

						return image_embeds







				@torch.no_grad()
				@replace_example_docstring(lowercase )
				def __call__(						self		,     lowercase		,     lowercase = 1		,     lowercase = 2_5		,     lowercase = None		,     lowercase = None		,     lowercase = 4.0		,     lowercase = 6_4		,     lowercase = "pil"		,     lowercase = True		,     ):



						"""simple docstring"""





						if isinstance(lowercase		,     PIL.Image.Image ):
								A_		:    int         =				1
						elif isinstance(lowercase		,     torch.Tensor ):
								A_		:    int         =				image.shape[0]
						elif isinstance(lowercase		,     lowercase ) and isinstance(image[0]		,     (torch.Tensor, PIL.Image.Image) ):
								A_		:    List[str]         =				len(lowercase )
						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(lowercase )}''' )

						A_		:    Any         =				self._execution_device

						A_		:    List[Any]         =				batch_size * num_images_per_prompt

						A_		:    int         =				guidance_scale > 1.0
						A_		:    Optional[int]         =				self._encode_image(lowercase		,     lowercase		,     lowercase		,     lowercase )

						# prior

						self.scheduler.set_timesteps(lowercase		,     device=lowercase )
						A_		:    Dict         =				self.scheduler.timesteps

						A_		:    int         =				self.prior.config.num_embeddings
						A_		:    int         =				self.prior.config.embedding_dim

						A_		:    Dict         =				self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     lowercase		,     lowercase		,     lowercase		,     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_		:    Union[str, Any]         =				latents.reshape(latents.shape[0]		,     lowercase		,     lowercase )

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

								A_		:    Any         =				self.prior(
								    lowercase		,     timestep=lowercase		,     proj_embedding=lowercase		,     ).predicted_image_embedding

								# remove the variance
								A_       ,      A_		:    int         =				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_		:    List[Any]         =				noise_pred.chunk(2 )
										A_		:    str         =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

								A_		:    Optional[int]         =				self.scheduler.step(
								    lowercase		,     timestep=lowercase		,     sample=lowercase		,     ).prev_sample

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

						A_		:    str         =				[]
						for i, latent in enumerate(lowercase ):
								print()
								A_		:    Optional[Any]         =				self.renderer.decode(
								    latent[None, :]		,     lowercase		,     size=lowercase		,     ray_batch_size=4_0_9_6		,     n_coarse_samples=6_4		,     n_fine_samples=1_2_8		,     )

								images.append(lowercase )

						A_		:    Dict         =				torch.stack(lowercase )

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

						if output_type == "pil":
								A_		:    str         =				[self.numpy_to_pil(lowercase ) 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=lowercase )






