"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase            =		["""gpt2"""]
_UpperCAmelCase            =		"""gpt2"""

if is_tf_available():





	class UpperCAmelCase						( tf.Module  ):


					'''simple docstring'''






					def __init__(						self		,     lowercase ):



							"""simple docstring"""





							super().__init__()
							A_		:    Any         =				tokenizer
							A_		:    Optional[Any]         =				AutoConfig.from_pretrained(lowercase )
							A_		:    int         =				TFGPTaLMHeadModel.from_config(lowercase )







					@tf.function(input_signature=(tf.TensorSpec((None,)		,     tf.string		,     name='text' ),) )
					def   lowerCAmelCase_       (						self		,     lowercase ):



							"""simple docstring"""





							A_		:    List[str]         =				self.tokenizer(lowercase )
							A_		:    Tuple         =				tokenized['input_ids'].to_tensor()

							A_		:    Optional[Any]         =				tf.cast(input_ids_dense > 0		,     tf.intaa )
							# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

							A_		:    List[Any]         =				self.model(input_ids=lowercase		,     attention_mask=lowercase )['logits']

							return outputs





@require_tf
@require_keras_nlp
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().setUp()

						A_		:    Any         =				[GPTaTokenizer.from_pretrained(lowercase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						A_		:    Any         =				[TFGPTaTokenizer.from_pretrained(lowercase ) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers ) == len(self.tf_tokenizers )

						A_		:    Union[str, Any]         =				[
						    'This is a straightforward English test sentence.',
						    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
						    'Now we\'re going to add some Chinese: 一 二 三 一二三',
						    'And some much more rare Chinese: 齉 堃 齉堃',
						    'Je vais aussi écrire en français pour tester les accents',
						    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
						]
						A_		:    Tuple         =				list(zip(self.test_sentences		,     self.test_sentences[::-1] ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tokenizer, tf_tokenizer in zip(self.tokenizers		,     self.tf_tokenizers ):
								for test_inputs in self.test_sentences:
										A_		:    List[Any]         =				tokenizer([test_inputs]		,     return_tensors='tf' )
										A_		:    str         =				tf_tokenizer([test_inputs] )

										for key in python_outputs.keys():
												# convert them to numpy to avoid messing with ragged tensors
												A_		:    Optional[int]         =				python_outputs[key].numpy()
												A_		:    Optional[int]         =				tf_outputs[key].numpy()

												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
												self.assertTrue(tf.reduce_all(tf.cast(lowercase		,     tf.intaa ) == tf_outputs_values ) )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Any         =				tf.function(lowercase )
								for test_inputs in self.test_sentences:
										A_		:    Optional[int]         =				tf.constant(lowercase )
										A_		:    Tuple         =				compiled_tokenizer(lowercase )
										A_		:    Tuple         =				tf_tokenizer(lowercase )

										for key in eager_outputs.keys():
												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Any         =				ModelToSave(tokenizer=lowercase )
								A_		:    int         =				tf.convert_to_tensor([self.test_sentences[0]] )
								A_		:    Dict         =				model.serving(lowercase )  # Build model with some sample inputs
								with TemporaryDirectory() as tempdir:
										A_		:    Dict         =				Path(lowercase ) / 'saved.model'
										tf.saved_model.save(lowercase		,     lowercase		,     signatures={'serving_default': model.serving} )
										A_		:    List[Any]         =				tf.saved_model.load(lowercase )
								A_		:    List[str]         =				loaded_model.signatures['serving_default'](lowercase )['output_0']
								# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
								self.assertTrue(tf.reduce_all(out == loaded_output ) )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								A_		:    Dict         =				tf.convert_to_tensor([self.test_sentences[0]] )
								A_		:    List[str]         =				tf_tokenizer(lowercase )  # Build model with some sample inputs

								A_		:    Any         =				tf_tokenizer.get_config()
								A_		:    str         =				TFGPTaTokenizer.from_config(lowercase )
								A_		:    Any         =				model_from_config(lowercase )

								for key in from_config_output.keys():
										self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for tf_tokenizer in self.tf_tokenizers:
								# for the test to run
								A_		:    Dict         =				1_2_3_1_2_3

								for max_length in [3, 5, 1_0_2_4]:
										A_		:    Optional[int]         =				tf.convert_to_tensor([self.test_sentences[0]] )
										A_		:    Any         =				tf_tokenizer(lowercase		,     max_length=lowercase )

										A_		:    Union[str, Any]         =				out['input_ids'].numpy().shape[1]

										assert out_length == max_length







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 )






