






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     def get_masked_lm_array(__snake_case       ):
          __lowerCAmelCase								=					F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

          if "kernel" in name:
               __lowerCAmelCase								=					array.transpose()

          return torch.from_numpy(__snake_case       )

     def get_encoder_array(__snake_case       ):
          __lowerCAmelCase								=					F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

          if "kernel" in name:
               __lowerCAmelCase								=					array.transpose()

          return torch.from_numpy(__snake_case       )

     def get_encoder_layer_array(__snake_case					, __snake_case       ):
          __lowerCAmelCase								=					F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )

          if "kernel" in name:
               __lowerCAmelCase								=					array.transpose()

          return torch.from_numpy(__snake_case       )

     def get_encoder_attention_layer_array(__snake_case					, __snake_case					, __snake_case       ):
          __lowerCAmelCase								=					F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
          __lowerCAmelCase								=					tf.train.load_variable(__snake_case					, __snake_case       )
          __lowerCAmelCase								=					array.reshape(__snake_case       )

          if "kernel" in name:
               __lowerCAmelCase								=					array.transpose()

          return torch.from_numpy(__snake_case       )

     print(F"""Loading model based on config from {config_path}..."""       )
     __lowerCAmelCase								=					BertConfig.from_json_file(__snake_case       )
     __lowerCAmelCase								=					BertForMaskedLM(__snake_case       )

     # Layers
     for layer_index in range(0					, config.num_hidden_layers       ):
          __lowerCAmelCase								=					model.bert.encoder.layer[layer_index]

          # Self-attention
          __lowerCAmelCase								=					layer.attention.self

          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_query_dense/kernel"					, self_attn.query.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_query_dense/bias"					, self_attn.query.bias.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_key_dense/kernel"					, self_attn.key.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_key_dense/bias"					, self_attn.key.bias.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_value_dense/kernel"					, self_attn.value.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_value_dense/bias"					, self_attn.value.bias.data.shape       )

          # Self-attention Output
          __lowerCAmelCase								=					layer.attention.output

          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_output_dense/kernel"					, self_output.dense.weight.data.shape       )
          __lowerCAmelCase								=					get_encoder_attention_layer_array(
              __snake_case					, "_output_dense/bias"					, self_output.dense.bias.data.shape       )

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_attention_layer_norm/gamma"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_attention_layer_norm/beta"       )

          # Intermediate
          __lowerCAmelCase								=					layer.intermediate

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_intermediate_dense/kernel"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_intermediate_dense/bias"       )

          # Output
          __lowerCAmelCase								=					layer.output

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_dense/kernel"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_dense/bias"       )

          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_layer_norm/gamma"       )
          __lowerCAmelCase								=					get_encoder_layer_array(__snake_case					, "_output_layer_norm/beta"       )

     # Embeddings
     __lowerCAmelCase								=					get_encoder_array("_position_embedding_layer/embeddings"       )
     __lowerCAmelCase								=					get_encoder_array("_type_embedding_layer/embeddings"       )
     __lowerCAmelCase								=					get_encoder_array("_embedding_norm_layer/gamma"       )
     __lowerCAmelCase								=					get_encoder_array("_embedding_norm_layer/beta"       )

     # LM Head
     __lowerCAmelCase								=					model.cls.predictions.transform

     __lowerCAmelCase								=					get_masked_lm_array("dense/kernel"       )
     __lowerCAmelCase								=					get_masked_lm_array("dense/bias"       )

     __lowerCAmelCase								=					get_masked_lm_array("layer_norm/gamma"       )
     __lowerCAmelCase								=					get_masked_lm_array("layer_norm/beta"       )

     __lowerCAmelCase								=					get_masked_lm_array("embedding_table"       )

     # Pooling
     __lowerCAmelCase								=					BertPooler(config=__snake_case       )
     __lowerCAmelCase								=					get_encoder_array("_pooler_layer/kernel"       )
     __lowerCAmelCase								=					get_encoder_array("_pooler_layer/bias"       )

     # Export final model
     model.save_pretrained(__snake_case       )

     # Integration test - should load without any errors ;)
     __lowerCAmelCase								=					BertForMaskedLM.from_pretrained(__snake_case       )
     print(new_model.eval()       )

     print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
 lowerCamelCase			:					Union[str, Any]         =	argparse.ArgumentParser()
 parser.add_argument(
     '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
 )
 parser.add_argument(
     '''--bert_config_file''',
     type=str,
     required=True,
     help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     type=str,
     required=True,
     help='''Path to the output PyTorch model.''',
 )
 lowerCamelCase			:					str         =	parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)














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


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

lowerCamelCase			:					str         =	'''
    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_			):
      snake_case_  =							42



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Dict:
           super().__init__()

           self.register_modules(
               prior=__UpperCamelCase					,						image_encoder=__UpperCamelCase					,						image_processor=__UpperCamelCase					,						scheduler=__UpperCamelCase					,						renderer=__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if latents is None:
                __lowerCAmelCase								=					randn_tensor(__UpperCamelCase					,						generator=__UpperCamelCase					,						device=__UpperCamelCase					,						dtype=__UpperCamelCase	)
           else:
                if latents.shape != shape:
                     raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
                __lowerCAmelCase								=					latents.to(__UpperCamelCase	)

           __lowerCAmelCase								=					latents * scheduler.init_noise_sigma
           return latents


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0	)->	str:
           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(__UpperCamelCase					,						__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           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(__UpperCamelCase					,						"_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					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Union[str, Any]:
           if isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						torch.Tensor	):
                __lowerCAmelCase								=					torch.cat(__UpperCamelCase					,						axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCamelCase					,						axis=0	)

           if not isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors="pt"	).pixel_values[0].unsqueeze(0	)

           __lowerCAmelCase								=					image.to(dtype=self.image_encoder.dtype					,						device=__UpperCamelCase	)

           __lowerCAmelCase								=					self.image_encoder(__UpperCamelCase	)["last_hidden_state"]
           __lowerCAmelCase								=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           __lowerCAmelCase								=					image_embeds.repeat_interleave(__UpperCamelCase					,						dim=0	)

           if do_classifier_free_guidance:
                __lowerCAmelCase								=					torch.zeros_like(__UpperCamelCase	)

                # 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(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 2_5					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 4.0					,						__UpperCamelCase = 6_4					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						)->	Dict:

           if isinstance(__UpperCamelCase					,						PIL.Image.Image	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					image.shape[0]
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						(torch.Tensor, PIL.Image.Image)	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           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(__UpperCamelCase	)}"""	)

           __lowerCAmelCase								=					self._execution_device

           __lowerCAmelCase								=					batch_size * num_images_per_prompt

           __lowerCAmelCase								=					guidance_scale > 1.0
           __lowerCAmelCase								=					self._encode_image(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # prior

           self.scheduler.set_timesteps(__UpperCamelCase					,						device=__UpperCamelCase	)
           __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					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						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]					,						__UpperCamelCase					,						__UpperCamelCase	)

           for i, t in enumerate(self.progress_bar(__UpperCamelCase	)	):
                # 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(__UpperCamelCase					,						__UpperCamelCase	)

                __lowerCAmelCase								=					self.prior(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						proj_embedding=__UpperCamelCase					,						).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(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						sample=__UpperCamelCase					,						).prev_sample

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

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

                images.append(__UpperCamelCase	)

           __lowerCAmelCase								=					torch.stack(__UpperCamelCase	)

           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(__UpperCamelCase	) 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=__UpperCamelCase	)






