

'''simple docstring'''







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					)    ->   int:





           '''simple docstring'''
           super().__init__()
           self.register_modules(
               vae=UpperCamelCase__		,					text_encoder=UpperCamelCase__		,					tokenizer=UpperCamelCase__		,					unet=UpperCamelCase__		,					scheduler=UpperCamelCase__		,					safety_checker=UpperCamelCase__		,					feature_extractor=UpperCamelCase__		,					)



    def        snake_case_      (       self		,					UpperCamelCase__ = "auto"							)    ->   List[str]:





           '''simple docstring'''
           if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  A_						       =   self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           self.enable_attention_slicing(UpperCamelCase__							)







    @torch.no_grad()
    def __call__(       self		,					UpperCamelCase__		,					UpperCamelCase__ = 512		,					UpperCamelCase__ = 512		,					UpperCamelCase__ = 50		,					UpperCamelCase__ = 7.5		,					UpperCamelCase__ = None		,					UpperCamelCase__ = 1		,					UpperCamelCase__ = 0.0		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = "pil"		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = 1		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''

           if isinstance(UpperCamelCase__		,					UpperCamelCase__							):
                  A_						       =   1
           elif isinstance(UpperCamelCase__		,					UpperCamelCase__							):
                  A_						       =   len(UpperCamelCase__							)
           else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__							)}'''							)

           if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(UpperCamelCase__		,					UpperCamelCase__							) or callback_steps <= 0)
           ):
                  raise ValueError(
                      f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                      f''' {type(UpperCamelCase__							)}.'''							)

           # get prompt text embeddings
           A_						       =   self.tokenizer(
               UpperCamelCase__		,					padding="""max_length"""		,					max_length=self.tokenizer.model_max_length		,					return_tensors="""pt"""		,					)
           A_						       =   text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  A_						       =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
                  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_						       =   text_input_ids[:, : self.tokenizer.model_max_length]

           if text_embeddings is None:
                  A_						       =   self.text_encoder(text_input_ids.to(self.device							)							)[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           A_       ,      A_       ,      A_						       =   text_embeddings.shape
           A_						       =   text_embeddings.repeat(1		,					UpperCamelCase__		,					1							)
           A_						       =   text_embeddings.view(bs_embed * num_images_per_prompt		,					UpperCamelCase__		,					-1							)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           A_						       =   guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                  A_						       =   42
                  if negative_prompt is None:
                         A_						       =   [""""""]
                  elif type(UpperCamelCase__							) is not type(UpperCamelCase__							):
                         raise TypeError(
                             f'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__							)} !='''
                             f''' {type(UpperCamelCase__							)}.'''							)
                  elif isinstance(UpperCamelCase__		,					UpperCamelCase__							):
                         A_						       =   [negative_prompt]
                  elif batch_size != len(UpperCamelCase__							):
                         raise ValueError(
                             f'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__							)}, but `prompt`:'''
                             f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                             """ the batch size of `prompt`."""							)
                  else:
                         A_						       =   negative_prompt

                  A_						       =   text_input_ids.shape[-1]
                  A_						       =   self.tokenizer(
                      UpperCamelCase__		,					padding="""max_length"""		,					max_length=UpperCamelCase__		,					truncation=UpperCamelCase__		,					return_tensors="""pt"""		,					)
                  A_						       =   self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

                  # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                  A_						       =   uncond_embeddings.shape[1]
                  A_						       =   uncond_embeddings.repeat(UpperCamelCase__		,					UpperCamelCase__		,					1							)
                  A_						       =   uncond_embeddings.view(batch_size * num_images_per_prompt		,					UpperCamelCase__		,					-1							)

                  # 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_						       =   torch.cat([uncond_embeddings, text_embeddings]							)

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           A_						       =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           A_						       =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
           A_						       =   text_embeddings.dtype
           if latents is None:
                  if self.device.type == "mps":
                         # randn does not exist on mps
                         A_						       =   torch.randn(
                             UpperCamelCase__		,					generator=UpperCamelCase__		,					device="""cpu"""		,					dtype=UpperCamelCase__							).to(self.device							)
                         A_						       =   torch.randn(UpperCamelCase__		,					generator=UpperCamelCase__		,					device="""cpu"""		,					dtype=UpperCamelCase__							).to(
                             self.device							)
                  else:
                         A_						       =   torch.randn(
                             UpperCamelCase__		,					generator=UpperCamelCase__		,					device=self.device		,					dtype=UpperCamelCase__							)
                         A_						       =   torch.randn(UpperCamelCase__		,					generator=UpperCamelCase__		,					device=self.device		,					dtype=UpperCamelCase__							)
           else:
                  if latents_reference.shape != latents_shape:
                         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
                  A_						       =   latents_reference.to(self.device							)
                  A_						       =   latents.to(self.device							)

           # This is the key part of the pipeline where we
           # try to ensure that the generated images w/ the same seed
           # but different sizes actually result in similar images
           A_						       =   (latents_shape[3] - latents_shape_reference[3]) // 2
           A_						       =   (latents_shape[2] - latents_shape_reference[2]) // 2
           A_						       =   latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
           A_						       =   latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
           A_						       =   0 if dx < 0 else dx
           A_						       =   0 if dy < 0 else dy
           A_						       =   max(-dx		,					0							)
           A_						       =   max(-dy		,					0							)
           # import pdb
           # pdb.set_trace()
           A_						       =   latents_reference[:, :, dy : dy + h, dx : dx + w]

           # set timesteps
           self.scheduler.set_timesteps(UpperCamelCase__							)

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           A_						       =   self.scheduler.timesteps.to(self.device							)

           # scale the initial noise by the standard deviation required by the scheduler
           A_						       =   latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           A_						       =   """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
           A_						       =   {}
           if accepts_eta:
                  A_						       =   eta

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

                  # predict the noise residual
                  A_						       =   self.unet(UpperCamelCase__		,					UpperCamelCase__		,					encoder_hidden_states=UpperCamelCase__							).sample

                  # perform guidance
                  if do_classifier_free_guidance:
                         A_       ,      A_						       =   noise_pred.chunk(2							)
                         A_						       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # compute the previous noisy sample x_t -> x_t-1
                  A_						       =   self.scheduler.step(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							).prev_sample

                  # call the callback, if provided
                  if callback is not None and i % callback_steps == 0:
                         callback(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)

           A_						       =   1 / 0.18215 * latents
           A_						       =   self.vae.decode(UpperCamelCase__							).sample

           A_						       =   (image / 2 + 0.5).clamp(0		,					1							)

           # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
           A_						       =   image.cpu().permute(0		,					2		,					3		,					1							).float().numpy()

           if self.safety_checker is not None:
                  A_						       =   self.feature_extractor(self.numpy_to_pil(UpperCamelCase__							)		,					return_tensors="""pt"""							).to(
                      self.device							)
                  A_       ,      A_						       =   self.safety_checker(
                      images=UpperCamelCase__		,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
           else:
                  A_						       =   None

           if output_type == "pil":
                  A_						       =   self.numpy_to_pil(UpperCamelCase__							)

           if not return_dict:
                  return (image, has_nsfw_concept)

           return StableDiffusionPipelineOutput(images=UpperCamelCase__		,					nsfw_content_detected=UpperCamelCase__							)



'''simple docstring'''







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__="attention"					)      ->      Dict:
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Optional[Any]:
       if split_mlp_wi:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              A_						       =   (wi_a, wi_a)
       else:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       A_						       =   params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def    UpperCAmelCase__     (   UpperCAmelCase__,							*, UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   traverse_util.flatten_dict(variables["""target"""]					)
       A_						       =   {"""/""".join(UpperCAmelCase__					): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       A_						       =   """encoder/layers_0/mlp/wi_0/kernel""" in old
       print("""Split MLP:""",							UpperCAmelCase__					)

       A_						       =   collections.OrderedDict()

       # Shared embeddings.
       A_						       =   old["""token_embedder/embedding"""]

       # Encoder.
       for i in range(UpperCAmelCase__					):
              # Block i, layer 0 (Self Attention).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_attention_layer_norm"""					)
              A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""attention"""					)
              A_						       =   layer_norm
              A_						       =   k.T
              A_						       =   o.T
              A_						       =   q.T
              A_						       =   v.T

              # Block i, layer 1 (MLP).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_mlp_layer_norm"""					)
              A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							UpperCAmelCase__					)
              A_						       =   layer_norm
              if split_mlp_wi:
                     A_						       =   wi[0].T
                     A_						       =   wi[1].T
              else:
                     A_						       =   wi.T
              A_						       =   wo.T

       A_						       =   old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       A_						       =   old["""encoder/encoder_norm/scale"""]

       if not is_encoder_only:
              # Decoder.
              for i in range(UpperCAmelCase__					):
                     # Block i, layer 0 (Self Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_self_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""self_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 1 (Cross Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_cross_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""encoder_decoder_attention"""					)
                     A_						       =   layer_norm
                     A_						       =   k.T
                     A_						       =   o.T
                     A_						       =   q.T
                     A_						       =   v.T

                     # Block i, layer 2 (MLP).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_mlp_layer_norm"""					)
                     A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							UpperCAmelCase__					)
                     A_						       =   layer_norm
                     if split_mlp_wi:
                            A_						       =   wi[0].T
                            A_						       =   wi[1].T
                     else:
                            A_						       =   wi.T
                     A_						       =   wo.T

              A_						       =   old["""decoder/decoder_norm/scale"""]
              A_						       =   old[
                  """decoder/relpos_bias/rel_embedding"""
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     A_						       =   old["""decoder/logits_dense/kernel"""].T

       return new
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              A_						       =   state_dict["""shared.weight"""]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     A_						       =   state_dict["""shared.weight"""]

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print("""Using shared word embeddings as lm_head."""					)
                     A_						       =   state_dict["""shared.weight"""]

       return state_dict
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
       A_						       =   convert_tax_to_pytorch(UpperCAmelCase__,							num_layers=config.num_layers,							is_encoder_only=UpperCAmelCase__					)
       A_						       =   make_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)
       model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      List[Any]:
       A_						       =   TaConfig.from_json_file(UpperCAmelCase__					)
       print(F'''Building PyTorch model from configuration: {config}'''					)
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              A_						       =   TaEncoderModel(UpperCAmelCase__					)
       else:
              A_						       =   TaForConditionalGeneration(UpperCAmelCase__					)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
       model.save_pretrained(UpperCAmelCase__					)

       # Verify that we can load the checkpoint.
       model.from_pretrained(UpperCAmelCase__					)
       print("""Done"""					)


if __name__ == "__main__":
      __lowerCamelCase									=     argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
      # Required parameters
      parser.add_argument(
          '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
      )
      __lowerCamelCase									=     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
