
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
 return ConvertCommand(
     args.model_type    ,		args.tf_checkpoint    ,		args.pytorch_dump_output    ,		args.config    ,		args.finetuning_task_name							)


__magic_name__            =  '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
       snake_case__						=				parser.add_parser(
           "convert"		,	help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."		,	)
       train_parser.add_argument("--model_type"		,	type=lowerCamelCase		,	required=lowerCamelCase		,	help="Model's type." )
       train_parser.add_argument(
           "--tf_checkpoint"		,	type=lowerCamelCase		,	required=lowerCamelCase		,	help="TensorFlow checkpoint path or folder." )
       train_parser.add_argument(
           "--pytorch_dump_output"		,	type=lowerCamelCase		,	required=lowerCamelCase		,	help="Path to the PyTorch saved model output." )
       train_parser.add_argument("--config"		,	type=lowerCamelCase		,	default=""		,	help="Configuration file path or folder." )
       train_parser.add_argument(
           "--finetuning_task_name"		,	type=lowerCamelCase		,	default=lowerCamelCase		,	help="Optional fine-tuning task name if the TF model was a finetuned model."		,	)
       train_parser.set_defaults(func=lowerCamelCase )
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	*lowerCamelCase		,	):
       snake_case__						=				logging.get_logger("transformers-cli/converting" )

       self._logger.info(F"""Loading model {model_type}""" )
       snake_case__						=				model_type
       snake_case__						=				tf_checkpoint
       snake_case__						=				pytorch_dump_output
       snake_case__						=				config
       snake_case__						=				finetuning_task_name



      def A_			(			self ):
       if self._model_type == "albert":
        try:
         from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
             convert_tf_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "bert":
        try:
         from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
             convert_tf_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "funnel":
        try:
         from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
             convert_tf_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "t5":
        try:
         from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "gpt":
        from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
            convert_openai_checkpoint_to_pytorch,
        )

        convert_openai_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "transfo_xl":
        try:
         from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
             convert_transfo_xl_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        if "ckpt" in self._tf_checkpoint.lower():
         snake_case__						=				self._tf_checkpoint
         snake_case__						=				""
        else:
         snake_case__						=				self._tf_checkpoint
         snake_case__						=				""
        convert_transfo_xl_checkpoint_to_pytorch(
            lowerCamelCase		,	self._config		,	self._pytorch_dump_output		,	lowerCamelCase )
       elif self._model_type == "gpt2":
        try:
         from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
             convert_gpta_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       elif self._model_type == "xlnet":
        try:
         from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
             convert_xlnet_checkpoint_to_pytorch,
         )
        except ImportError:
         raise ImportError(lowerCamelCase )

        convert_xlnet_checkpoint_to_pytorch(
            self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output		,	self._finetuning_task_name )
       elif self._model_type == "xlm":
        from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
            convert_xlm_checkpoint_to_pytorch,
        )

        convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint		,	self._pytorch_dump_output )
       elif self._model_type == "lxmert":
        from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
            convert_lxmert_checkpoint_to_pytorch,
        )

        convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint		,	self._pytorch_dump_output )
       elif self._model_type == "rembert":
        from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
            convert_rembert_tf_checkpoint_to_pytorch,
        )

        convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,	self._config		,	self._pytorch_dump_output )
       else:
        raise ValueError(
            "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__magic_name__            =  8





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x * 255).int().clamp(0    ,		255							)

    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c h w -> b c 1 h w"							)

    snake_case__						=				((x & mask) != 0).float()
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c d h w -> b (c d) h w"							)
    snake_case__						=				bits * 2 - 1
    return bits





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x > 0).int()
    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase    ,		dtype=torch.intaa							)

    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b (c d) h w -> b c d h w"    ,		d=8							)
    snake_case__						=				reduce(x * mask    ,		"b c d h w -> b c h w"    ,		"sum"							)
    return (dec / 255).clamp(0.0    ,		1.0							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = 0.0    ,		__lowerCAmelCase = True    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"							)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    snake_case__						=				timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[timestep]
    snake_case__						=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    snake_case__						=				1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						=				eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        snake_case__						=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        snake_case__						=				model_output.device if torch.is_tensor(__lowerCAmelCase							) else "cpu"
        snake_case__						=				torch.randn(model_output.shape    ,		dtype=model_output.dtype    ,		generator=__lowerCAmelCase							).to(__lowerCAmelCase							)
        snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							) ** 0.5 * eta * noise

        snake_case__						=				prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="epsilon"    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    snake_case__						=				timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        snake_case__						,					snake_case__						=				torch.split(__lowerCAmelCase    ,		sample.shape[1]    ,		dim=1							)
    else:
        snake_case__						=				None

    # 1. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[t]
    snake_case__						=				self.alphas_cumprod[t - 1] if t > 0 else self.one
    snake_case__						=				1 - alpha_prod_t
    snake_case__						=				1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        snake_case__						=				model_output
    else:
        raise ValueError(F"""Unsupported prediction_type {prediction_type}."""							)

    # 3. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    snake_case__						=				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    snake_case__						=				0
    if t > 0:
        snake_case__						=				torch.randn(
            model_output.size()    ,		dtype=model_output.dtype    ,		layout=model_output.layout    ,		generator=__lowerCAmelCase							).to(model_output.device							)
        snake_case__						=				(self._get_variance(__lowerCAmelCase    ,		predicted_variance=__lowerCAmelCase							) ** 0.5) * noise

    snake_case__						=				pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = 1.0		,	):
          super().__init__()
          snake_case__						=				bit_scale
          snake_case__						=				(
              ddim_bit_scheduler_step if isinstance(lowerCamelCase		,	lowerCamelCase ) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=lowerCamelCase		,	scheduler=lowerCamelCase )



      @torch.no_grad()
      def __call__(			self		,	lowerCamelCase = 2_56		,	lowerCamelCase = 2_56		,	lowerCamelCase = 50		,	lowerCamelCase = None		,	lowerCamelCase = 1		,	lowerCamelCase = "pil"		,	lowerCamelCase = True		,	**lowerCamelCase		,	):
          snake_case__						=				torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)		,	generator=lowerCamelCase		,	)
          snake_case__						=				decimal_to_bits(lowerCamelCase ) * self.bit_scale
          snake_case__						=				latents.to(self.device )

          self.scheduler.set_timesteps(lowerCamelCase )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # predict the noise residual
              snake_case__						=				self.unet(lowerCamelCase		,	lowerCamelCase ).sample

              # compute the previous noisy sample x_t -> x_t-1
              snake_case__						=				self.scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ).prev_sample

          snake_case__						=				bits_to_decimal(lowerCamelCase )

          if output_type == "pil":
              snake_case__						=				self.numpy_to_pil(lowerCamelCase )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase )



