






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase=0.9_99	,      lowerCAmelCase="cosine"	,      ):







       '''simple docstring'''


       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase		):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase		):
                     return math.exp(t * -12.0		)

       else:
              raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

       UpperCAmelCase	    =			[]
       for i in range(lowerCamelCase__		):
              UpperCAmelCase	    =			i / num_diffusion_timesteps
              UpperCAmelCase	    =			(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(lowerCamelCase__		) / alpha_bar_fn(lowerCamelCase__		)	,      lowerCamelCase__		)		)
       return torch.tensor(lowerCamelCase__	,      dtype=torch.floataa		)





class   UpperCamelCase_ (       a_   ,					a_  ):
  _A       :					Optional[int]				=	[e.name for e in KarrasDiffusionSchedulers]
  _A       :					Dict				=	2



  @register_to_config
  def __init__(      self ,		snake_case__ = 10_00 ,		snake_case__ = 0.00_085 ,		snake_case__ = 0.012 ,		snake_case__ = "linear" ,		snake_case__ = None ,		snake_case__ = "epsilon" ,		snake_case__ = "linspace" ,		snake_case__ = 0 ,		)			->			Union[str, Any]:



         """simple docstring"""





         if trained_betas is not None:
                UpperCAmelCase	    =			torch.tensor(UpperCamelCase_ ,		dtype=torch.floataa				)
         elif beta_schedule == "linear":
                UpperCAmelCase	    =			torch.linspace(UpperCamelCase_ ,		UpperCamelCase_ ,		UpperCamelCase_ ,		dtype=torch.floataa				)
         elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCAmelCase	    =			(
                    torch.linspace(beta_start**0.5 ,		beta_end**0.5 ,		UpperCamelCase_ ,		dtype=torch.floataa				) ** 2
                )
         elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCAmelCase	    =			betas_for_alpha_bar(UpperCamelCase_				)
         else:
                raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''				)

         UpperCAmelCase	    =			1.0 - self.betas
         UpperCAmelCase	    =			torch.cumprod(self.alphas ,		dim=0				)

         #  set all values
         self.set_timesteps(UpperCamelCase_ ,		UpperCamelCase_ ,		UpperCamelCase_				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=None				)			->			Tuple:



         """simple docstring"""





         if schedule_timesteps is None:
                UpperCAmelCase	    =			self.timesteps

         UpperCAmelCase	    =			(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter				) == 0:
                UpperCAmelCase	    =			1 if len(UpperCamelCase_				) > 1 else 0
         else:
                UpperCAmelCase	    =			timestep.cpu().item() if torch.is_tensor(UpperCamelCase_				) else timestep
                UpperCAmelCase	    =			self._index_counter[timestep_int]

         return indices[pos].item()



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.index_for_timestep(UpperCamelCase_				)

         if self.state_in_first_order:
                UpperCAmelCase	    =			self.sigmas[step_index]
         else:
                UpperCAmelCase	    =			self.sigmas_interpol[step_index]

         UpperCAmelCase	    =			sample / ((sigma**2 + 1) ** 0.5)
         return sample



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			num_inference_steps

         UpperCAmelCase	    =			num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
                UpperCAmelCase	    =			np.linspace(0 ,		num_train_timesteps - 1 ,		UpperCamelCase_ ,		dtype=UpperCamelCase_				)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
                UpperCAmelCase	    =			num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase	    =			(np.arange(0 ,		UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_				)
                timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
                UpperCAmelCase	    =			num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase	    =			(np.arange(UpperCamelCase_ ,		0 ,		-step_ratio				)).round().copy().astype(UpperCamelCase_				)
                timesteps -= 1
         else:
                raise ValueError(
                    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''				)

         UpperCAmelCase	    =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
         UpperCAmelCase	    =			torch.from_numpy(np.log(UpperCamelCase_				)				).to(UpperCamelCase_				)

         UpperCAmelCase	    =			np.interp(UpperCamelCase_ ,		np.arange(0 ,		len(UpperCamelCase_				)				) ,		UpperCamelCase_				)
         UpperCAmelCase	    =			np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
         UpperCAmelCase	    =			torch.from_numpy(UpperCamelCase_				).to(device=UpperCamelCase_				)

         # interpolate sigmas
         UpperCAmelCase	    =			sigmas.log().lerp(sigmas.roll(1				).log() ,		0.5				).exp()

         UpperCAmelCase	    =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
         UpperCAmelCase	    =			torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

         if str(UpperCamelCase_				).startswith("""mps"""				):
                # mps does not support float64
                UpperCAmelCase	    =			torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_ ,		dtype=torch.floataa				)
         else:
                UpperCAmelCase	    =			torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)

         # interpolate timesteps
         UpperCAmelCase	    =			self.sigma_to_t(UpperCamelCase_				).to(UpperCamelCase_ ,		dtype=timesteps.dtype				)
         UpperCAmelCase	    =			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,		dim=-1				).flatten()

         UpperCAmelCase	    =			torch.cat([timesteps[:1], interleaved_timesteps]				)

         UpperCAmelCase	    =			None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCAmelCase	    =			defaultdict(UpperCamelCase_				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			sigma.log()

         # get distribution
         UpperCAmelCase	    =			log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         UpperCAmelCase	    =			dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
         UpperCAmelCase	    =			low_idx + 1

         UpperCAmelCase	    =			self.log_sigmas[low_idx]
         UpperCAmelCase	    =			self.log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase	    =			(low - log_sigma) / (low - high)
         UpperCAmelCase	    =			w.clamp(0 ,		1				)

         # transform interpolation to time range
         UpperCAmelCase	    =			(1 - w) * low_idx + w * high_idx
         UpperCAmelCase	    =			t.view(sigma.shape				)
         return t



  @property
  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         return self.sample is None



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = True ,		)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.index_for_timestep(UpperCamelCase_				)

         # advance index counter by 1
         UpperCAmelCase	    =			timestep.cpu().item() if torch.is_tensor(UpperCamelCase_				) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
                UpperCAmelCase	    =			self.sigmas[step_index]
                UpperCAmelCase	    =			self.sigmas_interpol[step_index + 1]
                UpperCAmelCase	    =			self.sigmas[step_index + 1]
         else:
                # 2nd order / KDPM2's method
                UpperCAmelCase	    =			self.sigmas[step_index - 1]
                UpperCAmelCase	    =			self.sigmas_interpol[step_index]
                UpperCAmelCase	    =			self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         UpperCAmelCase	    =			0
         UpperCAmelCase	    =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
                UpperCAmelCase	    =			sigma_hat if self.state_in_first_order else sigma_interpol
                UpperCAmelCase	    =			sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
                UpperCAmelCase	    =			sigma_hat if self.state_in_first_order else sigma_interpol
                UpperCAmelCase	    =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                    sample / (sigma_input**2 + 1)
                )
         elif self.config.prediction_type == "sample":
                raise NotImplementedError("""prediction_type not implemented yet: sample"""				)
         else:
                raise ValueError(
                    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''				)

         if self.state_in_first_order:
                # 2. Convert to an ODE derivative for 1st order
                UpperCAmelCase	    =			(sample - pred_original_sample) / sigma_hat
                # 3. delta timestep
                UpperCAmelCase	    =			sigma_interpol - sigma_hat

                # store for 2nd order step
                UpperCAmelCase	    =			sample
         else:
                # DPM-Solver-2
                # 2. Convert to an ODE derivative for 2nd order
                UpperCAmelCase	    =			(sample - pred_original_sample) / sigma_interpol

                # 3. delta timestep
                UpperCAmelCase	    =			sigma_next - sigma_hat

                UpperCAmelCase	    =			self.sample
                UpperCAmelCase	    =			None

         UpperCAmelCase	    =			sample + derivative * dt

         if not return_dict:
                return (prev_sample,)

         return SchedulerOutput(prev_sample=UpperCamelCase_				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.sigmas.to(device=original_samples.device ,		dtype=original_samples.dtype				)
         if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_				):
                # mps does not support float64
                UpperCAmelCase	    =			self.timesteps.to(original_samples.device ,		dtype=torch.floataa				)
                UpperCAmelCase	    =			timesteps.to(original_samples.device ,		dtype=torch.floataa				)
         else:
                UpperCAmelCase	    =			self.timesteps.to(original_samples.device				)
                UpperCAmelCase	    =			timesteps.to(original_samples.device				)

         UpperCAmelCase	    =			[self.index_for_timestep(UpperCamelCase_ ,		UpperCamelCase_				) for t in timesteps]

         UpperCAmelCase	    =			sigmas[step_indices].flatten()
         while len(sigma.shape				) < len(original_samples.shape				):
                UpperCAmelCase	    =			sigma.unsqueeze(-1				)

         UpperCAmelCase	    =			original_samples + noise * sigma
         return noisy_samples



  def __len__(      self				)			->			int:



         """simple docstring"""





         return self.config.num_train_timesteps








"""simple docstring"""







import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowerCAmelCase_	:    List[str]            =      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              UpperCAmelCase	    =			cached_file(
                  lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       # Load PyTorch checkpoint in tf2 model:
       UpperCAmelCase	    =			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase	,      lowerCAmelCase		)

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

              with torch.no_grad():
                     UpperCAmelCase	    =			pt_model(**pt_model.dummy_inputs		)

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			np.amax(np.abs(np_pt - np_tf		)		)
              print(F'''Max absolute difference between models outputs {diff}'''		)
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''		)
       tf_model.save_weights(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {model_type}'''		)
              print("""=""" * 100		)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			list(aws_model_maps.keys()		)
              if config_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      start=1		):
                     print("""-""" * 100		)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
                                   continue
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase	,      pytorch_checkpoint_path=lowerCAmelCase	,      config_file=lowerCAmelCase	,      tf_dump_path=os.path.join(lowerCAmelCase	,      model_shortcut_name + """-tf_model.h5"""		)	,      compare_with_pt_model=lowerCAmelCase	,      )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase		)
                            os.remove(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )
