

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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__A			:					List[str]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Tuple     , __lowerCamelCase					:			WhisperForConditionalGeneration     , __lowerCamelCase					:			WhisperProcessor     , __lowerCamelCase					:			AutoencoderKL     , __lowerCamelCase					:			CLIPTextModel     , __lowerCamelCase					:			CLIPTokenizer     , __lowerCamelCase					:			UNetaDConditionModel     , __lowerCamelCase					:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     , __lowerCamelCase					:			StableDiffusionSafetyChecker     , __lowerCamelCase					:			CLIPImageProcessor     , ):
       super().__init__()

       if safety_checker is None:
           logger.warning(
               f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
               " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
               " results in services or applications open to the public. Both the diffusers team and Hugging Face"
               " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
               " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
               " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."			)

       self.register_modules(
           speech_model=__lowerCamelCase     , speech_processor=__lowerCamelCase     , vae=__lowerCamelCase     , text_encoder=__lowerCamelCase     , tokenizer=__lowerCamelCase     , unet=__lowerCamelCase     , scheduler=__lowerCamelCase     , feature_extractor=__lowerCamelCase     , )





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Optional[Union[str, int]] = "auto"			):
       if slice_size == "auto":
           SCREAMING_SNAKE_CASE										=   self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       self.enable_attention_slicing(__lowerCamelCase			)





   @torch.no_grad()
   def __call__(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]=16000     , __lowerCamelCase					:			int = 512     , __lowerCamelCase					:			int = 512     , __lowerCamelCase					:			int = 50     , __lowerCamelCase					:			float = 7.5     , __lowerCamelCase					:			Optional[Union[str, List[str]]] = None     , __lowerCamelCase					:			Optional[int] = 1     , __lowerCamelCase					:			float = 0.0     , __lowerCamelCase					:			Optional[torch.Generator] = None     , __lowerCamelCase					:			Optional[torch.FloatTensor] = None     , __lowerCamelCase					:			Optional[str] = "pil"     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None     , __lowerCamelCase					:			int = 1     , **__lowerCamelCase					:			Dict     , ):
       SCREAMING_SNAKE_CASE										=   self.speech_processor.feature_extractor(
           __lowerCamelCase     , return_tensors="pt"     , sampling_rate=__lowerCamelCase			).input_features.to(self.device			)
       SCREAMING_SNAKE_CASE										=   self.speech_model.generate(__lowerCamelCase     , max_length=480000			)

       SCREAMING_SNAKE_CASE										=   self.speech_processor.tokenizer.batch_decode(__lowerCamelCase     , skip_special_tokens=__lowerCamelCase     , normalize=__lowerCamelCase			)[
           0
       ]

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

       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(__lowerCamelCase     , __lowerCamelCase			) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__lowerCamelCase			)}."			)

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

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           SCREAMING_SNAKE_CASE										=   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}"			)
           SCREAMING_SNAKE_CASE										=   text_input_ids[:, : self.tokenizer.model_max_length]
       SCREAMING_SNAKE_CASE										=   self.text_encoder(text_input_ids.to(self.device			)			)[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   text_embeddings.shape
       SCREAMING_SNAKE_CASE										=   text_embeddings.repeat(1     , __lowerCamelCase     , 1			)
       SCREAMING_SNAKE_CASE										=   text_embeddings.view(bs_embed * num_images_per_prompt     , __lowerCamelCase     , -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.
       SCREAMING_SNAKE_CASE										=   guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
           SCREAMING_SNAKE_CASE										=   42
           if negative_prompt is None:
               SCREAMING_SNAKE_CASE										=   [""] * batch_size
           elif type(__lowerCamelCase			) is not type(__lowerCamelCase			):
               raise TypeError(
                   f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase			)} !="
                   f" {type(__lowerCamelCase			)}."			)
           elif isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   [negative_prompt]
           elif batch_size != len(__lowerCamelCase			):
               raise ValueError(
                   f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase			)}, but `prompt`:"
                   f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                   " the batch size of `prompt`."			)
           else:
               SCREAMING_SNAKE_CASE										=   negative_prompt

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

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.shape[1]
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.repeat(1     , __lowerCamelCase     , 1			)
           SCREAMING_SNAKE_CASE										=   uncond_embeddings.view(batch_size * num_images_per_prompt     , __lowerCamelCase     , -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
           SCREAMING_SNAKE_CASE										=   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`.
       SCREAMING_SNAKE_CASE										=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       SCREAMING_SNAKE_CASE										=   text_embeddings.dtype
       if latents is None:
           if self.device.type == "mps":
               # randn does not exist on mps
               SCREAMING_SNAKE_CASE										=   torch.randn(__lowerCamelCase     , generator=__lowerCamelCase     , device="cpu"     , dtype=__lowerCamelCase			).to(
                   self.device			)
           else:
               SCREAMING_SNAKE_CASE										=   torch.randn(__lowerCamelCase     , generator=__lowerCamelCase     , device=self.device     , dtype=__lowerCamelCase			)
       else:
           if latents.shape != latents_shape:
               raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"			)
           SCREAMING_SNAKE_CASE										=   latents.to(self.device			)

       # set timesteps
       self.scheduler.set_timesteps(__lowerCamelCase			)

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

       # scale the initial noise by the standard deviation required by the scheduler
       SCREAMING_SNAKE_CASE										=   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]
       SCREAMING_SNAKE_CASE										=   "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
       SCREAMING_SNAKE_CASE										=   {}
       if accepts_eta:
           SCREAMING_SNAKE_CASE										=   eta

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

           # predict the noise residual
           SCREAMING_SNAKE_CASE										=   self.unet(__lowerCamelCase     , __lowerCamelCase     , encoder_hidden_states=__lowerCamelCase			).sample

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

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

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

       SCREAMING_SNAKE_CASE										=   1 / 0.18_215 * latents
       SCREAMING_SNAKE_CASE										=   self.vae.decode(__lowerCamelCase			).sample

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

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

       if output_type == "pil":
           SCREAMING_SNAKE_CASE										=   self.numpy_to_pil(__lowerCamelCase			)

       if not return_dict:
           return image

       return StableDiffusionPipelineOutput(images=__lowerCamelCase     , nsfw_content_detected=__lowerCamelCase			)






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

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


if version.parse(nlp.__version__) != version.parse('0.8.3'):
    raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
    raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	'The Nymphenburg Palace is a beautiful palace in Munich!'






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       "attention_cell": "multi_head",
       "num_layers": 4,
       "units": 1024,
       "hidden_size": 768,
       "max_length": 512,
       "num_heads": 8,
       "scaled": True,
       "dropout": 0.1,
       "use_residual": True,
       "embed_size": 1024,
       "embed_dropout": 0.1,
       "word_embed": None,
       "layer_norm_eps": 1e-5,
       "token_type_vocab_size": 2,
   }

   UpperCAmelCase__						=bort_4_8_768_1024_hparams

   # Let's construct the original Bort model here
   # Taken from official BERT implementation, see:
   # https://github.com/alexa/bort/blob/master/bort/bort.py
   UpperCAmelCase__						=BERTEncoder(
       attention_cell=predefined_args["attention_cell"]				,							num_layers=predefined_args["num_layers"]				,							units=predefined_args["units"]				,							hidden_size=predefined_args["hidden_size"]				,							max_length=predefined_args["max_length"]				,							num_heads=predefined_args["num_heads"]				,							scaled=predefined_args["scaled"]				,							dropout=predefined_args["dropout"]				,							output_attention=A				,							output_all_encodings=A				,							use_residual=predefined_args["use_residual"]				,							activation=predefined_args.get("activation"				,							"gelu"     )				,							layer_norm_eps=predefined_args.get("layer_norm_eps"				,							A     )				,							)

   # Vocab information needs to be fetched first
   # It's the same as RoBERTa, so RobertaTokenizer can be used later
   UpperCAmelCase__						="openwebtext_ccnews_stories_books_cased"

   # Specify download folder to Gluonnlp's vocab
   UpperCAmelCase__						=os.path.join(get_home_dir()				,							"models"     )
   UpperCAmelCase__						=_load_vocab(A				,							A				,							A				,							cls=A     )

   UpperCAmelCase__						=nlp.model.BERTModel(
       A				,							len(A     )				,							units=predefined_args["units"]				,							embed_size=predefined_args["embed_size"]				,							embed_dropout=predefined_args["embed_dropout"]				,							word_embed=predefined_args["word_embed"]				,							use_pooler=A				,							use_token_type_embed=A				,							token_type_vocab_size=predefined_args["token_type_vocab_size"]				,							use_classifier=A				,							use_decoder=A				,							)

   original_bort.load_parameters(A				,							cast_dtype=A				,							ignore_extra=A     )
   UpperCAmelCase__						=original_bort._collect_params_with_prefix()

   # Build our config 🤗
   UpperCAmelCase__						={
       "architectures": ["BertForMaskedLM"],
       "attention_probs_dropout_prob": predefined_args["dropout"],
       "hidden_act": "gelu",
       "hidden_dropout_prob": predefined_args["dropout"],
       "hidden_size": predefined_args["embed_size"],
       "initializer_range": 0.02,
       "intermediate_size": predefined_args["hidden_size"],
       "layer_norm_eps": predefined_args["layer_norm_eps"],
       "max_position_embeddings": predefined_args["max_length"],
       "model_type": "bort",
       "num_attention_heads": predefined_args["num_heads"],
       "num_hidden_layers": predefined_args["num_layers"],
       "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
       "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
       "vocab_size": len(A     ),
   }

   UpperCAmelCase__						=BertConfig.from_dict(A     )
   UpperCAmelCase__						=BertForMaskedLM(A     )
   hf_bort_model.eval()

   # Parameter mapping table (Gluonnlp to Transformers)
   # * denotes layer index
   #
   # | Gluon Parameter                                                | Transformers Parameter
   # | -------------------------------------------------------------- | ----------------------
   # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
   # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
   # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
   # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
   # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
   # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
   # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
   # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
   # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
   # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
   # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
   # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
   # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
   # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
   # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
   # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

   # Helper function to convert MXNET Arrays to PyTorch
   def to_torch(A     ) -> nn.Parameter:
      return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()     )     )

   # Check param shapes and map new HF param back
   def check_and_map_params(A				,							A     ):
      UpperCAmelCase__						=hf_param.shape

      UpperCAmelCase__						=to_torch(params[gluon_param]     )
      UpperCAmelCase__						=gluon_param.shape

      assert (
          shape_hf == shape_gluon
      ), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

      return gluon_param

   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight				,							"word_embed.0.weight"     )
   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight				,							"encoder.position_weight"     )
   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias				,							"encoder.layer_norm.beta"     )
   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.weight				,							"encoder.layer_norm.gamma"     )

   # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
   UpperCAmelCase__						=torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data     )

   for i in range(hf_bort_config.num_hidden_layers     ):
      UpperCAmelCase__						=hf_bort_model.bert.encoder.layer[i]

      # self attention
      UpperCAmelCase__						=layer.attention.self

      UpperCAmelCase__						=check_and_map_params(
          self_attn.key.bias.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""     )

      UpperCAmelCase__						=check_and_map_params(
          self_attn.key.weight.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_attn.query.bias.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_attn.query.weight.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_attn.value.bias.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_attn.value.weight.data				,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""     )

      # self attention output
      UpperCAmelCase__						=layer.attention.output

      UpperCAmelCase__						=check_and_map_params(
          self_output.dense.bias				,							F"""encoder.transformer_cells.{i}.proj.bias"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_output.dense.weight				,							F"""encoder.transformer_cells.{i}.proj.weight"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_output.LayerNorm.bias				,							F"""encoder.transformer_cells.{i}.layer_norm.beta"""     )
      UpperCAmelCase__						=check_and_map_params(
          self_output.LayerNorm.weight				,							F"""encoder.transformer_cells.{i}.layer_norm.gamma"""     )

      # intermediate
      UpperCAmelCase__						=layer.intermediate

      UpperCAmelCase__						=check_and_map_params(
          intermediate.dense.bias				,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""     )
      UpperCAmelCase__						=check_and_map_params(
          intermediate.dense.weight				,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""     )

      # output
      UpperCAmelCase__						=layer.output

      UpperCAmelCase__						=check_and_map_params(
          bert_output.dense.bias				,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""     )
      UpperCAmelCase__						=check_and_map_params(
          bert_output.dense.weight				,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""     )
      UpperCAmelCase__						=check_and_map_params(
          bert_output.LayerNorm.bias				,							F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""     )
      UpperCAmelCase__						=check_and_map_params(
          bert_output.LayerNorm.weight				,							F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""     )

   # Save space and energy 🎄
   hf_bort_model.half()

   # Compare output of both models
   UpperCAmelCase__						=RobertaTokenizer.from_pretrained("roberta-base"     )

   UpperCAmelCase__						=tokenizer.encode_plus(A     )["input_ids"]

   # Get gluon output
   UpperCAmelCase__						=mx.nd.array([input_ids]     )
   UpperCAmelCase__						=original_bort(inputs=A				,							token_types=[]     )

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(A     )
   UpperCAmelCase__						=BertModel.from_pretrained(A     )
   hf_bort_model.eval()

   UpperCAmelCase__						=tokenizer.encode_plus(A				,							return_tensors="pt"     )
   UpperCAmelCase__						=hf_bort_model(**A     )[0]

   UpperCAmelCase__						=output_gluon[0].asnumpy()
   UpperCAmelCase__						=output_hf[0].detach().numpy()

   UpperCAmelCase__						=np.max(np.abs(hf_layer - gluon_layer     )     ).item()
   UpperCAmelCase__						=np.allclose(A				,							A				,							atol=1e-3     )

   if success:
      print("✔️ Both model do output the same tensors"     )
   else:
      print("❌ Both model do **NOT** output the same tensors"     )
      print("Absolute difference is:"				,							A     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCamelCase_												=	parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



