



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()
__snake_case						:int      		=     logging.get_logger(__name__)

__snake_case						:str      		=     '''The Nymphenburg Palace is a beautiful palace in Munich!'''





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{
        '''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,
    }

    __a						       =				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
    __a						       =				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=_UpperCAmelCase   , output_all_encodings=_UpperCAmelCase   , use_residual=predefined_args['''use_residual''']   , activation=predefined_args.get('''activation'''   , '''gelu'''				)   , layer_norm_eps=predefined_args.get('''layer_norm_eps'''   , _UpperCAmelCase				)   , )

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

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

    __a						       =				nlp.model.BERTModel(
        _UpperCAmelCase   , len(_UpperCAmelCase				)   , units=predefined_args['''units''']   , embed_size=predefined_args['''embed_size''']   , embed_dropout=predefined_args['''embed_dropout''']   , word_embed=predefined_args['''word_embed''']   , use_pooler=_UpperCAmelCase   , use_token_type_embed=_UpperCAmelCase   , token_type_vocab_size=predefined_args['''token_type_vocab_size''']   , use_classifier=_UpperCAmelCase   , use_decoder=_UpperCAmelCase   , )

    original_bort.load_parameters(_UpperCAmelCase   , cast_dtype=_UpperCAmelCase   , ignore_extra=_UpperCAmelCase				)
    __a						       =				original_bort._collect_params_with_prefix()

    # Build our config 🤗
    __a						       =				{
        '''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(_UpperCAmelCase				),
    }

    __a						       =				BertConfig.from_dict(_UpperCAmelCase				)
    __a						       =				BertForMaskedLM(_UpperCAmelCase				)
    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(_UpperCAmelCase				) -> 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(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				hf_param.shape

        __a						       =				to_torch(params[gluon_param]				)
        __a						       =				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

    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight   , '''word_embed.0.weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight   , '''encoder.position_weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias   , '''encoder.layer_norm.beta'''				)
    __a						       =				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)
    __a						       =				torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

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

        # self attention
        __a						       =				layer.attention.self

        __a						       =				check_and_map_params(
            self_attn.key.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'				)

        __a						       =				check_and_map_params(
            self_attn.key.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'				)
        __a						       =				check_and_map_params(
            self_attn.query.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'				)
        __a						       =				check_and_map_params(
            self_attn.query.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'				)
        __a						       =				check_and_map_params(
            self_attn.value.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'				)
        __a						       =				check_and_map_params(
            self_attn.value.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'				)

        # self attention output
        __a						       =				layer.attention.output

        __a						       =				check_and_map_params(
            self_output.dense.bias   , f'encoder.transformer_cells.{i}.proj.bias'				)
        __a						       =				check_and_map_params(
            self_output.dense.weight   , f'encoder.transformer_cells.{i}.proj.weight'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.layer_norm.beta'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.weight   , f'encoder.transformer_cells.{i}.layer_norm.gamma'				)

        # intermediate
        __a						       =				layer.intermediate

        __a						       =				check_and_map_params(
            intermediate.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'				)
        __a						       =				check_and_map_params(
            intermediate.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'				)

        # output
        __a						       =				layer.output

        __a						       =				check_and_map_params(
            bert_output.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'				)
        __a						       =				check_and_map_params(
            bert_output.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'				)
        __a						       =				check_and_map_params(
            bert_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'				)
        __a						       =				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
    __a						       =				RobertaTokenizer.from_pretrained('''roberta-base'''				)

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase				)['''input_ids''']

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

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

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase   , return_tensors='''pt'''				)
    __a						       =				hf_bort_model(**_UpperCAmelCase				)[0]

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

    __a						       =				np.max(np.abs(hf_layer - gluon_layer				)				).item()
    __a						       =				np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , 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:'''   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Optional[Any]      		=     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.'''
 )
 __snake_case						:Union[str, Any]      		=     parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE     ,		prediction_type=__SCREAMING_SNAKE_CASE     ,		sample_max_value=__SCREAMING_SNAKE_CASE     ,		)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for t in [0, 500, 999]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

           for i, timestep in enumerate(__SCREAMING_SNAKE_CASE):
               if i == len(__SCREAMING_SNAKE_CASE) - 1:
                   __a						       =				-1
               else:
                   __a						       =				timesteps[i + 1]

               __a						       =				scheduler.previous_timestep(__SCREAMING_SNAKE_CASE)
               __a						       =				prev_t.item()

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 51, 0]

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''`custom_timesteps` must be in descending order.'''):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
               scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE     ,		timesteps=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __SCREAMING_SNAKE_CASE     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)





