





'''simple docstring'''


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()
lowerCAmelCase					:     Dict												=logging.get_logger(__name__)

lowerCAmelCase					:     str												='''The Nymphenburg Palace is a beautiful palace in Munich!'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					str ):
   lowercase_			:int    			= {
       '''attention_cell''': '''multi_head''',
       '''num_layers''': 4,
       '''units''': 10_24,
       '''hidden_size''': 7_68,
       '''max_length''': 5_12,
       '''num_heads''': 8,
       '''scaled''': True,
       '''dropout''': 0.1,
       '''use_residual''': True,
       '''embed_size''': 10_24,
       '''embed_dropout''': 0.1,
       '''word_embed''': None,
       '''layer_norm_eps''': 1e-5,
       '''token_type_vocab_size''': 2,
   }

   lowercase_			:List[str]    			= 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
   lowercase_			:Optional[Any]    			= 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=snake_case_	,output_all_encodings=snake_case_	,use_residual=predefined_args["use_residual"]	,activation=predefined_args.get("activation"	,"gelu" )	,layer_norm_eps=predefined_args.get("layer_norm_eps"	,snake_case_ )	,)

   # Vocab information needs to be fetched first
   # It's the same as RoBERTa, so RobertaTokenizer can be used later
   lowercase_			:List[Any]    			= '''openwebtext_ccnews_stories_books_cased'''

   # Specify download folder to Gluonnlp's vocab
   lowercase_			:Any    			= os.path.join(get_home_dir()	,"models" )
   lowercase_			:Any    			= _load_vocab(snake_case_	,snake_case_	,snake_case_	,cls=snake_case_ )

   lowercase_			:Dict    			= nlp.model.BERTModel(
       snake_case_	,len(snake_case_ )	,units=predefined_args["units"]	,embed_size=predefined_args["embed_size"]	,embed_dropout=predefined_args["embed_dropout"]	,word_embed=predefined_args["word_embed"]	,use_pooler=snake_case_	,use_token_type_embed=snake_case_	,token_type_vocab_size=predefined_args["token_type_vocab_size"]	,use_classifier=snake_case_	,use_decoder=snake_case_	,)

   original_bort.load_parameters(snake_case_	,cast_dtype=snake_case_	,ignore_extra=snake_case_ )
   lowercase_			:Optional[Any]    			= original_bort._collect_params_with_prefix()

   # Build our config 🤗
   lowercase_			:Dict    			= {
       '''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(snake_case_ ),
   }

   lowercase_			:Dict    			= BertConfig.from_dict(snake_case_ )
   lowercase_			:Optional[int]    			= BertForMaskedLM(snake_case_ )
   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(__lowerCamelCase					:					Optional[Any] ) -> 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(__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					int ):
      lowercase_			:Dict    			= hf_param.shape

      lowercase_			:Tuple    			= to_torch(params[gluon_param] )
      lowercase_			:Union[str, Any]    			= 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

   lowercase_			:Optional[Any]    			= check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight	,"word_embed.0.weight" )
   lowercase_			:List[str]    			= check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight	,"encoder.position_weight" )
   lowercase_			:List[Any]    			= check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias	,"encoder.layer_norm.beta" )
   lowercase_			:Tuple    			= 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)
   lowercase_			:str    			= torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

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

      # self attention
      lowercase_			:BertSelfAttention    			= layer.attention.self

      lowercase_			:Dict    			= check_and_map_params(
          self_attn.key.bias.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias' )

      lowercase_			:Optional[int]    			= check_and_map_params(
          self_attn.key.weight.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight' )
      lowercase_			:Tuple    			= check_and_map_params(
          self_attn.query.bias.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias' )
      lowercase_			:int    			= check_and_map_params(
          self_attn.query.weight.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight' )
      lowercase_			:Optional[Any]    			= check_and_map_params(
          self_attn.value.bias.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias' )
      lowercase_			:List[Any]    			= check_and_map_params(
          self_attn.value.weight.data	,F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight' )

      # self attention output
      lowercase_			:BertSelfOutput    			= layer.attention.output

      lowercase_			:Union[str, Any]    			= check_and_map_params(
          self_output.dense.bias	,F'encoder.transformer_cells.{i}.proj.bias' )
      lowercase_			:Any    			= check_and_map_params(
          self_output.dense.weight	,F'encoder.transformer_cells.{i}.proj.weight' )
      lowercase_			:Tuple    			= check_and_map_params(
          self_output.LayerNorm.bias	,F'encoder.transformer_cells.{i}.layer_norm.beta' )
      lowercase_			:str    			= check_and_map_params(
          self_output.LayerNorm.weight	,F'encoder.transformer_cells.{i}.layer_norm.gamma' )

      # intermediate
      lowercase_			:BertIntermediate    			= layer.intermediate

      lowercase_			:str    			= check_and_map_params(
          intermediate.dense.bias	,F'encoder.transformer_cells.{i}.ffn.ffn_1.bias' )
      lowercase_			:List[str]    			= check_and_map_params(
          intermediate.dense.weight	,F'encoder.transformer_cells.{i}.ffn.ffn_1.weight' )

      # output
      lowercase_			:BertOutput    			= layer.output

      lowercase_			:List[Any]    			= check_and_map_params(
          bert_output.dense.bias	,F'encoder.transformer_cells.{i}.ffn.ffn_2.bias' )
      lowercase_			:Dict    			= check_and_map_params(
          bert_output.dense.weight	,F'encoder.transformer_cells.{i}.ffn.ffn_2.weight' )
      lowercase_			:List[Any]    			= check_and_map_params(
          bert_output.LayerNorm.bias	,F'encoder.transformer_cells.{i}.ffn.layer_norm.beta' )
      lowercase_			:int    			= 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
   lowercase_			:int    			= RobertaTokenizer.from_pretrained("roberta-base" )

   lowercase_			:Optional[int]    			= tokenizer.encode_plus(snake_case_ )['''input_ids''']

   # Get gluon output
   lowercase_			:str    			= mx.nd.array([input_ids] )
   lowercase_			:Any    			= original_bort(inputs=snake_case_	,token_types=[] )

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(snake_case_ )
   lowercase_			:List[Any]    			= BertModel.from_pretrained(snake_case_ )
   hf_bort_model.eval()

   lowercase_			:List[str]    			= tokenizer.encode_plus(snake_case_	,return_tensors="pt" )
   lowercase_			:Union[str, Any]    			= hf_bort_model(**snake_case_ )[0]

   lowercase_			:str    			= output_gluon[0].asnumpy()
   lowercase_			:Dict    			= output_hf[0].detach().numpy()

   lowercase_			:str    			= np.max(np.abs(hf_layer - gluon_layer ) ).item()
   lowercase_			:Optional[int]    			= np.allclose(snake_case_	,snake_case_	,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:"	,snake_case_ )


if __name__ == "__main__":
 lowerCAmelCase					:     str												=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.'''
 )
 lowerCAmelCase					:     Tuple												=parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
       import os

       import jax.numpy as jnp
       from jax import jit

       from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
       from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

       a_ =			"""0.12"""  # assumed parallelism: 8


if is_torch_available():
       import torch







def 		__lowercase     (   snake_case_  :  Any				,snake_case_  :  int				,snake_case_  :  List[str]=None						)   ->Union[str, Any]:



    '''simple docstring'''







    if rng is None:
        __A    :						Union[str, Any]						 =     random.Random()

    __A    :						List[str]						 =     1
    for dim in shape:
        total_dims *= dim

    __A    :						List[str]						 =     []
    for _ in range(snake_case_						):
        values.append(rng.randint(0				,vocab_size - 1						)						)

    __A    :						Optional[Any]						 =     np.array(snake_case_				,dtype=jnp.intaa						).reshape(snake_case_						)

    return output







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Union[str, Any]=None						)   ->int:



    '''simple docstring'''







    __A    :						int						 =     ids_tensor(snake_case_				,vocab_size=2				,rng=snake_case_						)
    # make sure that at least one token is attended to for each batch
    __A    :						str						 =     1
    return attn_mask






@require_flax
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     None
 _lowerCamelCase        =     ()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A    :						Optional[int]						 =     self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     __A    :						Optional[int]						 =     2
     __A    :						Tuple						 =     inputs['''input_ids'''].shape[-1] // 2
     __A    :						List[Any]						 =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

     __A    :						Optional[Any]						 =     jnp.ones_like(__lowerCamelCase  )
     __A    :						List[Any]						 =     attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     __A    :						Optional[int]						 =     input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
         # hack to allow generate for models such as GPT2 as is done in `generate()`
         __A    :						Optional[int]						 =     config.eos_token_id
     return config, input_ids, attention_mask, max_length

 @is_pt_flax_cross_test
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     False
     __A    :						str						 =     max_length
     __A    :						Tuple						 =     0

     for model_class in self.all_generative_model_classes:
         __A    :						Union[str, Any]						 =     model_class(__lowerCamelCase  )

         __A    :						Any						 =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __A    :						Dict						 =     getattr(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						Tuple						 =     pt_model_class(__lowerCamelCase  ).eval()
         __A    :						Optional[Any]						 =     load_flax_weights_in_pytorch_model(__lowerCamelCase					,						flax_model.params  )

         __A    :						str						 =     flax_model.generate(__lowerCamelCase  ).sequences
         __A    :						List[Any]						 =     pt_model.generate(torch.tensor(__lowerCamelCase					,						dtype=torch.long  )  )

         if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
             __A    :						Any						 =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

         self.assertListEqual(pt_generation_outputs.numpy().tolist()					,						flax_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Optional[Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

         __A    :						Dict						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     True
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Dict						 =     model_class(__lowerCamelCase  )

         __A    :						List[Any]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						List[Any]						 =     jit(model.generate  )
         __A    :						List[str]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Any						 =     max_length
     __A    :						List[Any]						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						Tuple						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[Any]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Tuple						 =     self._get_input_ids_and_config()
     __A    :						str						 =     False
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[Any]						 =     2
     __A    :						Any						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						List[str]						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[int]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[0]					,						input_ids.shape[0] * config.num_return_sequences  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Any						 =     self._get_input_ids_and_config()
     __A    :						Optional[Any]						 =     True
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[str]						 =     0.8
     __A    :						List[str]						 =     10
     __A    :						Union[str, Any]						 =     0.3
     __A    :						Union[str, Any]						 =     1
     __A    :						Optional[Any]						 =     8
     __A    :						Dict						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						List[Any]						 =     model_class(__lowerCamelCase  )

         __A    :						List[str]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Any						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[str]						 =     1
     __A    :						str						 =     8
     __A    :						Any						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						Union[str, Any]						 =     model_class(__lowerCamelCase  )

         __A    :						Tuple						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Optional[Any]						 =     jit(model.generate  )
         __A    :						Optional[Any]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						Optional[int]						 =     max_length
     __A    :						List[str]						 =     2
     __A    :						List[Any]						 =     1
     __A    :						Optional[Any]						 =     8
     __A    :						str						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						Dict						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						Optional[Any]						 =     False
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						int						 =     model_class(__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Optional[int]						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[int]						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						str						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						List[Any]						 =     True
     __A    :						Any						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						str						 =     model_class(__lowerCamelCase  )

         __A    :						List[Any]						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						List[str]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[Any]						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						List[str]						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						Optional[int]						 =     2
     __A    :						Dict						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Any						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )






@require_flax
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''  )
     __A    :						Optional[Any]						 =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )

     __A    :						Any						 =     '''Hello world'''
     __A    :						Dict						 =     tokenizer(__lowerCamelCase					,						return_tensors='''np'''  ).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(__lowerCamelCase					,						'''do_samples'''  ):
         model.generate(__lowerCamelCase					,						do_samples=__lowerCamelCase  )

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(__lowerCamelCase					,						'''foo'''  ):
         __A    :						Any						 =     {'''foo''': '''bar'''}
         model.generate(__lowerCamelCase					,						**__lowerCamelCase  )




