


import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     BioGptForCausalLM,
     BioGptForSequenceClassification,
     BioGptForTokenClassification,
     BioGptModel,
     BioGptTokenizer,
 )
 from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class A    :






      def __init__(   self		:	int     ,  lowercase_		:	Tuple     ,  lowercase_		:	Dict=13     ,  lowercase_		:	int=7     ,  lowercase_		:	Optional[int]=True     ,  lowercase_		:	int=True     ,  lowercase_		:	Union[str, Any]=False     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	Dict=99     ,  lowercase_		:	List[Any]=32     ,  lowercase_		:	List[str]=5     ,  lowercase_		:	Any=4     ,  lowercase_		:	Tuple=37     ,  lowercase_		:	List[str]="gelu"     ,  lowercase_		:	Optional[Any]=0.1     ,  lowercase_		:	Any=0.1     ,  lowercase_		:	Optional[Any]=512     ,  lowercase_		:	List[Any]=16     ,  lowercase_		:	str=2     ,  lowercase_		:	Optional[Any]=0.02     ,  lowercase_		:	Tuple=3     ,  lowercase_		:	Any=4     ,  lowercase_		:	Any=None     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						int            =parent
             _lowerCamelCase			:						int            =batch_size
             _lowerCamelCase			:						Tuple            =seq_length
             _lowerCamelCase			:						List[str]            =is_training
             _lowerCamelCase			:						Dict            =use_input_mask
             _lowerCamelCase			:						int            =use_token_type_ids
             _lowerCamelCase			:						int            =use_labels
             _lowerCamelCase			:						Tuple            =vocab_size
             _lowerCamelCase			:						Tuple            =hidden_size
             _lowerCamelCase			:						Any            =num_hidden_layers
             _lowerCamelCase			:						Dict            =num_attention_heads
             _lowerCamelCase			:						Optional[Any]            =intermediate_size
             _lowerCamelCase			:						List[Any]            =hidden_act
             _lowerCamelCase			:						Optional[int]            =hidden_dropout_prob
             _lowerCamelCase			:						Dict            =attention_probs_dropout_prob
             _lowerCamelCase			:						Dict            =max_position_embeddings
             _lowerCamelCase			:						str            =type_vocab_size
             _lowerCamelCase			:						Any            =type_sequence_label_size
             _lowerCamelCase			:						Tuple            =initializer_range
             _lowerCamelCase			:						int            =num_labels
             _lowerCamelCase			:						int            =num_choices
             _lowerCamelCase			:						int            =scope






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _lowerCamelCase			:						Tuple            =None
             if self.use_input_mask:
                    _lowerCamelCase			:						Optional[int]            =random_attention_mask([self.batch_size, self.seq_length]					)

             _lowerCamelCase			:						Optional[Any]            =None
             if self.use_token_type_ids:
                    _lowerCamelCase			:						Tuple            =ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

             _lowerCamelCase			:						Any            =None
             _lowerCamelCase			:						Union[str, Any]            =None
             _lowerCamelCase			:						Tuple            =None
             if self.use_labels:
                    _lowerCamelCase			:						Tuple            =ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                    _lowerCamelCase			:						Optional[Any]            =ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)
                    _lowerCamelCase			:						Tuple            =ids_tensor([self.batch_size]     ,  self.num_choices					)

             _lowerCamelCase			:						Optional[Any]            =self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[int]:
             """simple docstring"""




             return BioGptConfig(
                 vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  is_decoder=lowercase_     ,  initializer_range=self.initializer_range     ,  )






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	Tuple     ,  lowercase_		:	Tuple     ,  lowercase_		:	Tuple     ,  lowercase_		:	Tuple     ,  lowercase_		:	List[Any]     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =BioGptModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						Tuple            =model(lowercase_     ,  attention_mask=lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =model(lowercase_					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	int     ,  lowercase_		:	int     ,  lowercase_		:	List[Any]     ,  lowercase_		:	Dict     ,  lowercase_		:	Tuple     ,  lowercase_		:	str     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	Optional[Any]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =BioGptForCausalLM(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						Optional[Any]            =model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	int     ,  lowercase_		:	List[str]     ,  lowercase_		:	str     ,  lowercase_		:	List[str]     ,  lowercase_		:	Tuple     ,  *lowercase_		:	Union[str, Any]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =BioGptModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()

             # create attention mask
             _lowerCamelCase			:						int            =torch.ones(input_ids.shape     ,  dtype=torch.long     ,  device=lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =self.seq_length // 2
             _lowerCamelCase			:						Tuple            =0

             # first forward pass
             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =model(lowercase_     ,  attention_mask=lowercase_					).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _lowerCamelCase			:						Any            =ids_tensor((self.batch_size, 1)     ,  config.vocab_size					)

             # change a random masked slice from input_ids
             _lowerCamelCase			:						Any            =ids_tensor((1,)     ,  lowercase_					).item() + 1
             _lowerCamelCase			:						List[str]            =ids_tensor((self.batch_size, 1)     ,  config.vocab_size					).squeeze(-1					)
             _lowerCamelCase			:						Optional[int]            =random_other_next_tokens

             # append to next input_ids and attn_mask
             _lowerCamelCase			:						int            =torch.cat([input_ids, next_tokens]     ,  dim=-1					)
             _lowerCamelCase			:						Any            =torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)     ,  dtype=torch.long     ,  device=lowercase_					)]     ,  dim=1     ,  )

             # get two different outputs
             _lowerCamelCase			:						Any            =model(lowercase_     ,  attention_mask=lowercase_					)['last_hidden_state']
             _lowerCamelCase			:						Tuple            =model(lowercase_     ,  past_key_values=lowercase_     ,  attention_mask=lowercase_					)['last_hidden_state']

             # select random slice
             _lowerCamelCase			:						List[str]            =ids_tensor((1,)     ,  output_from_past.shape[-1]					).item()
             _lowerCamelCase			:						str            =output_from_no_past[:, -1, random_slice_idx].detach()
             _lowerCamelCase			:						List[str]            =output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(lowercase_     ,  lowercase_     ,  atol=1E-3					)					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Tuple     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	List[str]     ,  lowercase_		:	str     ,  lowercase_		:	Optional[int]     ,  *lowercase_		:	List[Any]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =BioGptModel(config=lowercase_					).to(lowercase_					).eval()

             _lowerCamelCase			:						Union[str, Any]            =torch.ones(input_ids.shape     ,  dtype=torch.long     ,  device=lowercase_					)

             # first forward pass
             _lowerCamelCase			:						str            =model(lowercase_     ,  attention_mask=lowercase_     ,  use_cache=lowercase_					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =outputs.to_tuple()

             # create hypothetical multiple next token and extent to next_input_ids
             _lowerCamelCase			:						Dict            =ids_tensor((self.batch_size, 3)     ,  config.vocab_size					)
             _lowerCamelCase			:						Optional[Any]            =ids_tensor((self.batch_size, 3)     ,  2					)

             # append to next input_ids and
             _lowerCamelCase			:						Dict            =torch.cat([input_ids, next_tokens]     ,  dim=-1					)
             _lowerCamelCase			:						List[Any]            =torch.cat([attention_mask, next_attn_mask]     ,  dim=-1					)

             _lowerCamelCase			:						Any            =model(lowercase_     ,  attention_mask=lowercase_					)['last_hidden_state']
             _lowerCamelCase			:						Dict            =model(lowercase_     ,  attention_mask=lowercase_     ,  past_key_values=lowercase_					)[
                 'last_hidden_state'
             ]

             # select random slice
             _lowerCamelCase			:						List[str]            =ids_tensor((1,)     ,  output_from_past.shape[-1]					).item()
             _lowerCamelCase			:						Union[str, Any]            =output_from_no_past[:, -3:, random_slice_idx].detach()
             _lowerCamelCase			:						int            =output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(lowercase_     ,  lowercase_     ,  atol=1E-3					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	List[Any]     ,  lowercase_		:	Tuple     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	Tuple     ,  lowercase_		:	Any     ,  *lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=False					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						str            =BioGptForCausalLM(lowercase_					)
             model.to(lowercase_					)
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  labels=lowercase_					)
             self.parent.assertEqual(result.loss.shape     ,  ()					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)
             result.loss.backward()






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	Tuple     ,  *lowercase_		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =BioGptModel(lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers					)
             for key in model.state_dict().keys():
                    if "c_proj" in key and "weight" in key:
                           self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]					) - model_std					)     ,  0.001					)
                           self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]					) - 0.0					)     ,  0.01					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	List[Any]     ,  lowercase_		:	List[str]     ,  lowercase_		:	Any     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	Tuple     ,  *lowercase_		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.num_labels
             _lowerCamelCase			:						Tuple            =BioGptForTokenClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						int            =model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.prepare_config_and_inputs()
             (
                 (
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	(
                 _lowerCamelCase
             )      ,	
             )			:						Union[str, Any]            =config_and_inputs
             _lowerCamelCase			:						str            ={'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_torch
class A    (  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    List[Any] =(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      UpperCamelCase__				:    Optional[Any] =(BioGptForCausalLM,) if is_torch_available() else ()
      UpperCamelCase__				:    Any =(
          {
              'feature-extraction': BioGptModel,
              'text-classification': BioGptForSequenceClassification,
              'text-generation': BioGptForCausalLM,
              'token-classification': BioGptForTokenClassification,
              'zero-shot': BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase__				:    Any =False






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =BioGptModelTester(self					)
             _lowerCamelCase			:						List[Any]            =ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Any:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _lowerCamelCase			:						Union[str, Any]            =type
                    self.model_tester.create_and_check_model(*lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*lowercase_     ,  gradient_checkpointing=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase_					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase_					)






      @slow
      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
             model.to(lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =BioGptTokenizer.from_pretrained('microsoft/biogpt'					)

             _lowerCamelCase			:						List[Any]            ='left'

             # Define PAD Token = EOS Token = 50256
             _lowerCamelCase			:						Optional[Any]            =tokenizer.eos_token
             _lowerCamelCase			:						Optional[int]            =model.config.eos_token_id

             # use different length sentences to test batching
             _lowerCamelCase			:						List[Any]            =[
                 'Hello, my dog is a little',
                 'Today, I',
             ]

             _lowerCamelCase			:						str            =tokenizer(lowercase_     ,  return_tensors='pt'     ,  padding=lowercase_					)
             _lowerCamelCase			:						Any            =inputs['input_ids'].to(lowercase_					)

             _lowerCamelCase			:						List[Any]            =model.generate(
                 input_ids=lowercase_     ,  attention_mask=inputs['attention_mask'].to(lowercase_					)     ,  )

             _lowerCamelCase			:						Optional[Any]            =tokenizer(sentences[0]     ,  return_tensors='pt'					).input_ids.to(lowercase_					)
             _lowerCamelCase			:						Dict            =model.generate(input_ids=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
             _lowerCamelCase			:						int            =tokenizer(sentences[1]     ,  return_tensors='pt'					).input_ids.to(lowercase_					)
             _lowerCamelCase			:						List[str]            =model.generate(input_ids=lowercase_     ,  max_length=model.config.max_length - num_paddings					)

             _lowerCamelCase			:						List[Any]            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
             _lowerCamelCase			:						Any            =tokenizer.decode(output_non_padded[0]     ,  skip_special_tokens=lowercase_					)
             _lowerCamelCase			:						Any            =tokenizer.decode(output_padded[0]     ,  skip_special_tokens=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =[
                 'Hello, my dog is a little bit bigger than a little bit.',
                 'Today, I have a good idea of how to use the information',
             ]
             self.assertListEqual(lowercase_     ,  lowercase_					)
             self.assertListEqual(lowercase_     ,  [non_padded_sentence, padded_sentence]					)






      @slow
      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		List[str]:
             """simple docstring"""




             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase			:						Dict            =BioGptModel.from_pretrained(lowercase_					)
                    self.assertIsNotNone(lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase			:						List[Any]            =3
             _lowerCamelCase			:						Dict            =input_dict['input_ids']
             _lowerCamelCase			:						Dict            =input_ids.ne(1					).to(lowercase_					)
             _lowerCamelCase			:						Optional[int]            =ids_tensor([self.model_tester.batch_size]     ,  self.model_tester.type_sequence_label_size					)
             _lowerCamelCase			:						Any            =BioGptForSequenceClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  attention_mask=lowercase_     ,  labels=lowercase_					)
             self.assertEqual(result.logits.shape     ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Any            =self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase			:						Union[str, Any]            =3
             _lowerCamelCase			:						Tuple            ='multi_label_classification'
             _lowerCamelCase			:						Tuple            =input_dict['input_ids']
             _lowerCamelCase			:						List[str]            =input_ids.ne(1					).to(lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]     ,  self.model_tester.type_sequence_label_size					).to(torch.float					)
             _lowerCamelCase			:						int            =BioGptForSequenceClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						List[str]            =model(lowercase_     ,  attention_mask=lowercase_     ,  labels=lowercase_					)
             self.assertEqual(result.logits.shape     ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)




@require_torch
class A    (  unittest.TestCase	):






      @slow
      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
             _lowerCamelCase			:						Tuple            =torch.tensor([[2, 4805, 9, 656, 21]]					)
             _lowerCamelCase			:						Any            =model(lowercase_					)[0]

             _lowerCamelCase			:						int            =4_2384

             _lowerCamelCase			:						Tuple            =torch.Size((1, 5, vocab_size)					)
             self.assertEqual(output.shape     ,  lowercase_					)

             _lowerCamelCase			:						Dict            =torch.tensor(
                 [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]					)

             self.assertTrue(torch.allclose(output[:, :3, :3]     ,  lowercase_     ,  atol=1E-4					)					)






      @slow
      def 			lowerCamelCase			(   self		:	Dict					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =BioGptTokenizer.from_pretrained('microsoft/biogpt'					)
             _lowerCamelCase			:						Any            =BioGptForCausalLM.from_pretrained('microsoft/biogpt'					)
             model.to(lowercase_					)

             torch.manual_seed(0					)
             _lowerCamelCase			:						Optional[Any]            =tokenizer('COVID-19 is'     ,  return_tensors='pt'					).to(lowercase_					)
             _lowerCamelCase			:						Optional[int]            =model.generate(
                 **lowercase_     ,  min_length=100     ,  max_length=1024     ,  num_beams=5     ,  early_stopping=lowercase_     ,  )
             _lowerCamelCase			:						Any            =tokenizer.decode(output_ids[0]     ,  skip_special_tokens=lowercase_					)

             _lowerCamelCase			:						int            =(
                 'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
                 ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
                 ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
                 ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
                 ' more than 800,000 deaths.'
             )
             self.assertEqual(lowercase_     ,  lowercase_					)






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCamelCase		   =			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowerCamelCase		   =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def       a_ (       SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       for attribute in key.split('.'	):
              _lowerCamelCase			:						Optional[Any]            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)

       if weight_type is not None:
              _lowerCamelCase			:						str            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	).shape
       else:
              _lowerCamelCase			:						Optional[Any]            =hf_pointer.shape

       assert hf_shape == value.shape, (
           F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )

       if weight_type == "weight":
              _lowerCamelCase			:						Tuple            =value
       elif weight_type == "weight_g":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "weight_v":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "bias":
              _lowerCamelCase			:						Dict            =value
       else:
              _lowerCamelCase			:						Union[str, Any]            =value

       logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''	)




def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : Optional[Any]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[]
       _lowerCamelCase			:						Optional[Any]            =fairseq_model.state_dict()

       _lowerCamelCase			:						Tuple            =hf_model.feature_extractor
       _lowerCamelCase			:						int            =hf_model.adapter

       for name, value in fairseq_dict.items():
              _lowerCamelCase			:						Optional[Any]            =False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     hf_model.config.feat_extract_norm == 'group'						,     )
                     _lowerCamelCase			:						Optional[int]            =True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']	):
                     load_adapter(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
                     _lowerCamelCase			:						int            =True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
                                   _lowerCamelCase			:						Dict            =True
                                   if "*" in mapped_key:
                                          _lowerCamelCase			:						List[str]            =name.split(SCREAMING_SNAKE_CASE__	)[0].split('.'	)[-2]
                                          _lowerCamelCase			:						Dict            =mapped_key.replace('*'						,     SCREAMING_SNAKE_CASE__	)
                                   if "weight_g" in name:
                                          _lowerCamelCase			:						List[Any]            ='weight_g'
                                   elif "weight_v" in name:
                                          _lowerCamelCase			:						int            ='weight_v'
                                   elif "bias" in name:
                                          _lowerCamelCase			:						int            ='bias'
                                   elif "weight" in name:
                                          _lowerCamelCase			:						Optional[int]            ='weight'
                                   else:
                                          _lowerCamelCase			:						Any            =None
                                   set_recursively(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE__	)

       logger.warning(F'''Unused weights: {unused_weights}'''	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





       _lowerCamelCase			:						str            =full_name.split('conv_layers.'	)[-1]
       _lowerCamelCase			:						List[Any]            =name.split('.'	)
       _lowerCamelCase			:						Optional[int]            =int(items[0]	)
       _lowerCamelCase			:						List[Any]            =int(items[1]	)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                     )
                     _lowerCamelCase			:						Any            =value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                     )
                     _lowerCamelCase			:						int            =value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                         " found."
                     )
                     _lowerCamelCase			:						List[Any]            =value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                     )
                     _lowerCamelCase			:						Tuple            =value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Dict	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =full_name.split('adaptor.'	)[-1]
       _lowerCamelCase			:						str            =name.split('.'	)

       if items[1].isdigit():
              _lowerCamelCase			:						Any            =int(items[1]	)
       else:
              _lowerCamelCase			:						int            =None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                            _lowerCamelCase			:						str            =value
                            logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''	)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                            _lowerCamelCase			:						str            =value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                            _lowerCamelCase			:						Optional[Any]            =value
                            logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''	)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                            _lowerCamelCase			:						Optional[Any]            =value
                            logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''	)
       elif isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
                     _lowerCamelCase			:						List[Any]            =value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''	)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
                     _lowerCamelCase			:						Dict            =value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''	)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =emb.weight.shape
       _lowerCamelCase			:						str            =nn.Linear(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     bias=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Optional[Any]            =emb.weight.data
       return lin_layer




@torch.no_grad()
def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     ):
       '''simple docstring'''





       _lowerCamelCase			:						int            =WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__						,     add_adapter=SCREAMING_SNAKE_CASE__						,     adapter_stride=SCREAMING_SNAKE_CASE__						,     adapter_kernel_size=SCREAMING_SNAKE_CASE__						,     use_auth_token=SCREAMING_SNAKE_CASE__						,     output_hidden_size=SCREAMING_SNAKE_CASE__						,     )
       _lowerCamelCase			:						str            =MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)

       # load model
       _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						str            =fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]						,     arg_overrides={
               'config_yaml': config_yaml_path,
               'data': '/'.join(dict_path.split('/'	)[:-1]	),
               'w2v_path': checkpoint_path,
               'load_pretrained_decoder_from': None,
           }						,     )
       _lowerCamelCase			:						Any            =model[0].eval()

       # load feature extractor
       _lowerCamelCase			:						List[Any]            =WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__						,     use_auth_token=SCREAMING_SNAKE_CASE__	)

       # set weights for wav2vec2 encoder
       _lowerCamelCase			:						List[Any]            =WavaVecaModel(SCREAMING_SNAKE_CASE__	)

       recursively_load_weights_wavaveca(model.encoder						,     SCREAMING_SNAKE_CASE__	)

       # load decoder weights
       _lowerCamelCase			:						int            =MBartForCausalLM(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,     strict=SCREAMING_SNAKE_CASE__	)
       logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''	)
       logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''	)

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__						,     decoder=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						List[Any]            =False

       _lowerCamelCase			:						Any            =MBartaaTokenizer(SCREAMING_SNAKE_CASE__	)
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						int            =hf_wavavec.config.to_dict()
       _lowerCamelCase			:						Optional[int]            =tokenizer.pad_token_id
       _lowerCamelCase			:						Tuple            =tokenizer.bos_token_id
       _lowerCamelCase			:						Any            =tokenizer.eos_token_id
       _lowerCamelCase			:						int            ='mbart50'
       _lowerCamelCase			:						Tuple            ='wav2vec2'

       _lowerCamelCase			:						Tuple            =tokenizer.eos_token_id
       _lowerCamelCase			:						Dict            =250_004
       _lowerCamelCase			:						List[str]            =tokenizer.eos_token_id

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__	)

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 lowerCamelCase		   =			argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
 parser.add_argument(
     '--encoder_config_path',
     default='facebook/wav2vec2-xls-r-1b',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/mbart-large-50-one-to-many-mmt',
     type=str,
     help='Path to hf decoder checkpoint config',
 )
 parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
 parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
 parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
 parser.add_argument('--encoder_output_dim', default=10_24, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

 lowerCamelCase		   =			parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )


