
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:

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=13       ,			SCREAMING_SNAKE_CASE_=7       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=False       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=99       ,			SCREAMING_SNAKE_CASE_=32       ,			SCREAMING_SNAKE_CASE_=5       ,			SCREAMING_SNAKE_CASE_=4       ,			SCREAMING_SNAKE_CASE_=37       ,			SCREAMING_SNAKE_CASE_="gelu"       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=5_12       ,			SCREAMING_SNAKE_CASE_=16       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=0.02       ,			SCREAMING_SNAKE_CASE_=3       ,			SCREAMING_SNAKE_CASE_=4       ,			SCREAMING_SNAKE_CASE_=None       ,			):
       UpperCamelCase__				=				parent
       UpperCamelCase__				=				batch_size
       UpperCamelCase__				=				seq_length
       UpperCamelCase__				=				is_training
       UpperCamelCase__				=				use_input_mask
       UpperCamelCase__				=				use_token_type_ids
       UpperCamelCase__				=				use_labels
       UpperCamelCase__				=				vocab_size
       UpperCamelCase__				=				hidden_size
       UpperCamelCase__				=				num_hidden_layers
       UpperCamelCase__				=				num_attention_heads
       UpperCamelCase__				=				intermediate_size
       UpperCamelCase__				=				hidden_act
       UpperCamelCase__				=				hidden_dropout_prob
       UpperCamelCase__				=				attention_probs_dropout_prob
       UpperCamelCase__				=				max_position_embeddings
       UpperCamelCase__				=				type_vocab_size
       UpperCamelCase__				=				type_sequence_label_size
       UpperCamelCase__				=				initializer_range
       UpperCamelCase__				=				num_labels
       UpperCamelCase__				=				num_choices
       UpperCamelCase__				=				scope







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size						)

       UpperCamelCase__				=				None
       if self.use_input_mask:
            UpperCamelCase__				=				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCamelCase__				=				None
       if self.use_token_type_ids:
            UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size						)

       UpperCamelCase__				=				None
       UpperCamelCase__				=				None
       UpperCamelCase__				=				None
       if self.use_labels:
            UpperCamelCase__				=				ids_tensor([self.batch_size]       ,			self.type_sequence_label_size						)
            UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.num_labels						)
            UpperCamelCase__				=				ids_tensor([self.batch_size]       ,			self.num_choices						)

       UpperCamelCase__				=				self.get_config()

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







  def        UpperCAmelCase_					(self						):
       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=SCREAMING_SNAKE_CASE_       ,			initializer_range=self.initializer_range       ,			)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				BioGptModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			):
       UpperCamelCase__				=				BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			token_type_ids=SCREAMING_SNAKE_CASE_       ,			labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				BioGptModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

       # create attention mask
       UpperCamelCase__				=				torch.ones(input_ids.shape       ,			dtype=torch.long       ,			device=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				self.seq_length // 2
       UpperCamelCase__				=				0

       # first forward pass
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_						).to_tuple()

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

       # change a random masked slice from input_ids
       UpperCamelCase__				=				ids_tensor((1,)       ,			SCREAMING_SNAKE_CASE_						).item() + 1
       UpperCamelCase__				=				ids_tensor((self.batch_size, 1)       ,			config.vocab_size						).squeeze(-1						)
       UpperCamelCase__				=				random_other_next_tokens

       # append to next input_ids and attn_mask
       UpperCamelCase__				=				torch.cat([input_ids, next_tokens]       ,			dim=-1						)
       UpperCamelCase__				=				torch.cat(
           [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,			dtype=torch.long       ,			device=SCREAMING_SNAKE_CASE_						)]       ,			dim=1       ,			)

       # get two different outputs
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_						)['last_hidden_state']
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			past_key_values=SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_						)['last_hidden_state']

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

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







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				BioGptModel(config=SCREAMING_SNAKE_CASE_						).to(SCREAMING_SNAKE_CASE_						).eval()

       UpperCamelCase__				=				torch.ones(input_ids.shape       ,			dtype=torch.long       ,			device=SCREAMING_SNAKE_CASE_						)

       # first forward pass
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			use_cache=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCamelCase__				=				ids_tensor((self.batch_size, 3)       ,			config.vocab_size						)
       UpperCamelCase__				=				ids_tensor((self.batch_size, 3)       ,			2						)

       # append to next input_ids and
       UpperCamelCase__				=				torch.cat([input_ids, next_tokens]       ,			dim=-1						)
       UpperCamelCase__				=				torch.cat([attention_mask, next_attn_mask]       ,			dim=-1						)

       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_						)['last_hidden_state']
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			past_key_values=SCREAMING_SNAKE_CASE_						)[
           'last_hidden_state'
       ]

       # select random slice
       UpperCamelCase__				=				ids_tensor((1,)       ,			output_from_past.shape[-1]						).item()
       UpperCamelCase__				=				output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase__				=				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(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			atol=1E-3						)						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=False						):
       UpperCamelCase__				=				BioGptForCausalLM(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			labels=SCREAMING_SNAKE_CASE_						)
       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        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				BioGptModel(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				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        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			*SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				self.num_labels
       UpperCamelCase__				=				BioGptForTokenClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			token_type_ids=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels)						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.prepare_config_and_inputs()
       (
           UpperCamelCase__							
       )				=				config_and_inputs
       UpperCamelCase__				=				{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict






@require_torch
class 	__A(				__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			(
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			    =			(BioGptForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__			    =			(
      {
          '''feature-extraction''': BioGptModel,
          '''text-classification''': BioGptForSequenceClassification,
          '''text-generation''': BioGptForCausalLM,
          '''token-classification''': BioGptForTokenClassification,
          '''zero-shot''': BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__			    =			False







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				BioGptModelTester(self						)
       UpperCamelCase__				=				ConfigTester(self       ,			config_class=SCREAMING_SNAKE_CASE_       ,			hidden_size=37						)







  def        UpperCAmelCase_					(self						):
       self.config_tester.run_common_tests()







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCamelCase__				=				type
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_       ,			gradient_checkpointing=SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_						)







  @slow
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       model.to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)

       UpperCamelCase__				=				'left'

       # Define PAD Token = EOS Token = 50256
       UpperCamelCase__				=				tokenizer.eos_token
       UpperCamelCase__				=				model.config.eos_token_id

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

       UpperCamelCase__				=				tokenizer(SCREAMING_SNAKE_CASE_       ,			return_tensors="""pt"""       ,			padding=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				inputs['input_ids'].to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				model.generate(
           input_ids=SCREAMING_SNAKE_CASE_       ,			attention_mask=inputs["""attention_mask"""].to(SCREAMING_SNAKE_CASE_						)       ,			)

       UpperCamelCase__				=				tokenizer(sentences[0]       ,			return_tensors="""pt"""						).input_ids.to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				model.generate(input_ids=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
       UpperCamelCase__				=				tokenizer(sentences[1]       ,			return_tensors="""pt"""						).input_ids.to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				model.generate(input_ids=SCREAMING_SNAKE_CASE_       ,			max_length=model.config.max_length - num_paddings						)

       UpperCamelCase__				=				tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,			skip_special_tokens=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				tokenizer.decode(output_non_padded[0]       ,			skip_special_tokens=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				tokenizer.decode(output_padded[0]       ,			skip_special_tokens=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				[
           '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(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)
       self.assertListEqual(SCREAMING_SNAKE_CASE_       ,			[non_padded_sentence, padded_sentence]						)







  @slow
  def        UpperCAmelCase_					(self						):

       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=				BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__				=				3
       UpperCamelCase__				=				input_dict['input_ids']
       UpperCamelCase__				=				input_ids.ne(1						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				ids_tensor([self.model_tester.batch_size]       ,			self.model_tester.type_sequence_label_size						)
       UpperCamelCase__				=				BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			labels=SCREAMING_SNAKE_CASE_						)
       self.assertEqual(result.logits.shape       ,			(self.model_tester.batch_size, self.model_tester.num_labels)						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__				=				3
       UpperCamelCase__				=				'multi_label_classification'
       UpperCamelCase__				=				input_dict['input_ids']
       UpperCamelCase__				=				input_ids.ne(1						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				ids_tensor(
           [self.model_tester.batch_size, config.num_labels]       ,			self.model_tester.type_sequence_label_size						).to(torch.float						)
       UpperCamelCase__				=				BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_       ,			attention_mask=SCREAMING_SNAKE_CASE_       ,			labels=SCREAMING_SNAKE_CASE_						)
       self.assertEqual(result.logits.shape       ,			(self.model_tester.batch_size, self.model_tester.num_labels)						)






@require_torch
class 	__A(				unittest.TestCase ):

  """simple docstring"""







  @slow
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       UpperCamelCase__				=				torch.tensor([[2, 48_05, 9, 6_56, 21]]						)
       UpperCamelCase__				=				model(SCREAMING_SNAKE_CASE_						)[0]

       UpperCamelCase__				=				4_23_84

       UpperCamelCase__				=				torch.Size((1, 5, vocab_size)						)
       self.assertEqual(output.shape       ,			SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				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]       ,			SCREAMING_SNAKE_CASE_       ,			atol=1E-4						)						)







  @slow
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)
       UpperCamelCase__				=				BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       model.to(SCREAMING_SNAKE_CASE_						)

       torch.manual_seed(0						)
       UpperCamelCase__				=				tokenizer("""COVID-19 is"""       ,			return_tensors="""pt"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=				model.generate(
           **SCREAMING_SNAKE_CASE_       ,			min_length=1_00       ,			max_length=10_24       ,			num_beams=5       ,			early_stopping=SCREAMING_SNAKE_CASE_       ,			)
       UpperCamelCase__				=				tokenizer.decode(output_ids[0]       ,			skip_special_tokens=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				(
           '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(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						)












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


