






'''simple docstring'''

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    ,	_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=512    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   Any:



        """simple docstring"""






        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 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        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 						A__		(   self   )						->   List[Any]:



        """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=_SCREAMING_SNAKE_CASE    ,	initializer_range=self.initializer_range    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        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 						A__		(   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    ,	)						->   str:



        """simple docstring"""






        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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        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						,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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        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						,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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Optional[Any]:



        """simple docstring"""






        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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        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   )    ,	0.0_0_1   )
                    self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]   ) - 0.0   )    ,	0.0_1   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        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 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,
        )       						=      config_and_inputs
        UpperCamelCase       						=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (
      (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
      if is_torch_available()
      else ()
  )
  lowercase  	=   (BioGptForCausalLM,) if is_torch_available() else ()
  lowercase  	=   (
      {
          """feature-extraction""": BioGptModel,
          """text-classification""": BioGptForSequenceClassification,
          """text-generation""": BioGptForCausalLM,
          """token-classification""": BioGptForTokenClassification,
          """zero-shot""": BioGptForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase  	=   False

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      BioGptModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	hidden_size=37   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        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 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        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 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_for_token_classification(*_SCREAMING_SNAKE_CASE   )

  @slow
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        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 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase						,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 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase						,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       ):

  @slow
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""   )
        UpperCamelCase       						=      torch.tensor([[2, 4805, 9, 656, 21]]   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )[0]

        UpperCamelCase       						=      42384

        UpperCamelCase       						=      torch.Size((1, 5, vocab_size)   )
        self.assertEqual(output.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor(
            [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]   )

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


  @slow
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        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=100    ,	max_length=1024    ,	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   )








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  UpperCamelCase       						=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
                  UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      old_tensor.split(channels // num_heads	,		dim=1					)

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      path["""new"""]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase       						=      new_path.replace(replacement["""old"""]	,		replacement["""new"""]					)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      {}

      UpperCamelCase       						=      checkpoint["""time_embed.0.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.0.bias"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.bias"""]

      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase       						=      checkpoint["""out.0.weight"""]
      UpperCamelCase       						=      checkpoint["""out.0.bias"""]
      UpperCamelCase       						=      checkpoint["""out.2.weight"""]
      UpperCamelCase       						=      checkpoint["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

            if F"input_blocks.{i}.0.op.weight" in checkpoint:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      F"input_blocks.{i}.1.qkv.bias": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      F"input_blocks.{i}.1.qkv.weight": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """middle_block.1.qkv.bias""": {
              """key""": """mid_block.attentions.0.key.bias""",
              """query""": """mid_block.attentions.0.query.bias""",
              """value""": """mid_block.attentions.0.value.bias""",
          },
          """middle_block.1.qkv.weight""": {
              """key""": """mid_block.attentions.0.key.weight""",
              """query""": """mid_block.attentions.0.query.weight""",
              """value""": """mid_block.attentions.0.value.weight""",
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            F"output_blocks.{i}.1.qkv.bias": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            F"output_blocks.{i}.1.qkv.weight": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE__											=     json.loads(f.read())

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          SCREAMING_SNAKE_CASE__											=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          SCREAMING_SNAKE_CASE__											=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)
