

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  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Optional[int]=1_3, UpperCAmelCase__							:      str=7, UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      str=True, UpperCAmelCase__							:      Optional[Any]=False, UpperCAmelCase__							:      int=True, UpperCAmelCase__							:      List[str]=9_9, UpperCAmelCase__							:      Union[str, Any]=3_2, UpperCAmelCase__							:      Any=5, UpperCAmelCase__							:      int=4, UpperCAmelCase__							:      List[str]=3_7, UpperCAmelCase__							:      Optional[Any]="gelu", UpperCAmelCase__							:      Any=0.1, UpperCAmelCase__							:      Any=0.1, UpperCAmelCase__							:      Tuple=5_1_2, UpperCAmelCase__							:      Any=1_6, UpperCAmelCase__							:      int=2, UpperCAmelCase__							:      List[str]=0.02, UpperCAmelCase__							:      Dict=3, UpperCAmelCase__							:      Optional[int]=4, UpperCAmelCase__							:      List[Any]=None, ):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_token_type_ids
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						type_vocab_size
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range
    __lowercase								=						num_labels
    __lowercase								=						num_choices
    __lowercase								=						scope


 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

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

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

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

    __lowercase								=						self.get_config()

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


 def  _lowercase			(      self							:      Union[str, Any]     ):
    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=snake_case_, initializer_range=self.initializer_range, )


 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Union[str, Any]     ):
    __lowercase								=						BioGptModel(config=snake_case_     )
    model.to(snake_case_     )
    model.eval()
    __lowercase								=						model(snake_case_, attention_mask=snake_case_     )
    __lowercase								=						model(snake_case_     )
    self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)     )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Optional[Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      int, UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						BioGptForCausalLM(config=snake_case_     )
    model.to(snake_case_     )
    model.eval()
    __lowercase								=						model(snake_case_, attention_mask=snake_case_, token_type_ids=snake_case_, labels=snake_case_     )
    self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Dict, *UpperCAmelCase__							:      Union[str, Any]     ):
    __lowercase								=						BioGptModel(config=snake_case_     )
    model.to(snake_case_     )
    model.eval()

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

    # first forward pass
    __lowercase								=						model(snake_case_, attention_mask=snake_case_     ).to_tuple()

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

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

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

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

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

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


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Optional[Any], UpperCAmelCase__							:      Any, *UpperCAmelCase__							:      Dict     ):
    __lowercase								=						BioGptModel(config=snake_case_     ).to(snake_case_     ).eval()

    __lowercase								=						torch.ones(input_ids.shape, dtype=torch.long, device=snake_case_     )

    # first forward pass
    __lowercase								=						model(snake_case_, attention_mask=snake_case_, use_cache=snake_case_     )

    __lowercase								=						outputs.to_tuple()

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

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

    __lowercase								=						model(snake_case_, attention_mask=snake_case_     )['''last_hidden_state''']
    __lowercase								=						model(snake_case_, attention_mask=snake_case_, past_key_values=snake_case_     )[
        '''last_hidden_state'''
    ]

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


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Optional[Any], *UpperCAmelCase__							:      str, UpperCAmelCase__							:      Optional[Any]=False     ):
    __lowercase								=						BioGptForCausalLM(snake_case_     )
    model.to(snake_case_     )
    if gradient_checkpointing:
       model.gradient_checkpointing_enable()

    __lowercase								=						model(snake_case_, labels=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  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int, *UpperCAmelCase__							:      Any     ):
    __lowercase								=						BioGptModel(snake_case_     )
    __lowercase								=						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  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any], *UpperCAmelCase__							:      Optional[int]     ):
    __lowercase								=						self.num_labels
    __lowercase								=						BioGptForTokenClassification(snake_case_     )
    model.to(snake_case_     )
    model.eval()
    __lowercase								=						model(snake_case_, attention_mask=snake_case_, token_type_ids=snake_case_     )
    self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)     )



 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        __lowercase	
    )								=						config_and_inputs
    __lowercase								=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
    return config, inputs_dict







@require_torch
class  _lowerCAmelCase  (     __lowercase					,__lowercase					,__lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Tuple       		=     (
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 __UpperCAmelCase     : List[Any]       		=     (BioGptForCausalLM,) if is_torch_available() else ()
 __UpperCAmelCase     : List[str]       		=     (
     {
         "feature-extraction": BioGptModel,
         "text-classification": BioGptForSequenceClassification,
         "text-generation": BioGptForCausalLM,
         "token-classification": BioGptForTokenClassification,
         "zero-shot": BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase     : Dict       		=     False


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						BioGptModelTester(self     )
    __lowercase								=						ConfigTester(self, config_class=snake_case_, hidden_size=3_7     )


 def  _lowercase			(      self							:      Optional[int]     ):
    self.config_tester.run_common_tests()


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_     )


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowercase								=						type
       self.model_tester.create_and_check_model(*snake_case_     )


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case_     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_forward_and_backwards(*snake_case_, gradient_checkpointing=snake_case_     )


 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case_     )


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case_     )


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case_     )


 @slow
 def  _lowercase			(      self							:      int     ):
    __lowercase								=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
    model.to(snake_case_     )
    __lowercase								=						BioGptTokenizer.from_pretrained("microsoft/biogpt"     )

    __lowercase								=						'''left'''

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

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

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

    __lowercase								=						model.generate(
        input_ids=snake_case_, attention_mask=inputs["attention_mask"].to(snake_case_     ), )

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

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

    __lowercase								=						tokenizer.batch_decode(snake_case_, skip_special_tokens=snake_case_     )
    __lowercase								=						tokenizer.decode(output_non_padded[0], skip_special_tokens=snake_case_     )
    __lowercase								=						tokenizer.decode(output_padded[0], skip_special_tokens=snake_case_     )

    __lowercase								=						[
        '''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(snake_case_, snake_case_     )
    self.assertListEqual(snake_case_, [non_padded_sentence, padded_sentence]     )


 @slow
 def  _lowercase			(      self							:      Tuple     ):
    for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						BioGptModel.from_pretrained(snake_case_     )
       self.assertIsNotNone(snake_case_     )


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



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







@require_torch
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
    __lowercase								=						torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]     )
    __lowercase								=						model(snake_case_     )[0]

    __lowercase								=						4_2_3_8_4

    __lowercase								=						torch.Size((1, 5, vocab_size)     )
    self.assertEqual(output.shape, snake_case_     )

    __lowercase								=						torch.tensor(
        [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]     )

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



 @slow
 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						BioGptTokenizer.from_pretrained("microsoft/biogpt"     )
    __lowercase								=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
    model.to(snake_case_     )

    torch.manual_seed(0     )
    __lowercase								=						tokenizer("COVID-19 is", return_tensors="pt"     ).to(snake_case_     )
    __lowercase								=						model.generate(
        **snake_case_, min_length=1_0_0, max_length=1_0_2_4, num_beams=5, early_stopping=snake_case_, )
    __lowercase								=						tokenizer.decode(output_ids[0], skip_special_tokens=snake_case_     )

    __lowercase								=						(
        '''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(snake_case_, snake_case_     )







"""simple docstring"""





import numpy

# List of input, output pairs
_a							=							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a							=							(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_a							=							[2, 4, 1, 5]
_a							=							len(train_data)
_a							=							0.009




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(len(UpperCamelCase_) - 1):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''simple docstring'''


   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0])
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0])
   return None




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(UpperCamelCase_):
      if index == -1:
         summation_value += _error(UpperCamelCase_)
      else:
         summation_value += _error(UpperCamelCase_) * train_data[i][0][index]
   return summation_value




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowercase								=						0.000_002
   __lowercase								=						0
   __lowercase								=						0
   while True:
      j += 1
      __lowercase								=						[0, 0, 0, 0]
      for i in range(0,      len(UpperCamelCase_)):
         __lowercase								=						get_cost_derivative(i - 1)
         __lowercase								=						(
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          UpperCamelCase_,      UpperCamelCase_,      atol=UpperCamelCase_,      rtol=UpperCamelCase_,      ):
         break
      __lowercase								=						temp_parameter_vector
   print(("Number of iterations:", j))




def   _A  (  )     ->						int:
   '''simple docstring'''


   for i in range(len(UpperCamelCase_)):
      print(("Actual output value:", output(UpperCamelCase_,      "test")))
      print(("Hypothesis output:", calculate_hypothesis_value(UpperCamelCase_,      "test")))


if __name__ == "__main__":
   run_gradient_descent()
   print('\nTesting gradient descent for a linear hypothesis function.\n')
   test_gradient_descent()




