




'''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 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Dict=1_3  ,				__lowerCamelCase :     List[str]=7  ,				__lowerCamelCase :     Tuple=True  ,				__lowerCamelCase :     Union[str, Any]=True  ,				__lowerCamelCase :     int=False  ,				__lowerCamelCase :     Tuple=True  ,				__lowerCamelCase :     Any=9_9  ,				__lowerCamelCase :     List[str]=3_2  ,				__lowerCamelCase :     Dict=5  ,				__lowerCamelCase :     Union[str, Any]=4  ,				__lowerCamelCase :     Optional[Any]=3_7  ,				__lowerCamelCase :     List[Any]="gelu"  ,				__lowerCamelCase :     Optional[int]=0.1  ,				__lowerCamelCase :     Optional[int]=0.1  ,				__lowerCamelCase :     str=5_1_2  ,				__lowerCamelCase :     Union[str, Any]=1_6  ,				__lowerCamelCase :     List[str]=2  ,				__lowerCamelCase :     Union[str, Any]=0.0_2  ,				__lowerCamelCase :     str=3  ,				__lowerCamelCase :     Dict=4  ,				__lowerCamelCase :     Optional[int]=None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parent
      _SCREAMING_SNAKE_CASE	=				batch_size
      _SCREAMING_SNAKE_CASE	=				seq_length
      _SCREAMING_SNAKE_CASE	=				is_training
      _SCREAMING_SNAKE_CASE	=				use_input_mask
      _SCREAMING_SNAKE_CASE	=				use_token_type_ids
      _SCREAMING_SNAKE_CASE	=				use_labels
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				type_sequence_label_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				num_labels
      _SCREAMING_SNAKE_CASE	=				num_choices
      _SCREAMING_SNAKE_CASE	=				scope



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size   )

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

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

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

      _SCREAMING_SNAKE_CASE	=				self.get_config()

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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=__lowerCamelCase  ,				initializer_range=self.initializer_range  ,				)



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptModel(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.seq_length, self.hidden_size)   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Any  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptForCausalLM(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptModel(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()

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

      # first forward pass
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase   ).to_tuple()

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

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

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

      # get two different outputs
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )["last_hidden_state"]
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				past_key_values=__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )["last_hidden_state"]

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

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



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     List[str]  ,				*__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptModel(config=__lowerCamelCase   ).to(__lowerCamelCase   ).eval()

      _SCREAMING_SNAKE_CASE	=				torch.ones(input_ids.shape  ,				dtype=torch.long  ,				device=__lowerCamelCase   )

      # first forward pass
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				use_cache=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				outputs.to_tuple()

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

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

      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )["last_hidden_state"]
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				past_key_values=__lowerCamelCase   )[
          "last_hidden_state"
      ]

      # select random slice
      _SCREAMING_SNAKE_CASE	=				ids_tensor((1,)  ,				output_from_past.shape[-1]   ).item()
      _SCREAMING_SNAKE_CASE	=				output_from_no_past[:, -3:, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE	=				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(__lowerCamelCase  ,				__lowerCamelCase  ,				atol=1e-3   )   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[Any]  ,				*__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]=False   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptForCausalLM(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      if gradient_checkpointing:
         model.gradient_checkpointing_enable()

      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.loss.shape  ,				()   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)   )
      result.loss.backward()



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     int  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptModel(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				*__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_labels
      _SCREAMING_SNAKE_CASE	=				BioGptForTokenClassification(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.num_labels)   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

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







@require_torch
class 		lowercase_   (			A					,  A					,  A					,  unittest.TestCase       ):


   """simple docstring"""

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



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptModelTester(self   )
      _SCREAMING_SNAKE_CASE	=				ConfigTester(self  ,				config_class=__lowerCamelCase  ,				hidden_size=3_7   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _SCREAMING_SNAKE_CASE	=				type
         self.model_tester.create_and_check_model(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_forward_and_backwards(*__lowerCamelCase  ,				gradient_checkpointing=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_weight_initialization(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_for_token_classification(*__lowerCamelCase   )



   @slow
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"   )
      model.to(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				BioGptTokenizer.from_pretrained("microsoft/biogpt"   )

      _SCREAMING_SNAKE_CASE	=				"left"

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

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

      _SCREAMING_SNAKE_CASE	=				tokenizer(__lowerCamelCase  ,				return_tensors="pt"  ,				padding=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				inputs["input_ids"].to(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				model.generate(
          input_ids=__lowerCamelCase  ,				attention_mask=inputs["attention_mask"].to(__lowerCamelCase   )  ,				)

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

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

      _SCREAMING_SNAKE_CASE	=				tokenizer.batch_decode(__lowerCamelCase  ,				skip_special_tokens=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer.decode(output_non_padded[0]  ,				skip_special_tokens=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer.decode(output_padded[0]  ,				skip_special_tokens=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				[
          "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(__lowerCamelCase  ,				__lowerCamelCase   )
      self.assertListEqual(__lowerCamelCase  ,				[non_padded_sentence, padded_sentence]   )



   @slow
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE	=				BioGptModel.from_pretrained(__lowerCamelCase   )
         self.assertIsNotNone(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs_for_common()
      _SCREAMING_SNAKE_CASE	=				3
      _SCREAMING_SNAKE_CASE	=				input_dict["input_ids"]
      _SCREAMING_SNAKE_CASE	=				input_ids.ne(1   ).to(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.model_tester.batch_size]  ,				self.model_tester.type_sequence_label_size   )
      _SCREAMING_SNAKE_CASE	=				BioGptForSequenceClassification(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.assertEqual(result.logits.shape  ,				(self.model_tester.batch_size, self.model_tester.num_labels)   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs_for_common()
      _SCREAMING_SNAKE_CASE	=				3
      _SCREAMING_SNAKE_CASE	=				"multi_label_classification"
      _SCREAMING_SNAKE_CASE	=				input_dict["input_ids"]
      _SCREAMING_SNAKE_CASE	=				input_ids.ne(1   ).to(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				ids_tensor(
          [self.model_tester.batch_size, config.num_labels]  ,				self.model_tester.type_sequence_label_size   ).to(torch.float   )
      _SCREAMING_SNAKE_CASE	=				BioGptForSequenceClassification(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.assertEqual(result.logits.shape  ,				(self.model_tester.batch_size, self.model_tester.num_labels)   )







@require_torch
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )[0]

      _SCREAMING_SNAKE_CASE	=				4_2_3_8_4

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

      _SCREAMING_SNAKE_CASE	=				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]  ,				__lowerCamelCase  ,				atol=1e-4   )   )



   @slow
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				BioGptTokenizer.from_pretrained("microsoft/biogpt"   )
      _SCREAMING_SNAKE_CASE	=				BioGptForCausalLM.from_pretrained("microsoft/biogpt"   )
      model.to(__lowerCamelCase   )

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

      _SCREAMING_SNAKE_CASE	=				(
          "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(__lowerCamelCase  ,				__lowerCamelCase   )







'''simple docstring'''



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCamelCase_			=						'<<<<<<< This should probably be modified because it mentions: '

lowerCamelCase_			=						'=======\n>>>>>>>\n'

lowerCamelCase_			=						[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCamelCase_			=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Namespace  )			->			List[Any]:
   return ConvertCommand(args.tfds_path	,					args.datasets_directory  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     ArgumentParser   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parser.add_parser(
          "convert"  ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."  ,				)
      train_parser.add_argument(
          "--tfds_path"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."  ,				)
      train_parser.add_argument(
          "--datasets_directory"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to the HuggingFace Datasets folder."   )
      train_parser.set_defaults(func=__lowerCamelCase   )



   def __init__(   self :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_logger("datasets-cli/converting"   )

      _SCREAMING_SNAKE_CASE	=				tfds_path
      _SCREAMING_SNAKE_CASE	=				datasets_directory



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._tfds_path   )
      elif os.path.isfile(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.dirname(self._tfds_path   )
      else:
         raise ValueError("--tfds_path is neither a directory nor a file. Please check path."   )

      _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._datasets_directory   )

      self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""   )

      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				{}

      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.listdir(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				[os.path.basename(self._tfds_path   )]

      for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )

         if not os.path.isfile(__lowerCamelCase   ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
            self._logger.info("Skipping file"   )
            continue

         with open(__lowerCamelCase  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				f.readlines()

         _SCREAMING_SNAKE_CASE	=				[]
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				[]
         for line in lines:
            _SCREAMING_SNAKE_CASE	=				line

            # Convert imports
            if "import tensorflow.compat.v2 as tf" in out_line:
               continue
            elif "@tfds.core" in out_line:
               continue
            elif "builder=self" in out_line:
               continue
            elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _SCREAMING_SNAKE_CASE	=				"import datasets\n"
            elif "import tensorflow" in out_line:
               # order is important here
               _SCREAMING_SNAKE_CASE	=				""
               continue
            elif "from absl import logging" in out_line:
               _SCREAMING_SNAKE_CASE	=				"from datasets import logging\n"
            elif "getLogger" in out_line:
               _SCREAMING_SNAKE_CASE	=				out_line.replace("getLogger"  ,				"get_logger"   )
            elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
               _SCREAMING_SNAKE_CASE	=				True
               _SCREAMING_SNAKE_CASE	=				list(filter(lambda __lowerCamelCase   : e in out_line  ,				__lowerCamelCase   )   )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase   ) + "\n"   )
               out_lines.append(__lowerCamelCase   )
               out_lines.append(__lowerCamelCase   )
               continue
            else:
               for pattern, replacement in TO_CONVERT:
                  _SCREAMING_SNAKE_CASE	=				re.sub(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

                # Take care of saving utilities (to later move them together with main script)
            if "tensorflow_datasets" in out_line:
               _SCREAMING_SNAKE_CASE	=				re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"  ,				__lowerCamelCase   )
               tfds_imports.extend(imp.strip() for imp in match.group(1   ).split(","   )   )
               _SCREAMING_SNAKE_CASE	=				"from . import " + match.group(1   )

            # Check we have not forget anything
            if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(F"""Error converting {out_line.strip()}"""   )

            if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _SCREAMING_SNAKE_CASE	=				True
            out_lines.append(__lowerCamelCase   )

         if is_builder or "wmt" in f_name:
            # We create a new directory for each dataset
            _SCREAMING_SNAKE_CASE	=				f_name.replace(".py"  ,				""   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            os.makedirs(__lowerCamelCase  ,				exist_ok=__lowerCamelCase   )
            self._logger.info(F"""Adding directory {output_dir}"""   )
            imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}   )
         else:
            # Utilities will be moved at the end
            utils_files.append(__lowerCamelCase   )

         if needs_manual_update:
            with_manual_update.append(__lowerCamelCase   )

         with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as f:
            f.writelines(__lowerCamelCase   )
         self._logger.info(F"""Converted in {output_file}"""   )

      for utils_file in utils_files:
         try:
            _SCREAMING_SNAKE_CASE	=				os.path.basename(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				imports_to_builder_map[f_name.replace(".py"  ,				""   )]
            self._logger.info(F"""Moving {dest_folder} to {utils_file}"""   )
            shutil.copy(__lowerCamelCase  ,				__lowerCamelCase   )
         except KeyError:
            self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""   )

      if with_manual_update:
         for file_path in with_manual_update:
            self._logger.warning(
                F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""   )

