
'''simple docstring'''





import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         RemBertConfig,
         RemBertForCausalLM,
         RemBertForMaskedLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
         RemBertModel,
     )
     from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			List[Any]						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Dict=13						,			lowerCAmelCase_	:			int=7						,			lowerCAmelCase_	:			List[Any]=True						,			lowerCAmelCase_	:			Optional[int]=True						,			lowerCAmelCase_	:			Tuple=True						,			lowerCAmelCase_	:			List[Any]=True						,			lowerCAmelCase_	:			Any=99						,			lowerCAmelCase_	:			Optional[Any]=32						,			lowerCAmelCase_	:			Optional[int]=18						,			lowerCAmelCase_	:			int=43						,			lowerCAmelCase_	:			Tuple=5						,			lowerCAmelCase_	:			Tuple=4						,			lowerCAmelCase_	:			Union[str, Any]=37						,			lowerCAmelCase_	:			Optional[Any]="gelu"						,			lowerCAmelCase_	:			List[str]=0.1						,			lowerCAmelCase_	:			Optional[Any]=0.1						,			lowerCAmelCase_	:			Union[str, Any]=5_12						,			lowerCAmelCase_	:			Any=16						,			lowerCAmelCase_	:			int=2						,			lowerCAmelCase_	:			Optional[Any]=0.02						,			lowerCAmelCase_	:			Optional[int]=3						,			lowerCAmelCase_	:			Dict=4						,			lowerCAmelCase_	:			Optional[Any]=None						,			) ->							str:
        snake_case_    							=				parent
        snake_case_    							=				batch_size
        snake_case_    							=				seq_length
        snake_case_    							=				is_training
        snake_case_    							=				use_input_mask
        snake_case_    							=				use_token_type_ids
        snake_case_    							=				use_labels
        snake_case_    							=				vocab_size
        snake_case_    							=				hidden_size
        snake_case_    							=				input_embedding_size
        snake_case_    							=				output_embedding_size
        snake_case_    							=				num_hidden_layers
        snake_case_    							=				num_attention_heads
        snake_case_    							=				intermediate_size
        snake_case_    							=				hidden_act
        snake_case_    							=				hidden_dropout_prob
        snake_case_    							=				attention_probs_dropout_prob
        snake_case_    							=				max_position_embeddings
        snake_case_    							=				type_vocab_size
        snake_case_    							=				type_sequence_label_size
        snake_case_    							=				initializer_range
        snake_case_    							=				num_labels
        snake_case_    							=				num_choices
        snake_case_    							=				scope

  def 		_lowerCamelCase						(self	:			str     ) ->							List[Any]:
        snake_case_    							=				ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size     )

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

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

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

        snake_case_    							=				RemBertConfig(
            vocab_size=self.vocab_size						,			hidden_size=self.hidden_size						,			input_embedding_size=self.input_embedding_size						,			output_embedding_size=self.output_embedding_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						,			)

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

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        (
            (
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						
        )    							=				self.prepare_config_and_inputs()

        snake_case_    							=				True
        snake_case_    							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        snake_case_    							=				ids_tensor([self.batch_size, self.seq_length]						,			vocab_size=2     )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]     ) ->							Tuple:
        snake_case_    							=				RemBertModel(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_     )
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)     )

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			int						,			) ->							Optional[int]:
        snake_case_    							=				True
        snake_case_    							=				RemBertModel(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			encoder_attention_mask=lowerCAmelCase_						,			)
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			)
        snake_case_    							=				model(lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_     )
        self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)     )

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]						,			) ->							Union[str, Any]:
        snake_case_    							=				RemBertForCausalLM(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        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_	:			List[Any]						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]     ) ->							str:
        snake_case_    							=				RemBertForMaskedLM(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        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	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Any						,			) ->							Any:
        snake_case_    							=				True
        snake_case_    							=				True
        snake_case_    							=				RemBertForCausalLM(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()

        # first forward pass
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			encoder_attention_mask=lowerCAmelCase_						,			use_cache=lowerCAmelCase_						,			)
        snake_case_    							=				outputs.past_key_values

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

        # append to next input_ids and
        snake_case_    							=				torch.cat([input_ids, next_tokens]						,			dim=-1     )
        snake_case_    							=				torch.cat([input_mask, next_mask]						,			dim=-1     )

        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			encoder_attention_mask=lowerCAmelCase_						,			output_hidden_states=lowerCAmelCase_						,			)["hidden_states"][0]
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			encoder_attention_mask=lowerCAmelCase_						,			past_key_values=lowerCAmelCase_						,			output_hidden_states=lowerCAmelCase_						,			)["hidden_states"][0]

        # select random slice
        snake_case_    							=				ids_tensor((1,)						,			output_from_past.shape[-1]     ).item()
        snake_case_    							=				output_from_no_past[:, -3:, random_slice_idx].detach()
        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_	:			List[Any]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[Any]     ) ->							Optional[int]:
        snake_case_    							=				RemBertForQuestionAnswering(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			start_positions=lowerCAmelCase_						,			end_positions=lowerCAmelCase_						,			)
        self.parent.assertEqual(result.start_logits.shape						,			(self.batch_size, self.seq_length)     )
        self.parent.assertEqual(result.end_logits.shape						,			(self.batch_size, self.seq_length)     )

  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Dict     ) ->							Tuple:
        snake_case_    							=				self.num_labels
        snake_case_    							=				RemBertForSequenceClassification(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			labels=lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)     )

  def 		_lowerCamelCase						(self	:			List[str]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.num_labels
        snake_case_    							=				RemBertForTokenClassification(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        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.num_labels)     )

  def 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[Any]     ) ->							int:
        snake_case_    							=				self.num_choices
        snake_case_    							=				RemBertForMultipleChoice(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				input_ids.unsqueeze(1     ).expand(-1						,			self.num_choices						,			-1     ).contiguous()
        snake_case_    							=				token_type_ids.unsqueeze(1     ).expand(-1						,			self.num_choices						,			-1     ).contiguous()
        snake_case_    							=				input_mask.unsqueeze(1     ).expand(-1						,			self.num_choices						,			-1     ).contiguous()
        snake_case_    							=				model(
            lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			labels=lowerCAmelCase_						,			)
        self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_choices)     )


  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        snake_case_    							=				self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						
        )    							=				config_and_inputs
        snake_case_    							=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_torch
class 			lowerCAmelCase__ (							UpperCamelCase       ,				UpperCamelCase       ,				unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			(
      (
          RemBertModel,
          RemBertForMaskedLM,
          RemBertForCausalLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _UpperCamelCase							=			(RemBertForCausalLM,) if is_torch_available() else ()
  _UpperCamelCase							=			(
      {
          'feature-extraction': RemBertModel,
          'fill-mask': RemBertForMaskedLM,
          'question-answering': RemBertForQuestionAnswering,
          'text-classification': RemBertForSequenceClassification,
          'text-generation': RemBertForCausalLM,
          'token-classification': RemBertForTokenClassification,
          'zero-shot': RemBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        snake_case_    							=				RemBertModelTester(self     )
        snake_case_    							=				ConfigTester(self						,			config_class=lowerCAmelCase_						,			hidden_size=37     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        self.config_tester.run_common_tests()

  def 		_lowerCamelCase						(self	:			str     ) ->							str:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Dict:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              snake_case_    							=				type
              self.model_tester.create_and_check_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							List[str]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							List[Any]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							str:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							str:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Dict:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[Any]:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						(
            snake_case_
        )							,						
        )    							=				self.model_tester.prepare_config_and_inputs_for_decoder()

        snake_case_    							=				None

        self.model_tester.create_and_check_model_as_decoder(
            lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			)


  @slow
  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_    							=				RemBertModel.from_pretrained(lowerCAmelCase_     )
              self.assertIsNotNone(lowerCAmelCase_     )



@require_torch
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  @slow
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Optional[Any]:
        # Test exact values at the last hidden layer
        snake_case_    							=				RemBertModel.from_pretrained("google/rembert"     )
        snake_case_    							=				torch.tensor([[3_12, 5_64_98, 3_13, 21_25, 3_13]]     )
        snake_case_    							=				torch.tensor([[0, 0, 0, 1, 1]]     )
        with torch.no_grad():
              snake_case_    							=				model(lowerCAmelCase_						,			token_type_ids=lowerCAmelCase_						,			output_hidden_states=lowerCAmelCase_     )

        snake_case_    							=				11_52

        snake_case_    							=				torch.Size((1, 5, hidden_size)     )
        self.assertEqual(output["last_hidden_state"].shape						,			lowerCAmelCase_     )

        snake_case_    							=				torch.tensor(
            [
                [
                    [0.0_754, -0.2_022, 0.1_904],
                    [-0.3_354, -0.3_692, -0.4_791],
                    [-0.2_314, -0.6_729, -0.0_749],
                    [-0.0_396, -0.3_105, -0.4_234],
                    [-0.1_571, -0.0_525, 0.5_353],
                ]
            ]     )

        # Running on the original tf implementation gives slightly different results here.
        # Not clear why this variations is present
        # TODO: Find reason for discrepancy
        # expected_original_implementation = [[
        #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
        #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
        #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
        #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
        #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
        # ]]

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








'''simple docstring'''






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.source'''		)     ,							UpperCamelCase__		)
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.target'''		)     ,							UpperCamelCase__		)
    return tmp_dir





class 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase     , check_contents=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''label_smoothing''': 0.0,
           '''early_stopping_patience''': -1,
           '''train_batch_size''': 1,
           '''eval_batch_size''': 2,
           '''max_epochs''': 2,
           '''alpha_mlm''': 0.2,
           '''alpha_ce''': 0.8,
           '''do_predict''': True,
           '''model_name_or_path''': '''sshleifer/tinier_bart''',
           '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
           '''val_check_interval''': 0.5,
       }
       default_updates.update(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
           if v is False or v is None:
               return ""
           if v is True:  # or len(str(v))==0:
               return f'''--{k}'''
           return f'''--{k}={v}'''

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [p for p in contents if p.endswith('''ckpt'''       )]
       assert len(_UpperCAmelCase       ) > 0

       self.assertIn('''test_generations.txt'''     , _UpperCAmelCase       )
       self.assertIn('''test_results.txt'''     , _UpperCAmelCase       )

       # get the following from the module, (we don't have access to `model` here)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    load_json(_UpperCAmelCase       )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase       )
       A							:							Optional[Any]				=    metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     , 0.01       )
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
