

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      UpperCamelCase :

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]=1_3							,    snake_case__ :      Optional[int]=7							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      Any=True							,    snake_case__ :      str=False							,    snake_case__ :      int=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      List[str]=3_2							,    snake_case__ :      int=5							,    snake_case__ :      Dict=4							,    snake_case__ :      int=3_7							,    snake_case__ :      Union[str, Any]="gelu"							,    snake_case__ :      Union[str, Any]=0.1							,    snake_case__ :      Optional[Any]=0.1							,    snake_case__ :      List[str]=5_1_2							,    snake_case__ :      str=1_6							,    snake_case__ :      Any=2							,    snake_case__ :      Tuple=0.02							,    snake_case__ :      Dict=3							,    snake_case__ :      Dict=4							,    snake_case__ :      Union[str, Any]=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 UpperCamelCase			(			self :      str    ):

     """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 UpperCamelCase			(			self :      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=snake_case__							,    initializer_range=self.initializer_range							,    )

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      Dict							,    snake_case__ :      str							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      Dict							,    *snake_case__ :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()

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

     # first forward pass
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    ).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,)							,    snake_case__    ).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=snake_case__    )]							,    dim=1							,    )

     # get two different outputs
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )['last_hidden_state']
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    past_key_values=snake_case__							,    attention_mask=snake_case__    )['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(snake_case__							,    snake_case__							,    atol=1E-3    )    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    *snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptModel(config=snake_case__    ).to(snake_case__    ).eval()

     SCREAMING_SNAKE_CASE								=			torch.ones(input_ids.shape							,    dtype=torch.long							,    device=snake_case__    )

     # first forward pass
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    use_cache=snake_case__    )

     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(snake_case__							,    attention_mask=snake_case__    )['last_hidden_state']
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    past_key_values=snake_case__    )[
         '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(snake_case__							,    snake_case__							,    atol=1E-3    )    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      int							,    snake_case__ :      List[str]							,    snake_case__ :      int							,    snake_case__ :      List[Any]							,    snake_case__ :      int							,    *snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]=False    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptForCausalLM(snake_case__    )
     model.to(snake_case__    )
     if gradient_checkpointing:
      model.gradient_checkpointing_enable()

     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Dict							,    *snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptModel(snake_case__    )
     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    )							,    0.001    )
       self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )							,    0.01    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      Tuple							,    *snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			BioGptForTokenClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
        if is_torch_available()
        else ()
    )
    __UpperCamelCase         =(BioGptForCausalLM,) if is_torch_available() else ()
    __UpperCamelCase         =(
        {
            "feature-extraction": BioGptModel,
            "text-classification": BioGptForSequenceClassification,
            "text-generation": BioGptForCausalLM,
            "token-classification": BioGptForTokenClassification,
            "zero-shot": BioGptForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """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(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_forward_and_backwards(*snake_case__							,    gradient_checkpointing=snake_case__    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case__    )

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case__    )

    def UpperCamelCase			(			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(*snake_case__    )

    @slow
    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
     model.to(snake_case__    )
     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(snake_case__							,    return_tensors='pt'							,    padding=snake_case__    )
     SCREAMING_SNAKE_CASE								=			inputs['input_ids'].to(snake_case__    )

     SCREAMING_SNAKE_CASE								=			model.generate(
         input_ids=snake_case__							,    attention_mask=inputs['attention_mask'].to(snake_case__    )							,    )

     SCREAMING_SNAKE_CASE								=			tokenizer(sentences[0]							,    return_tensors='pt'    ).input_ids.to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			model.generate(input_ids=snake_case__    )

     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(snake_case__    )
     SCREAMING_SNAKE_CASE								=			model.generate(input_ids=snake_case__							,    max_length=model.config.max_length - num_paddings    )

     SCREAMING_SNAKE_CASE								=			tokenizer.batch_decode(snake_case__							,    skip_special_tokens=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.decode(output_non_padded[0]							,    skip_special_tokens=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.decode(output_padded[0]							,    skip_special_tokens=snake_case__    )

     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(snake_case__							,    snake_case__    )
     self.assertListEqual(snake_case__							,    [non_padded_sentence, padded_sentence]    )

    @slow
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[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(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			BioGptForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Optional[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								=			'multi_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     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(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      List[Any]    ):

     """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(snake_case__    )[0]

     SCREAMING_SNAKE_CASE								=			4_2_3_8_4

     SCREAMING_SNAKE_CASE								=			torch.Size((1, 5, vocab_size)    )
     self.assertEqual(output.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BioGptTokenizer.from_pretrained('microsoft/biogpt'    )
     SCREAMING_SNAKE_CASE								=			BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
     model.to(snake_case__    )

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

     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(snake_case__							,    snake_case__    )




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,			_KWARGS_DESCRIPTION   )
class      UpperCamelCase (     datasets.Metric   ):

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
             {
                 'predictions': datasets.Value('string'							,    id='sequence'    ),
                 'references': datasets.Value('string'							,    id='sequence'    ),
             }    )							,    codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

