

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__    )




# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a_    :						str    		=      2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a_    :						int    		=      {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.1_5},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
a_    :						str    		=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      a_    :						List[Any]    		=      "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
      a_    :						Any    		=      "allenai"


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			dict((re.sub(R'@@$'		,							''		,							_UpperCamelCase	), v) if k.endswith('@@'	) else (re.sub(R'$'		,							'</w>'		,							_UpperCamelCase	), v) for k, v in d.items()	)
 SCREAMING_SNAKE_CASE								=			'<s> <pad> </s> <unk>'.split()
 # restore the special tokens
 for k in keep_keys:
  del da[f"""{k}</w>"""]
  SCREAMING_SNAKE_CASE								=			d[k]  # restore
 return da


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[str]	)		->							Dict:
 '''simple docstring'''


 assert os.path.exists(_UpperCamelCase	)
 os.makedirs(_UpperCamelCase		,							exist_ok=_UpperCamelCase	)
 print(f"""Writing results to {pytorch_dump_folder_path}"""	)

 # handle various types of models

 SCREAMING_SNAKE_CASE								=			basename(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			dirname(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 SCREAMING_SNAKE_CASE								=			cls.hub_models()
 SCREAMING_SNAKE_CASE								=			{'bpe': 'fastbpe', 'tokenizer': 'moses'}
 SCREAMING_SNAKE_CASE								=			'.'
 # note: since the model dump is old, fairseq has upgraded its model some
 # time later, and it does a whole lot of rewrites and splits on the saved
 # weights, therefore we can't use torch.load() directly on the model file.
 # see: upgrade_state_dict(state_dict) in fairseq_model.py
 print(f"""using checkpoint {checkpoint_file}"""	)
 SCREAMING_SNAKE_CASE								=			hub_utils.from_pretrained(
     _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							archive_map=_UpperCamelCase		,							**_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			vars(chkpt['args']['model']	)

 SCREAMING_SNAKE_CASE								=			args['source_lang']
 SCREAMING_SNAKE_CASE								=			args['target_lang']

 SCREAMING_SNAKE_CASE								=			dirname(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			basename(_UpperCamelCase	)

 # dicts
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							f"""dict.{src_lang}.txt"""	)
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							f"""dict.{tgt_lang}.txt"""	)

 SCREAMING_SNAKE_CASE								=			Dictionary.load(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			rewrite_dict_keys(src_dict.indices	)
 SCREAMING_SNAKE_CASE								=			len(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'vocab-src.json'	)
 print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""	)
 with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

 # detect whether this is a do_lower_case situation, which can be derived by checking whether we
 # have at least one uppercase letter in the source vocab
 SCREAMING_SNAKE_CASE								=			True
 for k in src_vocab.keys():
  if not k.islower():
   SCREAMING_SNAKE_CASE								=			False
   break

 SCREAMING_SNAKE_CASE								=			Dictionary.load(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			rewrite_dict_keys(tgt_dict.indices	)
 SCREAMING_SNAKE_CASE								=			len(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'vocab-tgt.json'	)
 print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""	)
 with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

 # merges_file (bpecodes)
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							VOCAB_FILES_NAMES['merges_file']	)
 for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
  SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)
  if os.path.exists(_UpperCamelCase	):
   break
 with open(_UpperCamelCase		,							encoding='utf-8'	) as fin:
  SCREAMING_SNAKE_CASE								=			fin.read()
 SCREAMING_SNAKE_CASE								=			re.sub(R' \d+$'		,							''		,							_UpperCamelCase		,							0		,							re.M	)  # remove frequency number
 print(f"""Generating {merges_file}"""	)
 with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as fout:
  fout.write(_UpperCamelCase	)

 # model config
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'config.json'	)

 # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
 # may have to modify the tokenizer if a different type is used by a future model
 assert args["bpe"] == "fastbpe", f"""need to extend tokenizer to support bpe={args['bpe']}"""
 assert args["tokenizer"] == "moses", f"""need to extend tokenizer to support bpe={args['tokenizer']}"""

 SCREAMING_SNAKE_CASE								=			{
     'architectures': ['FSMTForConditionalGeneration'],
     'model_type': 'fsmt',
     'activation_dropout': args['activation_dropout'],
     'activation_function': 'relu',
     'attention_dropout': args['attention_dropout'],
     'd_model': args['decoder_embed_dim'],
     'dropout': args['dropout'],
     'init_std': 0.02,
     'max_position_embeddings': args['max_source_positions'],
     'num_hidden_layers': args['encoder_layers'],
     'src_vocab_size': src_vocab_size,
     'tgt_vocab_size': tgt_vocab_size,
     'langs': [src_lang, tgt_lang],
     'encoder_attention_heads': args['encoder_attention_heads'],
     'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
     'encoder_layerdrop': args['encoder_layerdrop'],
     'encoder_layers': args['encoder_layers'],
     'decoder_attention_heads': args['decoder_attention_heads'],
     'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
     'decoder_layerdrop': args['decoder_layerdrop'],
     'decoder_layers': args['decoder_layers'],
     'bos_token_id': 0,
     'pad_token_id': 1,
     'eos_token_id': 2,
     'is_encoder_decoder': True,
     'scale_embedding': not args['no_scale_embedding'],
     'tie_word_embeddings': args['share_all_embeddings'],
 }

 # good hparam defaults to start with
 SCREAMING_SNAKE_CASE								=			5
 SCREAMING_SNAKE_CASE								=			False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  SCREAMING_SNAKE_CASE								=			best_score_hparams[model_dir]['length_penalty']
 else:
  SCREAMING_SNAKE_CASE								=			1.0

 print(f"""Generating {fsmt_model_config_file}"""	)
 with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

 # tokenizer config
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			{
     'langs': [src_lang, tgt_lang],
     'model_max_length': 10_24,
     'do_lower_case': do_lower_case,
 }

 print(f"""Generating {fsmt_tokenizer_config_file}"""	)
 with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

 # model
 SCREAMING_SNAKE_CASE								=			chkpt['models'][0]
 SCREAMING_SNAKE_CASE								=			model.state_dict()

 # rename keys to start with 'model.'
 SCREAMING_SNAKE_CASE								=			OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()	)

 # remove unneeded keys
 SCREAMING_SNAKE_CASE								=			[
     'model.model',
     'model.encoder.version',
     'model.decoder.version',
     'model.encoder_embed_tokens.weight',
     'model.decoder_embed_tokens.weight',
     'model.encoder.embed_positions._float_tensor',
     'model.decoder.embed_positions._float_tensor',
 ]
 for k in ignore_keys:
  model_state_dict.pop(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			FSMTConfig.from_pretrained(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			FSMTForConditionalGeneration(_UpperCamelCase	)

 # check that it loads ok
 model_new.load_state_dict(_UpperCamelCase		,							strict=_UpperCamelCase	)

 # save
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)
 print(f"""Generating {pytorch_weights_dump_path}"""	)
 torch.save(_UpperCamelCase		,							_UpperCamelCase	)

 print('Conversion is done!'	)
 print('\nLast step is to upload the files to s3'	)
 print(f"""cd {data_root}"""	)
 print(f"""transformers-cli upload {model_dir}"""	)


if __name__ == "__main__":
      a_    :						List[str]    		=      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--fsmt_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help=(
              "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
              " bpecodes, etc."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      a_    :						int    		=      parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

