





'''simple docstring'''


import math
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           BioGptForCausalLM,
           BioGptForSequenceClassification,
           BioGptForTokenClassification,
           BioGptModel,
           BioGptTokenizer,
       )
       from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=99      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=None      ,  )     ->				str:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   seq_length
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_input_mask
     _lowerCAmelCase						      =   use_token_type_ids
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   type_vocab_size
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   num_labels
     _lowerCAmelCase						      =   num_choices
     _lowerCAmelCase						      =   scope




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size		)

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

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

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

     _lowerCAmelCase						      =   self.get_config()

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




 def 				_snake_case (						self		)     ->				Optional[int]:
     return BioGptConfig(
         vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  max_position_embeddings=self.max_position_embeddings      ,  type_vocab_size=self.type_vocab_size      ,  is_decoder=_lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   BioGptModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				Tuple:
     _lowerCAmelCase						      =   BioGptForCausalLM(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  *_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   BioGptModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

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

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

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

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

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

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

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  *_lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   BioGptModel(config=_lowerCAmelCase		).to(_lowerCAmelCase		).eval()

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

     # first forward pass
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  use_cache=_lowerCAmelCase		)

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   outputs.to_tuple()

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

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

     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase		)["last_hidden_state"]
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  past_key_values=_lowerCAmelCase		)[
         "last_hidden_state"
     ]

     # select random slice
     _lowerCAmelCase						      =   ids_tensor((1,)      ,  output_from_past.shape[-1]		).item()
     _lowerCAmelCase						      =   output_from_no_past[:, -3:, random_slice_idx].detach()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  *_lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				Optional[int]:
     _lowerCAmelCase						      =   BioGptForCausalLM(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     if gradient_checkpointing:
         model.gradient_checkpointing_enable()

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  *_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   BioGptModel(_lowerCAmelCase		)
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  *_lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.num_labels
     _lowerCAmelCase						      =   BioGptForTokenClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  token_type_ids=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels)		)





 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[int]								=       (
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				int								=       (BioGptForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase    :				str								=       (
     {
         "feature-extraction": BioGptModel,
         "text-classification": BioGptForSequenceClassification,
         "text-generation": BioGptForCausalLM,
         "token-classification": BioGptForTokenClassification,
         "zero-shot": BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase    :				int								=       False




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   BioGptModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				Dict:
     self.config_tester.run_common_tests()




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCAmelCase						      =   type
         self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_forward_and_backwards(*_lowerCAmelCase      ,  gradient_checkpointing=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_weight_initialization(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_for_token_classification(*_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
     model.to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   BioGptTokenizer.from_pretrained("microsoft/biogpt"		)

     _lowerCAmelCase						      =   "left"

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

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

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

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

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

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

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

     _lowerCAmelCase						      =   [
         "Hello, my dog is a little bit bigger than a little bit.",
         "Today, I have a good idea of how to use the information",
     ]
     self.assertListEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase      ,  [non_padded_sentence, padded_sentence]		)




 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   BioGptModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   input_dict["input_ids"]
     _lowerCAmelCase						      =   input_ids.ne(1		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   ids_tensor([self.model_tester.batch_size]      ,  self.model_tester.type_sequence_label_size		)
     _lowerCAmelCase						      =   BioGptForSequenceClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.assertEqual(result.logits.shape      ,  (self.model_tester.batch_size, self.model_tester.num_labels)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   "multi_label_classification"
     _lowerCAmelCase						      =   input_dict["input_ids"]
     _lowerCAmelCase						      =   input_ids.ne(1		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   ids_tensor(
         [self.model_tester.batch_size, config.num_labels]      ,  self.model_tester.type_sequence_label_size		).to(torch.float		)
     _lowerCAmelCase						      =   BioGptForSequenceClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  attention_mask=_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.assertEqual(result.logits.shape      ,  (self.model_tester.batch_size, self.model_tester.num_labels)		)







@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 @slow
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
     _lowerCAmelCase						      =   torch.tensor([[2, 4805, 9, 656, 21]]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)[0]

     _lowerCAmelCase						      =   42384

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

     _lowerCAmelCase						      =   torch.tensor(
         [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]		)

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





 @slow
 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   BioGptTokenizer.from_pretrained("microsoft/biogpt"		)
     _lowerCAmelCase						      =   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
     model.to(_lowerCAmelCase		)

     torch.manual_seed(0		)
     _lowerCAmelCase						      =   tokenizer("COVID-19 is"      ,  return_tensors="pt"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(
         **_lowerCAmelCase      ,  min_length=100      ,  max_length=1024      ,  num_beams=5      ,  early_stopping=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   tokenizer.decode(output_ids[0]      ,  skip_special_tokens=_lowerCAmelCase		)

     _lowerCAmelCase						      =   (
         "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
         " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
         " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
         " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
         " more than 800,000 deaths."
     )
     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)






'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
    # fmt: on
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['''vocab_file'''] )
    with open(self.vocab_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       fp.write(json.dumps(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor_add_kwargs.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       return_tensors='''np''' )

    for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()			,       input_processor[key].sum()			,       delta=1e-2 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       ['''pixel_values''', '''labels'''] )

    # test if it raises when no input is passed
    with pytest.raises(__lowerCamelCase ):
       processor()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       processor.model_input_names )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

    self.assertListEqual(list(results.keys() )			,       ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] )



