


"""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     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=13       ,  __a=7       ,  __a=True       ,  __a=True       ,  __a=False       ,  __a=True       ,  __a=99       ,  __a=32       ,  __a=5       ,  __a=4       ,  __a=37       ,  __a="gelu"       ,  __a=0.1       ,  __a=0.1       ,  __a=5_12       ,  __a=16       ,  __a=2       ,  __a=0.0_2       ,  __a=3       ,  __a=4       ,  __a=None       ,  ):
										__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      ):
										__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      ):
										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=__a       ,  initializer_range=self.initializer_range       ,  )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     BioGptModel(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a      )
										__lowerCAmelCase							       =     model(__a      )
										self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  ):
										__lowerCAmelCase							       =     BioGptForCausalLM(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  token_type_ids=__a       ,  labels=__a      )
										self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  *__a      ):
										__lowerCAmelCase							       =     BioGptModel(config=__a      )
										model.to(__a      )
										model.eval()

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

										# first forward pass
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     model(__a       ,  attention_mask=__a      ).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,)       ,  __a      ).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=__a      )]       ,  dim=1       ,  )

										# get two different outputs
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a      )["last_hidden_state"]
										__lowerCAmelCase							       =     model(__a       ,  past_key_values=__a       ,  attention_mask=__a      )["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(__a       ,  __a       ,  atol=1e-3      )      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  *__a      ):
										__lowerCAmelCase							       =     BioGptModel(config=__a      ).to(__a      ).eval()

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

										# first forward pass
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  use_cache=__a      )

										__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(__a       ,  attention_mask=__a      )["last_hidden_state"]
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  past_key_values=__a      )[
										    "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(__a       ,  __a       ,  atol=1e-3      )      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  *__a       ,  __a=False      ):
										__lowerCAmelCase							       =     BioGptForCausalLM(__a      )
										model.to(__a      )
										if gradient_checkpointing:
													model.gradient_checkpointing_enable()

										__lowerCAmelCase							       =     model(__a       ,  labels=__a      )
										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       ,  __a       ,  *__a      ):
										__lowerCAmelCase							       =     BioGptModel(__a      )
										__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      )       ,  0.0_0_1      )
																self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )       ,  0.0_1      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  *__a      ):
										__lowerCAmelCase							       =     self.num_labels
										__lowerCAmelCase							       =     BioGptForTokenClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  token_type_ids=__a      )
										self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)      )



							def      snake_case							(	self      ):
										__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     _UpperCamelCase    (  lowerCAmelCase__			,lowerCAmelCase__			,lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =(
							    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase		:       Union[str, Any]							 =(BioGptForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase		:       Any							 =(
							    {
							        """feature-extraction""": BioGptModel,
							        """text-classification""": BioGptForSequenceClassification,
							        """text-generation""": BioGptForCausalLM,
							        """token-classification""": BioGptForTokenClassification,
							        """zero-shot""": BioGptForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase		:       List[str]							 =False

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     BioGptModelTester(self      )
										__lowerCAmelCase							       =     ConfigTester(self       ,  config_class=__a       ,  hidden_size=37      )

							def      snake_case							(	self      ):
										self.config_tester.run_common_tests()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a      )

							def      snake_case							(	self      ):
										__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(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*__a       ,  gradient_checkpointing=__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*__a      )

							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
										model.to(__a      )
										__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(__a       ,  return_tensors="pt"       ,  padding=__a      )
										__lowerCAmelCase							       =     inputs["input_ids"].to(__a      )

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

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

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

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

										__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(__a       ,  __a      )
										self.assertListEqual(__a       ,  [non_padded_sentence, padded_sentence]      )

							@slow
							def      snake_case							(	self      ):
										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase							       =     BioGptModel.from_pretrained(__a      )
													self.assertIsNotNone(__a      )

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



							def      snake_case							(	self      ):
										__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(__a      )
										__lowerCAmelCase							       =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size      ).to(torch.float      )
										__lowerCAmelCase							       =     BioGptForSequenceClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  labels=__a      )
										self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)      )
@require_torch
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
										__lowerCAmelCase							       =     torch.tensor([[2, 48_05, 9, 6_56, 21]]      )
										__lowerCAmelCase							       =     model(__a      )[0]

										__lowerCAmelCase							       =     4_23_84

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

										__lowerCAmelCase							       =     torch.tensor(
										    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]      )

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



							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     BioGptTokenizer.from_pretrained("microsoft/biogpt"      )
										__lowerCAmelCase							       =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"      )
										model.to(__a      )

										torch.manual_seed(0      )
										__lowerCAmelCase							       =     tokenizer("COVID-19 is"       ,  return_tensors="pt"      ).to(__a      )
										__lowerCAmelCase							       =     model.generate(
										    **__a       ,  min_length=1_00       ,  max_length=10_24       ,  num_beams=5       ,  early_stopping=__a       ,  )
										__lowerCAmelCase							       =     tokenizer.decode(output_ids[0]       ,  skip_special_tokens=__a      )

										__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(__a       ,  __a      )








"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


A	:  Optional[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  **__a      ):
										warnings.warn(
										    "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
										    " use YolosImageProcessor instead."       ,  __a       ,  )
										super().__init__(*__a       ,  **__a      )




