
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 :			Dict							,A :			Optional[int]							,A :			Optional[int]=13							,A :			str=7							,A :			str=True							,A :			int=True							,A :			Optional[Any]=False							,A :			List[Any]=True							,A :			Tuple=99							,A :			Optional[int]=32							,A :			Dict=5							,A :			List[str]=4							,A :			Any=37							,A :			List[str]="gelu"							,A :			List[Any]=0.1							,A :			Union[str, Any]=0.1							,A :			Optional[Any]=5_12							,A :			Any=16							,A :			Union[str, Any]=2							,A :			Optional[Any]=0.02							,A :			Dict=3							,A :			Tuple=4							,A :			Optional[int]=None							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			seq_length
					__A             =			is_training
					__A             =			use_input_mask
					__A             =			use_token_type_ids
					__A             =			use_labels
					__A             =			vocab_size
					__A             =			hidden_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			intermediate_size
					__A             =			hidden_act
					__A             =			hidden_dropout_prob
					__A             =			attention_probs_dropout_prob
					__A             =			max_position_embeddings
					__A             =			type_vocab_size
					__A             =			type_sequence_label_size
					__A             =			initializer_range
					__A             =			num_labels
					__A             =			num_choices
					__A             =			scope






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size			)

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

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

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

					__A             =			self.get_config()

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






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					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     UpperCamelCase_  (	self :			int							,A :			int							,A :			Optional[int]							,A :			int							,A :			int							,A :			Union[str, Any]							,A :			Dict							,A :			List[Any]			):
					__A             =			BioGptModel(config=A			)
					model.to(A			)
					model.eval()
					__A             =			model(A							,attention_mask=A			)
					__A             =			model(A			)
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Optional[int]							,A :			Optional[Any]							,A :			List[str]							,A :			Optional[Any]							,A :			str							,A :			Optional[int]							,A :			Optional[Any]							,A :			List[Any]							,A :			Optional[int]							,):
					__A             =			BioGptForCausalLM(config=A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			Any							,A :			Union[str, Any]							,A :			Union[str, Any]							,A :			Dict							,A :			Any							,A :			int							,*A :			Dict			):
					__A             =			BioGptModel(config=A			)
					model.to(A			)
					model.eval()

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

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

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

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

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

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

					# select random slice
					__A             =			ids_tensor((1,)							,output_from_past.shape[-1]			).item()
					__A             =			output_from_no_past[:, -1, random_slice_idx].detach()
					__A             =			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     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Dict							,A :			Union[str, Any]							,A :			Tuple							,A :			Any							,A :			str							,*A :			int			):
					__A             =			BioGptModel(config=A			).to(A			).eval()

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

					# first forward pass
					__A             =			model(A							,attention_mask=A							,use_cache=A			)

					__A      ,							__A             =			outputs.to_tuple()

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

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

					__A             =			model(A							,attention_mask=A			)["last_hidden_state"]
					__A             =			model(A							,attention_mask=A							,past_key_values=A			)[
					    "last_hidden_state"
					]

					# select random slice
					__A             =			ids_tensor((1,)							,output_from_past.shape[-1]			).item()
					__A             =			output_from_no_past[:, -3:, random_slice_idx].detach()
					__A             =			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     UpperCamelCase_  (	self :			Optional[Any]							,A :			Optional[int]							,A :			Optional[Any]							,A :			Tuple							,A :			List[str]							,A :			List[Any]							,*A :			List[Any]							,A :			Union[str, Any]=False			):
					__A             =			BioGptForCausalLM(A			)
					model.to(A			)
					if gradient_checkpointing:
									model.gradient_checkpointing_enable()

					__A             =			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     UpperCamelCase_  (	self :			Any							,A :			Any							,*A :			List[str]			):
					__A             =			BioGptModel(A			)
					__A             =			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_01			)
													self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]			) - 0.0			)							,0.01			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			Tuple							,A :			Optional[Any]							,A :			List[Any]							,A :			List[str]							,A :			Optional[int]							,*A :			Any			):
					__A             =			self.num_labels
					__A             =			BioGptForTokenClassification(A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.prepare_config_and_inputs()
					(
					    (
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							
					)             =			config_and_inputs
					__A             =			{"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  ):




	'''simple docstring'''





	snake_case_    =					(
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	snake_case_    =					(BioGptForCausalLM,) if is_torch_available() else ()
	snake_case_    =					(
	    {
	        "feature-extraction": BioGptModel,
	        "text-classification": BioGptForSequenceClassification,
	        "text-generation": BioGptForCausalLM,
	        "token-classification": BioGptForTokenClassification,
	        "zero-shot": BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			BioGptModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,hidden_size=37			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					self.config_tester.run_common_tests()






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									__A             =			type
									self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A			)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_forward_and_backwards(*A							,gradient_checkpointing=A			)






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A			)






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_weight_initialization(*A			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_for_token_classification(*A			)






	@slow
	def     UpperCamelCase_  (	self :			str			):
					__A             =			BioGptForCausalLM.from_pretrained("microsoft/biogpt"			)
					model.to(A			)
					__A             =			BioGptTokenizer.from_pretrained("microsoft/biogpt"			)

					__A             =			"left"

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

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

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

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

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

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

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

					__A             =			[
					    "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     UpperCamelCase_  (	self :			List[str]			):
					for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A             =			BioGptModel.from_pretrained(A			)
									self.assertIsNotNone(A			)






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







	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()
					__A             =			3
					__A             =			"multi_label_classification"
					__A             =			input_dict["input_ids"]
					__A             =			input_ids.ne(1			).to(A			)
					__A             =			ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]							,self.model_tester.type_sequence_label_size			).to(torch.float			)
					__A             =			BioGptForSequenceClassification(A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			Dict			):
					__A             =			BioGptForCausalLM.from_pretrained("microsoft/biogpt"			)
					__A             =			torch.tensor([[2, 48_05, 9, 6_56, 21]]			)
					__A             =			model(A			)[0]

					__A             =			4_23_84

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

					__A             =			torch.tensor(
					    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]			)

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







	@slow
	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			BioGptTokenizer.from_pretrained("microsoft/biogpt"			)
					__A             =			BioGptForCausalLM.from_pretrained("microsoft/biogpt"			)
					model.to(A			)

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

					__A             =			(
					    "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			)


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					CTRLTokenizer
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Dict			):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A             =			["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
					__A             =			dict(zip(A							,range(len(A			)			)			)			)
					__A             =			["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
					__A             =			{"unk_token": "<unk>"}

					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"]			)
					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["merges_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write(json.dumps(A			) + "\n"			)
					with open(self.merges_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write("\n".join(A			)			)






	def     UpperCamelCase_  (	self :			List[str]							,**A :			List[str]			):
					kwargs.update(self.special_tokens_map			)
					return CTRLTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Tuple			):
					__A             =			"adapt react readapt apt"
					__A             =			"adapt react readapt apt"
					return input_text, output_text







	def     UpperCamelCase_  (	self :			Any			):
					__A             =			CTRLTokenizer(self.vocab_file							,self.merges_file							,**self.special_tokens_map			)
					__A             =			"adapt react readapt apt"
					__A             =			"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
					__A             =			tokenizer.tokenize(A			)
					self.assertListEqual(A							,A			)

					__A             =			tokens + [tokenizer.unk_token]

					__A             =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)							,A			)
