

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       __snake_case  :

		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=512						,		_A=16						,		_A=2						,		_A=0.0_2						,		_A=3						,		_A=4						,		_A=None						,		):
						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 						lowerCAmelCase__     (					self):
						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 						lowerCAmelCase__     (					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=_SCREAMING_SNAKE_CASE						,		initializer_range=self.initializer_range						,		)

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						BioGptModel(config=_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE)
						self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		):
						SCREAMING_SNAKE_CASE_									=						BioGptForCausalLM(config=_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		token_type_ids=_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_SNAKE_CASE)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A):
						SCREAMING_SNAKE_CASE_									=						BioGptModel(config=_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()

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

						# first forward pass
						SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_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,)						,		_SCREAMING_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=_SCREAMING_SNAKE_CASE)]						,		dim=1						,		)

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

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A):
						SCREAMING_SNAKE_CASE_									=						BioGptModel(config=_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE).eval()

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

						# first forward pass
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		use_cache=_SCREAMING_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(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE)['last_hidden_state']
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		past_key_values=_SCREAMING_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(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		atol=1E-3))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A						,		_A=False):
						SCREAMING_SNAKE_CASE_									=						BioGptForCausalLM(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						if gradient_checkpointing:
										model.gradient_checkpointing_enable()

						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_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 						lowerCAmelCase__     (					self						,		_A						,		*_A):
						SCREAMING_SNAKE_CASE_									=						BioGptModel(_SCREAMING_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)						,		0.0_0_1)
														self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)						,		0.0_1)

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		*_A):
						SCREAMING_SNAKE_CASE_									=						self.num_labels
						SCREAMING_SNAKE_CASE_									=						BioGptForTokenClassification(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		token_type_ids=_SCREAMING_SNAKE_CASE)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.num_labels))


		def 						lowerCAmelCase__     (					self):
						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       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , unittest.TestCase       ):
		__lowerCAmelCase     :	Union[str, Any]						 =  (
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		__lowerCAmelCase     :	List[str]						 =  (BioGptForCausalLM,) if is_torch_available() else ()
		__lowerCAmelCase     :	int						 =  (
		    {
		        'feature-extraction': BioGptModel,
		        'text-classification': BioGptForSequenceClassification,
		        'text-generation': BioGptForCausalLM,
		        'token-classification': BioGptForTokenClassification,
		        'zero-shot': BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowerCAmelCase     :	List[Any]						 =  False

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						BioGptModelTester(self)
						SCREAMING_SNAKE_CASE_									=						ConfigTester(self						,		config_class=_SCREAMING_SNAKE_CASE						,		hidden_size=37)

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

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						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(*_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*_SCREAMING_SNAKE_CASE						,		gradient_checkpointing=_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*_SCREAMING_SNAKE_CASE)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*_SCREAMING_SNAKE_CASE)

		@slow
		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						BioGptForCausalLM.from_pretrained('microsoft/biogpt')
						model.to(_SCREAMING_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(_SCREAMING_SNAKE_CASE						,		return_tensors='pt'						,		padding=_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						inputs['input_ids'].to(_SCREAMING_SNAKE_CASE)

						SCREAMING_SNAKE_CASE_									=						model.generate(
						    input_ids=_SCREAMING_SNAKE_CASE						,		attention_mask=inputs['attention_mask'].to(_SCREAMING_SNAKE_CASE)						,		)

						SCREAMING_SNAKE_CASE_									=						tokenizer(sentences[0]						,		return_tensors='pt').input_ids.to(_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						model.generate(input_ids=_SCREAMING_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(_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						model.generate(input_ids=_SCREAMING_SNAKE_CASE						,		max_length=model.config.max_length - num_paddings)

						SCREAMING_SNAKE_CASE_									=						tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						,		skip_special_tokens=_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						tokenizer.decode(output_non_padded[0]						,		skip_special_tokens=_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						tokenizer.decode(output_padded[0]						,		skip_special_tokens=_SCREAMING_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(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE)
						self.assertListEqual(_SCREAMING_SNAKE_CASE						,		[non_padded_sentence, padded_sentence])

		@slow
		def 						lowerCAmelCase__     (					self):






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

		def 						lowerCAmelCase__     (					self):
						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(_SCREAMING_SNAKE_CASE)
						SCREAMING_SNAKE_CASE_									=						ids_tensor([self.model_tester.batch_size]						,		self.model_tester.type_sequence_label_size)
						SCREAMING_SNAKE_CASE_									=						BioGptForSequenceClassification(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_SNAKE_CASE)
						self.assertEqual(result.logits.shape						,		(self.model_tester.batch_size, self.model_tester.num_labels))


		def 						lowerCAmelCase__     (					self):
						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(_SCREAMING_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(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE						,		attention_mask=_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_SNAKE_CASE)
						self.assertEqual(result.logits.shape						,		(self.model_tester.batch_size, self.model_tester.num_labels))






@require_torch
class       __snake_case  (    unittest.TestCase       ):

		@slow
		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						BioGptForCausalLM.from_pretrained('microsoft/biogpt')
						SCREAMING_SNAKE_CASE_									=						torch.tensor([[2, 4805, 9, 656, 21]])
						SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE)[0]

						SCREAMING_SNAKE_CASE_									=						42384

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

						SCREAMING_SNAKE_CASE_									=						torch.tensor(
						    [[[-9.5_2_3_6, -9.8_9_1_8, 10.4557], [-11.0469, -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]						,		_SCREAMING_SNAKE_CASE						,		atol=1E-4))


		@slow
		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						BioGptTokenizer.from_pretrained('microsoft/biogpt')
						SCREAMING_SNAKE_CASE_									=						BioGptForCausalLM.from_pretrained('microsoft/biogpt')
						model.to(_SCREAMING_SNAKE_CASE)

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





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__  :      Optional[int]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[Any]			=    torch.device("cpu")


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for k in state_dict.keys():
        SCREAMING_SNAKE_CASE_									=						k
        if ".pwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.pwconv' ,   '.point_wise_conv'       )
        if ".dwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
        if ".Proj." in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.Proj.' ,   '.proj.'       )
        if "patch_embed" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
        if "network" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.split('.'       )
            if ls[2].isdigit():
                SCREAMING_SNAKE_CASE_									=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
            else:
                SCREAMING_SNAKE_CASE_									=						k_new.replace('network' ,   'swiftformer.encoder.network'       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
    SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_									=						idalabel
    SCREAMING_SNAKE_CASE_									=						{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 6, 4]
        SCREAMING_SNAKE_CASE_									=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 9, 6]
        SCREAMING_SNAKE_CASE_									=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        SCREAMING_SNAKE_CASE_									=						[4, 3, 10, 5]
        SCREAMING_SNAKE_CASE_									=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        SCREAMING_SNAKE_CASE_									=						[4, 4, 12, 6]
        SCREAMING_SNAKE_CASE_									=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'       ):
            SCREAMING_SNAKE_CASE_									=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						create_rename_keys(_SCREAMING_SNAKE_CASE       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

    # prepare test inputs
    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						ViTImageProcessor.from_pretrained('preprocessor_config'       )
    SCREAMING_SNAKE_CASE_									=						processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    assert torch.allclose(hf_logits[0, 0:5] ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

    Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
    print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""       )
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       UpperCamelCase__  :      str			=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swiftformer_name",
           default="swiftformer_xs",
           choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
           type=str,
           help="Name of the SwiftFormer model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="./converted_outputs/",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


