



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
							_lowerCAmelCase     :							List[str]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  ).convert("RGB"  )

							return image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    []
							# fmt: off

							# vision encoder
							rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")  )
							rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")  )
							rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")  )
							rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")  )
							rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")  )
							rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")  )

							for i in range(config.vision_config.num_hidden_layers  ):
														rename_keys.append((F"visual_encoder.blocks.{i}.norm1.weight", F"vision_model.encoder.layers.{i}.layer_norm1.weight")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.norm1.bias", F"vision_model.encoder.layers.{i}.layer_norm1.bias")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.norm2.weight", F"vision_model.encoder.layers.{i}.layer_norm2.weight")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.norm2.bias", F"vision_model.encoder.layers.{i}.layer_norm2.bias")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.attn.qkv.weight", F"vision_model.encoder.layers.{i}.self_attn.qkv.weight")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.weight", F"vision_model.encoder.layers.{i}.self_attn.projection.weight",)  )
														rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.bias", F"vision_model.encoder.layers.{i}.self_attn.projection.bias")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.weight", F"vision_model.encoder.layers.{i}.mlp.fc1.weight")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.bias", F"vision_model.encoder.layers.{i}.mlp.fc1.bias")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.weight", F"vision_model.encoder.layers.{i}.mlp.fc2.weight")  )
														rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.bias", F"vision_model.encoder.layers.{i}.mlp.fc2.bias")  )

							# QFormer
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight")  )
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")  )

							# fmt: on
							return rename_keys






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Any    =    dct.pop(_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[int]    =    val






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							for i in range(config.vision_config.num_hidden_layers  ):
														# read in original q and v biases
														_lowerCAmelCase     :							Tuple    =    state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"  )
														_lowerCAmelCase     :							Optional[int]    =    state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"  )

														# next, set bias in the state dict
														_lowerCAmelCase     :							str    =    torch.cat((q_bias, torch.zeros_like(_lowerCamelCase				, requires_grad=_lowerCamelCase  ), v_bias)  )
														_lowerCAmelCase     :							Any    =    qkv_bias






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    364 if "coco" in model_name else 224
							_lowerCAmelCase     :							Dict    =    InstructBlipVisionConfig(image_size=_lowerCamelCase  ).to_dict()

							# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
							# seems like flan-T5 models don't have bos_token_id properly set?
							if "t5-xl" in model_name:
														_lowerCAmelCase     :							Any    =    TaConfig.from_pretrained("google/flan-t5-xl"				, dense_act_fn="gelu"				, bos_token_id=1  ).to_dict()
							elif "t5-xxl" in model_name:
														_lowerCAmelCase     :							int    =    TaConfig.from_pretrained("google/flan-t5-xxl"				, dense_act_fn="gelu"				, bos_token_id=1  ).to_dict()
							elif "vicuna-7b" in model_name:
														_lowerCAmelCase     :							Union[str, Any]    =    LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"				, vocab_size=32_001  ).to_dict()
							elif "vicuna-13b" in model_name:
														_lowerCAmelCase     :							Any    =    LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"				, vocab_size=32_001  ).to_dict()
							else:
														raise ValueError("Model name not supported"  )

							# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
							_lowerCAmelCase     :							int    =    InstructBlipQFormerConfig(vocab_size=30_523  ).to_dict()
							_lowerCAmelCase     :							str    =    InstructBlipConfig(vision_config=_lowerCamelCase				, text_config=_lowerCamelCase				, qformer_config=_lowerCamelCase  )

							return config, image_size






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase=None				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    AutoTokenizer.from_pretrained("bert-base-uncased"				, truncation_side="left"  )
							qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}  )

							if "t5" in model_name:
														_lowerCAmelCase     :							Any    =    TaTokenizerFast.from_pretrained("google/flan-t5-xl"				, truncation_side="left"  )
							elif "vicuna" in model_name:
														# the following was used in the original implementation:
														# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
														# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
														# tokenizer.add_special_tokens({"bos_token": "</s>"})
														# tokenizer.add_special_tokens({"eos_token": "</s>"})
														# tokenizer.add_special_tokens({"unk_token": "</s>"})
														_lowerCAmelCase     :							str    =    LlamaTokenizerFast.from_pretrained(
														    "huggyllama/llama-7b"				, truncation_side="left"				, bos_token="</s>"				, unk_token="</s>"  )
														tokenizer.add_special_tokens({"pad_token": "[PAD]"}  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_blipa_config(_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[int]    =    InstructBlipForConditionalGeneration(_lowerCamelCase  ).eval()

							_lowerCAmelCase     :							Dict    =    {
							    "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
							    "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
							    "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
							    "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
							}

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    model_name_to_original[model_name]

							# load original model
							print("Loading original model..."  )
							_lowerCAmelCase     :							Optional[int]    =    "cuda:1" if torch.cuda.is_available() else "cpu"
							_lowerCAmelCase     :							Optional[int]    =    "cuda:2" if torch.cuda.is_available() else "cpu"
							_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    load_model_and_preprocess(
							    name=_lowerCamelCase				, model_type=_lowerCamelCase				, is_eval=_lowerCamelCase				, device=_lowerCamelCase  )
							original_model.eval()
							print("Done!"  )

							# update state dict keys
							_lowerCAmelCase     :							Union[str, Any]    =    original_model.state_dict()
							_lowerCAmelCase     :							Optional[Any]    =    create_rename_keys(_lowerCamelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														_lowerCAmelCase     :							Optional[int]    =    state_dict.pop(_lowerCamelCase  )
														if key.startswith("Qformer.bert"  ):
																					_lowerCAmelCase     :							List[Any]    =    key.replace("Qformer.bert"				, "qformer"  )
														if "attention.self" in key:
																					_lowerCAmelCase     :							Optional[int]    =    key.replace("self"				, "attention"  )
														if "llm_proj" in key:
																					_lowerCAmelCase     :							Union[str, Any]    =    key.replace("llm_proj"				, "language_projection"  )
														if "t5_proj" in key:
																					_lowerCAmelCase     :							List[str]    =    key.replace("t5_proj"				, "language_projection"  )
														if key.startswith("llm_model"  ):
																					_lowerCAmelCase     :							Tuple    =    key.replace("llm_model"				, "language_model"  )
														if key.startswith("t5"  ):
																					_lowerCAmelCase     :							Optional[Any]    =    key.replace("t5"				, "language"  )
														_lowerCAmelCase     :							Optional[Any]    =    val

							# read in qv biases
							read_in_q_v_bias(_lowerCamelCase				, _lowerCamelCase  )

							# note: weights get loaded in torch.float32 by default
							hf_model.load_state_dict(_lowerCamelCase				, strict=_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    load_demo_image()
							_lowerCAmelCase     :							Optional[int]    =    "What is unusual about this image?"

							# create processor
							_lowerCAmelCase     :							Union[str, Any]    =    BlipImageProcessor(
							    size={"height": image_size, "width": image_size}				, image_mean=_lowerCamelCase				, image_std=_lowerCamelCase  )
							_lowerCAmelCase     :							List[str]    =    InstructBlipProcessor(
							    image_processor=_lowerCamelCase				, tokenizer=_lowerCamelCase				, qformer_tokenizer=_lowerCamelCase				, )
							_lowerCAmelCase     :							int    =    processor(images=_lowerCamelCase				, text=_lowerCamelCase				, return_tensors="pt"  ).to(_lowerCamelCase  )

							# make sure processor creates exact same pixel values
							_lowerCAmelCase     :							Tuple    =    vis_processors["eval"](_lowerCamelCase  ).unsqueeze(0  ).to(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    inputs.pixel_values
							assert torch.allclose(original_pixel_values.to(pixel_values.device  )				, _lowerCamelCase  )

							original_model.to(_lowerCamelCase  )
							hf_model.to(_lowerCamelCase  )
							with torch.no_grad():
														if "vicuna" in model_name:
																					_lowerCAmelCase     :							List[Any]    =    original_model({"image": original_pixel_values, "text_input": [prompt]}  ).logits
																					_lowerCAmelCase     :							Union[str, Any]    =    hf_model(**_lowerCamelCase  ).logits
														else:
																					_lowerCAmelCase     :							Optional[int]    =    original_model(
																					    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}  ).logits
																					_lowerCAmelCase     :							List[str]    =    tokenizer("\n"				, return_tensors="pt"  ).input_ids.to(_lowerCamelCase  )
																					_lowerCAmelCase     :							Optional[Any]    =    label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				, -100  )
																					_lowerCAmelCase     :							Tuple    =    hf_model(**_lowerCamelCase				, labels=_lowerCamelCase  ).logits

							print("First values of original logits:"				, original_logits[0, :3, :3]  )
							print("First values of HF logits:"				, logits[0, :3, :3]  )

							# assert values
							assert original_logits.shape == logits.shape
							_lowerCAmelCase     :							Dict    =    1e-4 if "vicuna" in model_name else 1e-5
							assert torch.allclose(original_logits.to(logits.device  )				, _lowerCamelCase				, atol=_lowerCamelCase  )
							print("Looks ok!"  )

							print("Generating with original model..."  )
							_lowerCAmelCase     :							Dict    =    original_model.generate({"image": original_pixel_values, "prompt": prompt}				, num_beams=5  )

							# important: we need to cast the weights of the HF model to the appropriate type
							print("Generating with HF model..."  )
							_lowerCAmelCase     :							Any    =    hf_model.generate(
							    **_lowerCamelCase				, do_sample=_lowerCamelCase				, num_beams=5				, max_length=256				, min_length=1				, top_p=0.9				, repetition_penalty=1.5				, length_penalty=1.0				, temperature=1				, )
							if "vicuna" in model_name:
														# convert output id 0 to 2 (eos_token_id)
														# TODO add this in the generate method?
														_lowerCAmelCase     :							List[str]    =    2
							print("Original generation:"				, _lowerCamelCase  )
							_lowerCAmelCase     :							Optional[int]    =    processor.batch_decode(_lowerCamelCase				, skip_special_tokens=_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[Any]    =    [text.strip() for text in output_text]
							print("HF generation:"				, _lowerCamelCase  )

							if pytorch_dump_folder_path is not None:
														processor.save_pretrained(_lowerCamelCase  )
														hf_model.save_pretrained(_lowerCamelCase  )

							if push_to_hub:
														processor.push_to_hub(F"Salesforce/{model_name}"  )
														hf_model.push_to_hub(F"Salesforce/{model_name}"  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						_snake_case		=  [
						    "instructblip-vicuna-7b",
						    "instructblip-vicuna-13b",
						    "instructblip-flan-t5-xl",
						    "instructblip-flan-t5-xxl",
						]
						parser.add_argument(
						    "--model_name",
						    default="instructblip-flan-t5-xl",
						    choices=choices,
						    type=str,
						    help="Path to hf config.json of model to convert",
						)
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						    help="Whether to push the model and processor to the hub after converting",
						)

						_snake_case		=  parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    LiltForQuestionAnswering,
						    LiltForSequenceClassification,
						    LiltForTokenClassification,
						    LiltModel,
						)
						from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a=13,			__a=7,			__a=True,			__a=True,			__a=True,			__a=True,			__a=99,			__a=24,			__a=2,			__a=6,			__a=37,			__a="gelu",			__a=0.1,			__a=0.1,			__a=512,			__a=16,			__a=2,			__a=0.02,			__a=3,			__a=None,			__a=1000,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    parent
														_lowerCAmelCase     :							List[str]    =    batch_size
														_lowerCAmelCase     :							int    =    seq_length
														_lowerCAmelCase     :							Optional[int]    =    is_training
														_lowerCAmelCase     :							Dict    =    use_input_mask
														_lowerCAmelCase     :							List[str]    =    use_token_type_ids
														_lowerCAmelCase     :							str    =    use_labels
														_lowerCAmelCase     :							Optional[Any]    =    vocab_size
														_lowerCAmelCase     :							Tuple    =    hidden_size
														_lowerCAmelCase     :							List[Any]    =    num_hidden_layers
														_lowerCAmelCase     :							Optional[Any]    =    num_attention_heads
														_lowerCAmelCase     :							Any    =    intermediate_size
														_lowerCAmelCase     :							List[str]    =    hidden_act
														_lowerCAmelCase     :							Union[str, Any]    =    hidden_dropout_prob
														_lowerCAmelCase     :							Any    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							int    =    max_position_embeddings
														_lowerCAmelCase     :							Optional[int]    =    type_vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    type_sequence_label_size
														_lowerCAmelCase     :							List[str]    =    initializer_range
														_lowerCAmelCase     :							List[Any]    =    num_labels
														_lowerCAmelCase     :							Tuple    =    scope
														_lowerCAmelCase     :							str    =    range_bbox







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    ids_tensor([self.batch_size, self.seq_length],			self.vocab_size)

														_lowerCAmelCase     :							int    =    ids_tensor([self.batch_size, self.seq_length, 4],			self.range_bbox)
														# Ensure that bbox is legal
														for i in range(bbox.shape[0]):
																					for j in range(bbox.shape[1]):
																												if bbox[i, j, 3] < bbox[i, j, 1]:
																																			_lowerCAmelCase     :							Dict    =    bbox[i, j, 3]
																																			_lowerCAmelCase     :							int    =    bbox[i, j, 1]
																																			_lowerCAmelCase     :							Tuple    =    t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			_lowerCAmelCase     :							str    =    bbox[i, j, 2]
																																			_lowerCAmelCase     :							List[Any]    =    bbox[i, j, 0]
																																			_lowerCAmelCase     :							str    =    t

														_lowerCAmelCase     :							Optional[Any]    =    None
														if self.use_input_mask:
																					_lowerCAmelCase     :							Union[str, Any]    =    ids_tensor([self.batch_size, self.seq_length],			vocab_size=2)

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

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

														_lowerCAmelCase     :							Optional[int]    =    self.get_config()

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







							def    snake_case__      (   self):

														'''simple docstring'''

														return LiltConfig(
														    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,			initializer_range=self.initializer_range,			)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    LiltModel(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Dict    =    model(__a,			bbox=__a,			attention_mask=__a,			token_type_ids=__a)
														_lowerCAmelCase     :							str    =    model(__a,			bbox=__a,			token_type_ids=__a)
														_lowerCAmelCase     :							List[Any]    =    model(__a,			bbox=__a)

														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size))
														self.parent.assertEqual(result.pooler_output.shape,			(self.batch_size, self.hidden_size))







							def    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.num_labels
														_lowerCAmelCase     :							Optional[Any]    =    LiltForTokenClassification(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Dict    =    model(
														    __a,			bbox=__a,			attention_mask=__a,			token_type_ids=__a,			labels=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.num_labels))







							def    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    LiltForQuestionAnswering(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Tuple    =    model(
														    __a,			bbox=__a,			attention_mask=__a,			token_type_ids=__a,			start_positions=__a,			end_positions=__a,			)
														self.parent.assertEqual(result.start_logits.shape,			(self.batch_size, self.seq_length))
														self.parent.assertEqual(result.end_logits.shape,			(self.batch_size, self.seq_length))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.prepare_config_and_inputs()
														(
														    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    
														)     :							Dict    =    config_and_inputs
														_lowerCAmelCase     :							List[Any]    =    {
														    "input_ids": input_ids,
														    "bbox": bbox,
														    "token_type_ids": token_type_ids,
														    "attention_mask": input_mask,
														}
														return config, inputs_dict



@require_torch
class   UpperCAmelCase_    (       a   ,					a   ,					a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase__   			=		(
							    {
							        'feature-extraction': LiltModel,
							        'question-answering': LiltForQuestionAnswering,
							        'text-classification': LiltForSequenceClassification,
							        'token-classification': LiltForTokenClassification,
							        'zero-shot': LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														return True







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    LiltModelTester(self)
														_lowerCAmelCase     :							Union[str, Any]    =    ConfigTester(self,			config_class=__a,			hidden_size=37)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					_lowerCAmelCase     :							Any    =    type
																					self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase     :							str    =    LiltModel.from_pretrained(__a)
																					self.assertIsNotNone(__a)



@require_torch
@slow
class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)

														_lowerCAmelCase     :							Any    =    torch.tensor([[1, 2]],			device=__a)
														_lowerCAmelCase     :							str    =    torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]],			device=__a)

														# forward pass
														with torch.no_grad():
																					_lowerCAmelCase     :							Optional[Any]    =    model(input_ids=__a,			bbox=__a)

														_lowerCAmelCase     :							Optional[int]    =    torch.Size([1, 2, 768])
														_lowerCAmelCase     :							List[str]    =    torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],			device=__a,			)

														self.assertTrue(outputs.last_hidden_state.shape,			__a)
														self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3],			__a,			atol=1E-3))
