
'''simple docstring'''







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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	# initialize config
	if "resnet-50" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	_snake_case   						=		DetrConfig(use_timm_backbone=__A			,				backbone_config=__A	)

	# set label attributes
	_snake_case   						=		'panoptic' in model_name
	if is_panoptic:
		_snake_case   						=		250
	else:
		_snake_case   						=		91
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'coco-detection-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	return config, is_panoptic







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	# here we list all keys to be renamed (original name on the left, our name on the right)
	_snake_case   						=		[]

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')	)
	# stages
	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
				    )	)
			# 3 convs
			for i in range(3	):
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
		        F'encoder.layers.{i}.self_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias')	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
		        F'decoder.layers.{i}.self_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')	)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
		        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
		        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
		    )	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias')	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		state_dict.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False	)       ->	int:
	_snake_case   						=		''
	if is_panoptic:
		_snake_case   						=		'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		_snake_case   						=		state_dict.pop(
		    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		_snake_case   						=		in_proj_weight_cross_attn[:256, :]
		_snake_case   						=		in_proj_bias_cross_attn[:256]
		_snake_case   						=		in_proj_weight_cross_attn[256:512, :]
		_snake_case   						=		in_proj_bias_cross_attn[256:512]
		_snake_case   						=		in_proj_weight_cross_attn[-256:, :]
		_snake_case   						=		in_proj_bias_cross_attn[-256:]







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[int]:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)

	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=None			,				__A=False	)       ->	Optional[int]:
	_snake_case						, _snake_case   						=		get_detr_config(__A	)

	# load original model from torch hub
	_snake_case   						=		{
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F'Converting model {model_name}...'	)
	_snake_case   						=		torch.hub.load('facebookresearch/detr'			,				model_name_to_original_name[model_name]			,				pretrained=__A	).eval()
	_snake_case   						=		detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(__A	):
		if is_panoptic:
			_snake_case   						=		'detr.' + src
		rename_key(__A			,				__A			,				__A	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(__A			,				is_panoptic=__A	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	_snake_case   						=		'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val

    # finally, create HuggingFace model and load state dict
	_snake_case   						=		DetrForSegmentation(__A	) if is_panoptic else DetrForObjectDetection(__A	)
	model.load_state_dict(__A	)
	model.eval()

	# verify our conversion on an image
	_snake_case   						=		'coco_panoptic' if is_panoptic else 'coco_detection'
	_snake_case   						=		DetrImageProcessor(format=__A	)

	_snake_case   						=		processor(images=prepare_img()			,				return_tensors='pt'	)
	_snake_case   						=		encoding['pixel_values']

	_snake_case   						=		detr(__A	)
	_snake_case   						=		model(__A	)

	assert torch.allclose(outputs.logits			,				original_outputs['pred_logits']			,				atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes			,				original_outputs['pred_boxes']			,				atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks			,				original_outputs['pred_masks']			,				atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
		Path(__A	).mkdir(exist_ok=__A	)
		model.save_pretrained(__A	)
		processor.save_pretrained(__A	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F'nielsr/{model_name}'	)
		processor.push_to_hub(F'nielsr/{model_name}'	)


if __name__ == "__main__":
					lowercase    :							Optional[int]      =					argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="detr-resnet-50",
					    type=str,
					    choices=["detr-resnet-50", "detr-resnet-101"],
					    help="Name of the DETR model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
					lowercase    :							Any      =					parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class       __lowerCAmelCase				:






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Union[str, Any]=True				,    _lowerCAmelCase:    str=99				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Dict=5				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    Optional[Any]=4				,    _lowerCAmelCase:    Any="gelu"				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Optional[int]=True				,    _lowerCAmelCase:    Any=5_12				,    _lowerCAmelCase:    Optional[Any]=16				,    _lowerCAmelCase:    str=2				,    _lowerCAmelCase:    Tuple=0.02				,    _lowerCAmelCase:    str=3				,    _lowerCAmelCase:    List[str]=4				,    _lowerCAmelCase:    Optional[Any]=None				,    ):
         lowercase			:List[str]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Dict							=  is_training
         lowercase			:Union[str, Any]							=  use_input_mask
         lowercase			:Optional[int]							=  use_token_type_ids
         lowercase			:Dict							=  use_labels
         lowercase			:Any							=  vocab_size
         lowercase			:List[Any]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Union[str, Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_multiple_size
         lowercase			:str							=  hidden_act
         lowercase			:List[Any]							=  hidden_dropout
         lowercase			:Optional[Any]							=  attention_dropout
         lowercase			:int							=  weight_tying
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:str							=  type_vocab_size
         lowercase			:Union[str, Any]							=  type_sequence_label_size
         lowercase			:Dict							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:Dict							=  num_choices
         lowercase			:Any							=  scope






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

         lowercase			:Optional[Any]							=  None
         if self.use_input_mask:
           lowercase			:str							=  random_attention_mask([self.batch_size, self.seq_length] )

         lowercase			:int							=  None
         if self.use_labels:
           lowercase			:Dict							=  ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels )

         lowercase			:Tuple							=  self.get_config()

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return GPTNeoXJapaneseConfig(
             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_multiple_size=self.intermediate_multiple_size				,    hidden_act=self.hidden_act				,    hidden_dropout=self.hidden_dropout				,    attention_dropout=self.attention_dropout				,    weight_tying=self.weight_tying				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=_lowerCAmelCase				,    initializer_range=self.initializer_range				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Any							=  self.prepare_config_and_inputs()

         lowercase			:Tuple							=  True

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  GPTNeoXJapaneseModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Optional[int]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         lowercase			:Dict							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  GPTNeoXJapaneseModel(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:str							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Tuple							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    labels=_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  True
         lowercase			:List[str]							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()

         # first forward pass
         lowercase			:str							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )
         lowercase			:Tuple							=  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase			:Optional[int]							=  ids_tensor((self.batch_size, 3)				,    config.vocab_size )
         lowercase			:Tuple							=  ids_tensor((self.batch_size, 3)				,    vocab_size=2 )

         # append to next input_ids and
         lowercase			:Tuple							=  torch.cat([input_ids, next_tokens]				,    dim=-1 )
         lowercase			:List[Any]							=  torch.cat([input_mask, next_mask]				,    dim=-1 )

         lowercase			:Dict							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase )
         lowercase			:List[str]							=  output_from_no_past["hidden_states"][0]
         lowercase			:str							=  model(
             _lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase				,    )["hidden_states"][0]

         # select random slice
         lowercase			:Dict							=  ids_tensor((1,)				,    output_from_past.shape[-1] ).item()
         lowercase			:str							=  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase			:List[str]							=  output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

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






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:str							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs
         lowercase			:Union[str, Any]							=  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       _a           =  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       _a           =  (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       _a           =  False
       _a           =  False
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  GPTNeoXJapaneseModelTester(self )
         lowercase			:List[str]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         # This regression test was failing with PyTorch < 1.3
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase			:Union[str, Any]							=  None

         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  "abeja/gpt-neox-japanese-2.7b"

         lowercase			:Optional[int]							=  ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

         lowercase			:int							=  [
             "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
             "100年後に必要とされる会社は、「人」が中心の会社です。",
             "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
             "国境の長いトンネルを抜けると、そこは雪国だった。",
             "美味しい日本食といえば、やっぱりお寿司ですよね。",
         ]

         lowercase			:List[str]							=  GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
         lowercase			:Tuple							=  GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )

         lowercase			:List[str]							=  []
         for prompt in prompts:
           lowercase			:Optional[int]							=  tokenizer(_lowerCAmelCase				,    return_tensors="pt" ).input_ids
           lowercase			:Union[str, Any]							=  model.generate(_lowerCAmelCase				,    max_length=50 )
           lowercase			:List[str]							=  tokenizer.batch_decode(_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase )
           predicted_outputs += generated_string
         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )



