


'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
	__A  :		str       =			DPTConfig(embedding_type='hybrid'			)

	if "large" in checkpoint_url:
		__A  :		Dict       =			1024
		__A  :		Optional[int]       =			4096
		__A  :		List[Any]       =			24
		__A  :		Optional[Any]       =			16
		__A  :		List[Any]       =			[5, 11, 17, 23]
		__A  :		List[str]       =			[256, 512, 1024, 1024]
		__A  :		List[Any]       =			(1, 384, 384)

	if "nyu" or "midas" in checkpoint_url:
		__A  :		Optional[int]       =			768
		__A  :		int       =			[1, 1, 1, 0.5]
		__A  :		List[Any]       =			[256, 512, 768, 768]
		__A  :		str       =			150
		__A  :		Dict       =			16
		__A  :		List[Any]       =			(1, 384, 384)
		__A  :		int       =			False
		__A  :		Tuple       =			'project'

	if "ade" in checkpoint_url:
		__A  :		int       =			True
		__A  :		Optional[Any]       =			768
		__A  :		int       =			[1, 1, 1, 0.5]
		__A  :		List[str]       =			150
		__A  :		List[Any]       =			16
		__A  :		Any       =			'huggingface/label-files'
		__A  :		Optional[int]       =			'ade20k-id2label.json'
		__A  :		Any       =			json.load(open(cached_download(hf_hub_url(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			)			),				'r'			)			)
		__A  :		Dict       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
		__A  :		List[Any]       =			idalabel
		__A  :		Any       =			{v: k for k, v in idalabel.items()}
		__A  :		List[str]       =			[1, 150, 480, 480]

	return config, expected_shape


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
	__A  :		Union[str, Any]       =			['pretrained.model.head.weight', 'pretrained.model.head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		__A  :		List[str]       =			name.replace('pretrained.model',				'dpt.encoder'			)
	if "pretrained.model" in name:
		__A  :		Dict       =			name.replace('pretrained.model',				'dpt.embeddings'			)
	if "patch_embed" in name:
		__A  :		str       =			name.replace('patch_embed',				''			)
	if "pos_embed" in name:
		__A  :		Optional[int]       =			name.replace('pos_embed',				'position_embeddings'			)
	if "attn.proj" in name:
		__A  :		int       =			name.replace('attn.proj',				'attention.output.dense'			)
	if "proj" in name and "project" not in name:
		__A  :		int       =			name.replace('proj',				'projection'			)
	if "blocks" in name:
		__A  :		Optional[int]       =			name.replace('blocks',				'layer'			)
	if "mlp.fc1" in name:
		__A  :		Optional[Any]       =			name.replace('mlp.fc1',				'intermediate.dense'			)
	if "mlp.fc2" in name:
		__A  :		List[Any]       =			name.replace('mlp.fc2',				'output.dense'			)
	if "norm1" in name and "backbone" not in name:
		__A  :		Dict       =			name.replace('norm1',				'layernorm_before'			)
	if "norm2" in name and "backbone" not in name:
		__A  :		Optional[Any]       =			name.replace('norm2',				'layernorm_after'			)
	if "scratch.output_conv" in name:
		__A  :		List[Any]       =			name.replace('scratch.output_conv',				'head'			)
	if "scratch" in name:
		__A  :		int       =			name.replace('scratch',				'neck'			)
	if "layer1_rn" in name:
		__A  :		Tuple       =			name.replace('layer1_rn',				'convs.0'			)
	if "layer2_rn" in name:
		__A  :		Tuple       =			name.replace('layer2_rn',				'convs.1'			)
	if "layer3_rn" in name:
		__A  :		Union[str, Any]       =			name.replace('layer3_rn',				'convs.2'			)
	if "layer4_rn" in name:
		__A  :		Tuple       =			name.replace('layer4_rn',				'convs.3'			)
	if "refinenet" in name:
		__A  :		Any       =			int(name[len('neck.refinenet'			) : len('neck.refinenet'			) + 1]			)
		# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
		__A  :		Dict       =			name.replace(f"""refinenet{layer_idx}""",				f"""fusion_stage.layers.{abs(layer_idx-4			)}"""			)
	if "out_conv" in name:
		__A  :		List[str]       =			name.replace('out_conv',				'projection'			)
	if "resConfUnit1" in name:
		__A  :		str       =			name.replace('resConfUnit1',				'residual_layer1'			)
	if "resConfUnit2" in name:
		__A  :		List[str]       =			name.replace('resConfUnit2',				'residual_layer2'			)
	if "conv1" in name:
		__A  :		List[str]       =			name.replace('conv1',				'convolution1'			)
	if "conv2" in name:
		__A  :		List[Any]       =			name.replace('conv2',				'convolution2'			)
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		__A  :		int       =			name.replace('pretrained.act_postprocess1.0.project.0',				'neck.reassemble_stage.readout_projects.0.0'			)
	if "pretrained.act_postprocess2.0.project.0" in name:
		__A  :		int       =			name.replace('pretrained.act_postprocess2.0.project.0',				'neck.reassemble_stage.readout_projects.1.0'			)
	if "pretrained.act_postprocess3.0.project.0" in name:
		__A  :		Dict       =			name.replace('pretrained.act_postprocess3.0.project.0',				'neck.reassemble_stage.readout_projects.2.0'			)
	if "pretrained.act_postprocess4.0.project.0" in name:
		__A  :		int       =			name.replace('pretrained.act_postprocess4.0.project.0',				'neck.reassemble_stage.readout_projects.3.0'			)

	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		__A  :		str       =			name.replace('pretrained.act_postprocess1.3',				'neck.reassemble_stage.layers.0.projection'			)
	if "pretrained.act_postprocess1.4" in name:
		__A  :		Tuple       =			name.replace('pretrained.act_postprocess1.4',				'neck.reassemble_stage.layers.0.resize'			)
	if "pretrained.act_postprocess2.3" in name:
		__A  :		Optional[int]       =			name.replace('pretrained.act_postprocess2.3',				'neck.reassemble_stage.layers.1.projection'			)
	if "pretrained.act_postprocess2.4" in name:
		__A  :		Tuple       =			name.replace('pretrained.act_postprocess2.4',				'neck.reassemble_stage.layers.1.resize'			)
	if "pretrained.act_postprocess3.3" in name:
		__A  :		Dict       =			name.replace('pretrained.act_postprocess3.3',				'neck.reassemble_stage.layers.2.projection'			)
	if "pretrained.act_postprocess4.3" in name:
		__A  :		Optional[Any]       =			name.replace('pretrained.act_postprocess4.3',				'neck.reassemble_stage.layers.3.projection'			)
	if "pretrained.act_postprocess4.4" in name:
		__A  :		int       =			name.replace('pretrained.act_postprocess4.4',				'neck.reassemble_stage.layers.3.resize'			)
	if "pretrained" in name:
		__A  :		Tuple       =			name.replace('pretrained',				'dpt'			)
	if "bn" in name:
		__A  :		Tuple       =			name.replace('bn',				'batch_norm'			)
	if "head" in name:
		__A  :		Optional[Any]       =			name.replace('head',				'head.head'			)
	if "encoder.norm" in name:
		__A  :		Dict       =			name.replace('encoder.norm',				'layernorm'			)
	if "auxlayer" in name:
		__A  :		Any       =			name.replace('auxlayer',				'auxiliary_head.head'			)
	if "backbone" in name:
		__A  :		List[str]       =			name.replace('backbone',				'backbone.bit.encoder'			)

	if ".." in name:
		__A  :		Optional[int]       =			name.replace('..',				'.'			)

	if "stem.conv" in name:
		__A  :		Dict       =			name.replace('stem.conv',				'bit.embedder.convolution'			)
	if "blocks" in name:
		__A  :		Optional[Any]       =			name.replace('blocks',				'layers'			)
	if "convolution" in name and "backbone" in name:
		__A  :		Tuple       =			name.replace('convolution',				'conv'			)
	if "layer" in name and "backbone" in name:
		__A  :		List[str]       =			name.replace('layer',				'layers'			)
	if "backbone.bit.encoder.bit" in name:
		__A  :		List[Any]       =			name.replace('backbone.bit.encoder.bit',				'backbone.bit'			)
	if "embedder.conv" in name:
		__A  :		List[str]       =			name.replace('embedder.conv',				'embedder.convolution'			)
	if "backbone.bit.encoder.stem.norm" in name:
		__A  :		Union[str, Any]       =			name.replace('backbone.bit.encoder.stem.norm',				'backbone.bit.embedder.norm'			)
	return name


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Tuple			):
	for i in range(config.num_hidden_layers			):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__A  :		int       =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""			)
		__A  :		Tuple       =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""			)
		# next, add query, keys and values (in that order) to the state dict
		__A  :		Tuple       =			in_proj_weight[: config.hidden_size, :]
		__A  :		List[Any]       =			in_proj_bias[: config.hidden_size]
		__A  :		str       =			in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__A  :		Union[str, Any]       =			in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__A  :		List[Any]       =			in_proj_weight[
		    -config.hidden_size :, :
		]
		__A  :		Tuple       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				):
	__A  :		Union[str, Any]       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
	__A  :		Dict       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
	return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any			):
	__A  :		Tuple       =			get_dpt_config(UpperCamelCase__			)
	# load original state_dict from URL
	# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
	__A  :		int       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
	# remove certain keys
	remove_ignore_keys_(UpperCamelCase__			)
	# rename keys
	for key in state_dict.copy().keys():
		__A  :		Dict       =			state_dict.pop(UpperCamelCase__			)
		__A  :		List[str]       =			val
	# read in qkv matrices
	read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__			)

	# load HuggingFace model
	__A  :		Optional[int]       =			DPTForSemanticSegmentation(UpperCamelCase__			) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__			)
	model.load_state_dict(UpperCamelCase__			)
	model.eval()

	# Check outputs on an image
	__A  :		Any       =			480 if 'ade' in checkpoint_url else 384
	__A  :		Optional[Any]       =			DPTImageProcessor(size=UpperCamelCase__			)

	__A  :		Tuple       =			prepare_img()
	__A  :		Union[str, Any]       =			image_processor(UpperCamelCase__,				return_tensors='pt'			)

	# forward pass
	__A  :		List[str]       =			model(**UpperCamelCase__			).logits if 'ade' in checkpoint_url else model(**UpperCamelCase__			).predicted_depth

	if show_prediction:
		__A  :		str       =			(
		    torch.nn.functional.interpolate(
		        outputs.unsqueeze(1			),				size=(image.size[1], image.size[0]),				mode='bicubic',				align_corners=UpperCamelCase__,				)
		    .squeeze()
		    .cpu()
		    .numpy()
		)

		Image.fromarray((prediction / prediction.max()) * 255			).show()

	if pytorch_dump_folder_path is not None:
		Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
		print(f"""Saving model to {pytorch_dump_folder_path}"""			)
		model.save_pretrained(UpperCamelCase__			)
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
		image_processor.save_pretrained(UpperCamelCase__			)

	if push_to_hub:
		model.push_to_hub('ybelkada/dpt-hybrid-midas'			)
		image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'			)


if __name__ == "__main__":
	UpperCAmelCase_ :     Optional[Any]								=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
	    type=str,
	    help='URL of the original DPT checkpoint you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    required=False,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	)
	parser.add_argument(
	    '--model_name',
	    default='dpt-large',
	    type=str,
	    help='Name of the model, in case you\'re pushing to the hub.',
	)
	parser.add_argument(
	    '--show_prediction',
	    action='store_true',
	)

	UpperCAmelCase_ :     List[str]								=      parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)










'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=5      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=16      ,      __lowercase=2      ,      __lowercase=0.0_2      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=None      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

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

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

  __A  :		Union[str, Any]       =			None
  __A  :		Optional[int]       =			None
  __A  :		List[str]       =			None
  if self.use_labels:
   __A  :		str       =			ids_tensor([self.batch_size]      ,      self.type_sequence_label_size						)
   __A  :		List[str]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels						)
   __A  :		Union[str, Any]       =			ids_tensor([self.batch_size]      ,      self.num_choices						)

  __A  :		List[Any]       =			self.get_config()

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


 def  snake_case__   (      self						):

  """simple docstring"""







  return OpenLlamaConfig(
      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=__lowercase      ,      initializer_range=self.initializer_range      ,      use_stable_embedding=__lowercase      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			OpenLlamaModel(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase						)
  __A  :		Dict       =			model(__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		List[str]       =			True
  __A  :		int       =			OpenLlamaModel(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		List[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      )
  __A  :		Optional[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      )
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		Dict       =			OpenLlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Dict       =			OpenLlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()

  # first forward pass
  __A  :		List[str]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      use_cache=__lowercase      ,      )
  __A  :		Optional[Any]       =			outputs.past_key_values

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

  # append to next input_ids and
  __A  :		int       =			torch.cat([input_ids, next_tokens]      ,      dim=-1						)
  __A  :		Dict       =			torch.cat([input_mask, next_mask]      ,      dim=-1						)

  __A  :		Tuple       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]
  __A  :		Any       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      past_key_values=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]

  # select random slice
  __A  :		Any       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
  __A  :		Union[str, Any]       =			output_from_no_past[:, -3:, random_slice_idx].detach()
  __A  :		int       =			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(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.prepare_config_and_inputs()
  (
      (
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,(
      __A
  )     ,
  )  :		Optional[Any]       =			config_and_inputs
  __A  :		Optional[int]       =			{'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		List[str]    							=						(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowercase				:		List[Any]    							=						(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowercase				:		Optional[int]    							=						(
     {
         '''feature-extraction''': OpenLlamaModel,
         '''text-classification''': OpenLlamaForSequenceClassification,
         '''text-generation''': OpenLlamaForCausalLM,
         '''zero-shot''': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase				:		Union[str, Any]    							=						False
 __lowercase				:		Union[str, Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			OpenLlamaModelTester(self						)
  __A  :		List[str]       =			ConfigTester(self      ,      config_class=__lowercase      ,      hidden_size=37						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Union[str, Any]       =			3
  __A  :		int       =			input_dict['input_ids']
  __A  :		int       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Optional[int]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Optional[Any]       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[int]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		str       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Any       =			3
  __A  :		List[str]       =			'single_label_classification'
  __A  :		Dict       =			input_dict['input_ids']
  __A  :		Dict       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		List[str]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Union[str, Any]       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Dict       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		str       =			3
  __A  :		int       =			'multi_label_classification'
  __A  :		Union[str, Any]       =			input_dict['input_ids']
  __A  :		str       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Union[str, Any]       =			ids_tensor(
      [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size						).to(torch.float						)
  __A  :		int       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass







 @parameterized.expand([('linear',), ('dynamic',)]						)
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A     ,__A  :		str       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		List[Any]       =			ids_tensor([1, 10]      ,      config.vocab_size						)
  __A  :		str       =			ids_tensor([1, int(config.max_position_embeddings * 1.5						)]      ,      config.vocab_size						)

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		Union[str, Any]       =			OpenLlamaModel(__lowercase						)
  original_model.to(__lowercase						)
  original_model.eval()
  __A  :		Optional[int]       =			original_model(__lowercase						).last_hidden_state
  __A  :		int       =			original_model(__lowercase						).last_hidden_state

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		List[Any]       =			{'type': scaling_type, 'factor': 1_0.0}
  __A  :		str       =			OpenLlamaModel(__lowercase						)
  scaled_model.to(__lowercase						)
  scaled_model.eval()
  __A  :		Dict       =			scaled_model(__lowercase						).last_hidden_state
  __A  :		List[str]       =			scaled_model(__lowercase						).last_hidden_state

  # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
  # maximum sequence length, so the outputs for the short input should match.
  if scaling_type == "dynamic":
   self.assertTrue(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)
  else:
   self.assertFalse(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)

  # The output should be different for long inputs
  self.assertFalse(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)






