

import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_       		=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_       		=  []
for i in range(6):
     # 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 encoder + 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.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("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"),
    ]
)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      List[str]:





    '''simple docstring'''






    A__          =   state_dict.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str      )							->      int:





    '''simple docstring'''






    A__          =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            A__          =   key.replace('backbone.0.body'    ,			'backbone.conv_encoder.model'      )
            A__          =   value
        else:
            A__          =   value

    return new_state_dict
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   ''

    # 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)
        A__          =   state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'      )
        A__          =   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
        A__          =   in_proj_weight[:256, :]
        A__          =   in_proj_bias[:256]
        A__          =   in_proj_weight[256:512, :]
        A__          =   in_proj_bias[256:512]
        A__          =   in_proj_weight[-256:, :]
        A__          =   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
        A__          =   state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
        A__          =   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
        A__          =   in_proj_weight[:256, :]
        A__          =   in_proj_bias[:256]
        A__          =   in_proj_weight[256:512, :]
        A__          =   in_proj_bias[256:512]
        A__          =   in_proj_weight[-256:, :]
        A__          =   in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        A__          =   state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'      )
        A__          =   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
        A__          =   in_proj_weight_cross_attn[:256, :]
        A__          =   in_proj_bias_cross_attn[:256]
        A__          =   in_proj_weight_cross_attn[256:512, :]
        A__          =   in_proj_bias_cross_attn[256:512]
        A__          =   in_proj_weight_cross_attn[-256:, :]
        A__          =   in_proj_bias_cross_attn[-256:]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      str:





    '''simple docstring'''






    A__      ,						A__          =   image.size
    A__          =   max(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   800 if 'detection' in checkpoint_url else 1000
    A__          =   target_max_size / current_max_size
    A__          =   image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

    return resized_image
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      int:





    '''simple docstring'''






    A__          =   F.to_tensor(SCREAMING_SNAKE_CASE__      )
    A__          =   F.normalize(SCREAMING_SNAKE_CASE__    ,			mean=[0.485, 0.456, 0.406]    ,			std=[0.229, 0.224, 0.225]      )
    return image
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Dict:





    '''simple docstring'''






    logger.info('Converting model...'      )

    # load original state dict
    A__          =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'      )
    # rename keys
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   rename_backbone_keys(SCREAMING_SNAKE_CASE__      )
    # query, key and value matrices need special treatment
    read_in_q_k_v(SCREAMING_SNAKE_CASE__      )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    A__          =   'model.'
    for key in state_dict.copy().keys():
        if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
            A__          =   state_dict.pop(SCREAMING_SNAKE_CASE__      )
            A__          =   val
    # create HuggingFace model and load state dict
    A__          =   TableTransformerConfig(
        backbone='resnet18'    ,			mask_loss_coefficient=1    ,			dice_loss_coefficient=1    ,			ce_loss_coefficient=1    ,			bbox_loss_coefficient=5    ,			giou_loss_coefficient=2    ,			eos_coefficient=0.4    ,			class_cost=1    ,			bbox_cost=5    ,			giou_cost=2    ,			)

    if "detection" in checkpoint_url:
        A__          =   15
        A__          =   2
        A__          =   {0: 'table', 1: 'table rotated'}
        A__          =   idalabel
        A__          =   {v: k for k, v in idalabel.items()}
    else:
        A__          =   125
        A__          =   6
        A__          =   {
            0: 'table',
            1: 'table column',
            2: 'table row',
            3: 'table column header',
            4: 'table projected row header',
            5: 'table spanning cell',
        }
        A__          =   idalabel
        A__          =   {v: k for k, v in idalabel.items()}

    A__          =   DetrImageProcessor(
        format='coco_detection'    ,			max_size=800 if 'detection' in checkpoint_url else 1000      )
    A__          =   TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE__      )
    model.load_state_dict(SCREAMING_SNAKE_CASE__      )
    model.eval()

    # verify our conversion
    A__          =   'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
    A__          =   hf_hub_download(repo_id='nielsr/example-pdf'    ,			repo_type='dataset'    ,			filename=SCREAMING_SNAKE_CASE__      )
    A__          =   Image.open(SCREAMING_SNAKE_CASE__      ).convert('RGB'      )
    A__          =   normalize(resize(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )      ).unsqueeze(0      )

    A__          =   model(SCREAMING_SNAKE_CASE__      )

    if "detection" in checkpoint_url:
        A__          =   (1, 15, 3)
        A__          =   torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]      )
        A__          =   torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]      )

    else:
        A__          =   (1, 125, 7)
        A__          =   torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
        A__          =   torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]      )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]    ,			SCREAMING_SNAKE_CASE__    ,			atol=1E-4      )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,			SCREAMING_SNAKE_CASE__    ,			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(SCREAMING_SNAKE_CASE__      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__      )
        model.save_pretrained(SCREAMING_SNAKE_CASE__      )
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE__      )

    if push_to_hub:
        # Push model to HF hub
        logger.info('Pushing model to the hub...'      )
        A__          =   (
            'microsoft/table-transformer-detection'
            if 'detection' in checkpoint_url
            else 'microsoft/table-transformer-structure-recognition'
        )
        model.push_to_hub(SCREAMING_SNAKE_CASE__      )
        image_processor.push_to_hub(SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  argparse.ArgumentParser()

     parser.add_argument(
         "--checkpoint_url",
         default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
         type=str,
         choices=[
             "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
             "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
         ],
         help="URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub."
     )
     lowercase_       		=  parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
     )


class  A						:







    """simple docstring"""





    def __init__(      self :       str,lowercase_ :       Any,lowercase_ :       Tuple=1_3,lowercase_ :       str=7,lowercase_ :       Tuple=True,lowercase_ :       int=True,lowercase_ :       List[Any]=True,lowercase_ :       List[str]=True,lowercase_ :       List[str]=9_9,lowercase_ :       List[Any]=6_4,lowercase_ :       List[str]=5,lowercase_ :       Optional[Any]=4,lowercase_ :       Optional[Any]=3_7,lowercase_ :       Optional[Any]="gelu",lowercase_ :       int=0.1,lowercase_ :       str=0.1,lowercase_ :       Optional[Any]=5_1_2,lowercase_ :       int=1_6,lowercase_ :       List[Any]=2,lowercase_ :       Union[str, Any]=0.02,lowercase_ :       Tuple=3,lowercase_ :       List[Any]=4,lowercase_ :       str=None,)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   seq_length
        A__          =   is_training
        A__          =   use_input_mask
        A__          =   use_token_type_ids
        A__          =   use_labels
        A__          =   vocab_size
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   max_position_embeddings
        A__          =   type_vocab_size
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   num_labels
        A__          =   num_choices
        A__          =   scope
        A__          =   vocab_size - 1


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)

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

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

        A__          =   self.get_config()

        return config, input_ids, input_mask, token_labels


    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        return GPTNeoXConfig(
            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,pad_token_id=self.pad_token_id,)


    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.prepare_config_and_inputs()

        A__          =   True

        return config, input_ids, input_mask, token_labels


    def 						snake_case__     (      self :       Any,lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       str		)->    Any:





        '''simple docstring'''







        A__          =   GPTNeoXModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[str],lowercase_ :       Dict,lowercase_ :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   True
        A__          =   GPTNeoXModel(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   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 :       Union[str, Any],lowercase_ :       str,lowercase_ :       Union[str, Any],lowercase_ :       Union[str, Any],lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   GPTNeoXForCausalLM(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   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 :       Optional[int],lowercase_ :       Optional[int],lowercase_ :       Optional[int],lowercase_ :       Dict,lowercase_ :       Any		)->    int:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForQuestionAnswering(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_		)
        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 :       List[str],lowercase_ :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Optional[int]		)->    str:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForSequenceClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   ids_tensor([self.batch_size],self.type_sequence_label_size		)
        A__          =   model(lowercase_,attention_mask=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       Any,lowercase_ :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       int		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForTokenClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)		)


    def 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       int,lowercase_ :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   True
        A__          =   GPTNeoXForCausalLM(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()

        # first forward pass
        A__          =   model(lowercase_,attention_mask=lowercase_,use_cache=lowercase_		)
        A__          =   outputs.past_key_values

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

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

        A__          =   model(lowercase_,attention_mask=lowercase_,output_hidden_states=lowercase_		)
        A__          =   output_from_no_past['hidden_states'][0]
        A__          =   model(
            lowercase_,attention_mask=lowercase_,past_key_values=lowercase_,output_hidden_states=lowercase_,)['hidden_states'][0]

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

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

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


    def 						snake_case__     (      self :       str		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase			=							(GPTNeoXForCausalLM,) if is_torch_available() else ()
    lowerCamelCase			=							(
        {
            'feature-extraction': GPTNeoXModel,
            'question-answering': GPTNeoXForQuestionAnswering,
            'text-classification': GPTNeoXForSequenceClassification,
            'text-generation': GPTNeoXForCausalLM,
            'token-classification': GPTNeoXForTokenClassification,
            'zero-shot': GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   GPTNeoXModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,hidden_size=6_4,num_attention_heads=8		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    Any:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_decoder()

        A__          =   None

        self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[str]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase_		)


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase_		)


    @unittest.skip(reason='Feed forward chunking is not implemented'		)
    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @parameterized.expand([('linear',), ('dynamic',)]		)
    def 						snake_case__     (      self :       List[str],lowercase_ :       Any		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        A__          =   ids_tensor([1, 1_0],config.vocab_size		)
        A__          =   ids_tensor([1, int(config.max_position_embeddings * 1.5		)],config.vocab_size		)

        set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
        A__          =   GPTNeoXModel(lowercase_		)
        original_model.to(lowercase_		)
        original_model.eval()
        A__          =   original_model(lowercase_		).last_hidden_state
        A__          =   original_model(lowercase_		).last_hidden_state

        set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
        A__          =   {'type': scaling_type, 'factor': 10.0}
        A__          =   GPTNeoXModel(lowercase_		)
        scaled_model.to(lowercase_		)
        scaled_model.eval()
        A__          =   scaled_model(lowercase_		).last_hidden_state
        A__          =   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		)		)




@require_torch
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @slow
    def 						snake_case__     (      self :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'		)
        for checkpointing in [True, False]:
            A__          =   GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'		)

            if checkpointing:
                model.gradient_checkpointing_enable()
            else:
                model.gradient_checkpointing_disable()
            model.to(lowercase_		)

            A__          =   tokenizer('My favorite food is',return_tensors='pt'		).to(lowercase_		)
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            A__          =   'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

            A__          =   model.generate(**lowercase_,do_sample=lowercase_,max_new_tokens=2_0		)
            A__          =   tokenizer.batch_decode(lowercase_		)[0]

            self.assertEqual(lowercase_,lowercase_		)

