



'''simple docstring'''






import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = XCLIPTextConfig()

       # derive patch size from model name
       _UpperCAmelCase  :						Any			     = model_name.find("""patch"""		)
       _UpperCAmelCase  :						Any			     = int(model_name[start_idx + len("""patch"""		) : start_idx + len("""patch"""		) + 2]		)
       _UpperCAmelCase  :						Any			     = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_       ,   num_frames=SCREAMING_SNAKE_CASE_		)

       if "large" in model_name:
              _UpperCAmelCase  :						List[str]			     = 768
              _UpperCAmelCase  :						Optional[int]			     = 3072
              _UpperCAmelCase  :						Any			     = 12

              _UpperCAmelCase  :						Optional[int]			     = 1024
              _UpperCAmelCase  :						List[Any]			     = 4096
              _UpperCAmelCase  :						Optional[Any]			     = 16
              _UpperCAmelCase  :						Any			     = 24
              _UpperCAmelCase  :						Dict			     = 768
              _UpperCAmelCase  :						List[str]			     = 3072

       if model_name == "xclip-large-patch14-16-frames":
              _UpperCAmelCase  :						str			     = 336

       _UpperCAmelCase  :						Any			     = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_		)

       if "large" in model_name:
              _UpperCAmelCase  :						List[Any]			     = 768

       return config




def     snake_case_					(     lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       if name == "token_embedding.weight":
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""token_embedding.weight"""       ,   """text_model.embeddings.token_embedding.weight"""		)
       if name == "positional_embedding":
              _UpperCAmelCase  :						int			     = name.replace("""positional_embedding"""       ,   """text_model.embeddings.position_embedding.weight"""		)
       if "ln_1" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""ln_1"""       ,   """layer_norm1"""		)
       if "ln_2" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""ln_2"""       ,   """layer_norm2"""		)
       if "c_fc" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""c_fc"""       ,   """fc1"""		)
       if "c_proj" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""c_proj"""       ,   """fc2"""		)
       if name.startswith("""transformer.resblocks"""		):
              _UpperCAmelCase  :						List[str]			     = name.replace("""transformer.resblocks"""       ,   """text_model.encoder.layers"""		)
       if "attn.out_proj" in name and "message" not in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""attn.out_proj"""       ,   """self_attn.out_proj"""		)
       if "ln_final" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""ln_final"""       ,   """text_model.final_layer_norm"""		)
       # visual encoder
       if name == "visual.class_embedding":
              _UpperCAmelCase  :						Dict			     = name.replace("""visual.class_embedding"""       ,   """vision_model.embeddings.class_embedding"""		)
       if name == "visual.positional_embedding":
              _UpperCAmelCase  :						Any			     = name.replace("""visual.positional_embedding"""       ,   """vision_model.embeddings.position_embedding.weight"""		)
       if name.startswith("""visual.transformer.resblocks"""		):
              _UpperCAmelCase  :						Union[str, Any]			     = name.replace("""visual.transformer.resblocks"""       ,   """vision_model.encoder.layers"""		)
       if "visual.conv1" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""visual.conv1"""       ,   """vision_model.embeddings.patch_embedding"""		)
       if "visual.ln_pre" in name:
              _UpperCAmelCase  :						Union[str, Any]			     = name.replace("""visual.ln_pre"""       ,   """vision_model.pre_layernorm"""		)
       if "visual.ln_post" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""visual.ln_post"""       ,   """vision_model.post_layernorm"""		)
       if "visual.proj" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""visual.proj"""       ,   """visual_projection.weight"""		)
       if "text_projection" in name:
              _UpperCAmelCase  :						Any			     = name.replace("""text_projection"""       ,   """text_projection.weight"""		)
       # things on top
       if "prompts_visual_proj" in name:
              _UpperCAmelCase  :						str			     = name.replace("""prompts_visual_proj"""       ,   """prompts_visual_projection"""		)
       if "prompts_visual_ln" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""prompts_visual_ln"""       ,   """prompts_visual_layernorm"""		)
       # mit
       if name == "mit.positional_embedding":
              _UpperCAmelCase  :						Dict			     = name.replace("""positional"""       ,   """position"""		)
       if name.startswith("""mit.resblocks"""		):
              _UpperCAmelCase  :						List[str]			     = name.replace("""mit.resblocks"""       ,   """mit.encoder.layers"""		)
       # prompts generator
       if name.startswith("""prompts_generator.norm"""		):
              _UpperCAmelCase  :						List[Any]			     = name.replace("""prompts_generator.norm"""       ,   """prompts_generator.layernorm"""		)

       return name




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase  :						Any			     = orig_state_dict.pop(SCREAMING_SNAKE_CASE_		)

              if "attn.in_proj" in key:
                     _UpperCAmelCase  :						str			     = key.split("""."""		)
                     if key.startswith("""visual"""		):
                            _UpperCAmelCase  :						str			     = key_split[3]
                            _UpperCAmelCase  :						Optional[Any]			     = config.vision_config.hidden_size
                            if "message_attn" in key:
                                   if "weight" in key:
                                          _UpperCAmelCase  :						Any			     = val[
                                              :dim, :
                                          ]
                                          _UpperCAmelCase  :						Optional[Any]			     = val[
                                              dim : dim * 2, :
                                          ]
                                          _UpperCAmelCase  :						int			     = val[
                                              -dim:, :
                                          ]
                                   else:
                                          _UpperCAmelCase  :						str			     = val[
                                              :dim
                                          ]
                                          _UpperCAmelCase  :						Union[str, Any]			     = val[
                                              dim : dim * 2
                                          ]
                                          _UpperCAmelCase  :						Any			     = val[
                                              -dim:
                                          ]
                            else:
                                   if "weight" in key:
                                          _UpperCAmelCase  :						str			     = val[
                                              :dim, :
                                          ]
                                          _UpperCAmelCase  :						Tuple			     = val[
                                              dim : dim * 2, :
                                          ]
                                          _UpperCAmelCase  :						int			     = val[
                                              -dim:, :
                                          ]
                                   else:
                                          _UpperCAmelCase  :						List[Any]			     = val[:dim]
                                          _UpperCAmelCase  :						Optional[Any]			     = val[
                                              dim : dim * 2
                                          ]
                                          _UpperCAmelCase  :						int			     = val[-dim:]
                     elif key.startswith("""mit"""		):
                            _UpperCAmelCase  :						Dict			     = key_split[2]
                            _UpperCAmelCase  :						str			     = config.vision_config.mit_hidden_size
                            if "weight" in key:
                                   _UpperCAmelCase  :						List[Any]			     = val[:dim, :]
                                   _UpperCAmelCase  :						List[Any]			     = val[dim : dim * 2, :]
                                   _UpperCAmelCase  :						Any			     = val[-dim:, :]
                            else:
                                   _UpperCAmelCase  :						int			     = val[:dim]
                                   _UpperCAmelCase  :						Optional[Any]			     = val[dim : dim * 2]
                                   _UpperCAmelCase  :						List[str]			     = val[-dim:]
                     else:
                            _UpperCAmelCase  :						int			     = key_split[2]
                            _UpperCAmelCase  :						Optional[Any]			     = config.text_config.hidden_size
                            if "weight" in key:
                                   _UpperCAmelCase  :						int			     = val[:dim, :]
                                   _UpperCAmelCase  :						List[str]			     = val[
                                       dim : dim * 2, :
                                   ]
                                   _UpperCAmelCase  :						Dict			     = val[-dim:, :]
                            else:
                                   _UpperCAmelCase  :						List[str]			     = val[:dim]
                                   _UpperCAmelCase  :						int			     = val[
                                       dim : dim * 2
                                   ]
                                   _UpperCAmelCase  :						Optional[int]			     = val[-dim:]
              else:
                     _UpperCAmelCase  :						Optional[int]			     = rename_key(SCREAMING_SNAKE_CASE_		)
                     if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                            _UpperCAmelCase  :						List[str]			     = val.T
                     _UpperCAmelCase  :						List[str]			     = val

       return orig_state_dict




def     snake_case_					(     lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       if num_frames == 8:
              _UpperCAmelCase  :						Tuple			     = 'eating_spaghetti_8_frames.npy'
       elif num_frames == 16:
              _UpperCAmelCase  :						Dict			     = 'eating_spaghetti.npy'
       elif num_frames == 32:
              _UpperCAmelCase  :						List[Any]			     = 'eating_spaghetti_32_frames.npy'
       _UpperCAmelCase  :						Dict			     = hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""       ,   filename=SCREAMING_SNAKE_CASE_       ,   repo_type="""dataset"""       ,   )
       _UpperCAmelCase  :						Optional[int]			     = np.load(SCREAMING_SNAKE_CASE_		)
       return list(SCREAMING_SNAKE_CASE_		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_=None       ,   lowerCAmelCase_=False		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = {
           # fully supervised kinetics-400 checkpoints
           'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
           'xclip-base-patch32-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
           ),
           'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
           'xclip-base-patch16-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
           ),
           'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
           'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
           # fully supervised kinetics-600 checkpoints
           'xclip-base-patch16-kinetics-600': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
           ),
           'xclip-base-patch16-kinetics-600-16-frames': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
           ),
           'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
           # few shot
           'xclip-base-patch16-hmdb-2-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
           ),
           'xclip-base-patch16-hmdb-4-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
           ),
           'xclip-base-patch16-hmdb-8-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
           ),
           'xclip-base-patch16-hmdb-16-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
           ),
           'xclip-base-patch16-ucf-2-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
           ),
           'xclip-base-patch16-ucf-4-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
           ),
           'xclip-base-patch16-ucf-8-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
           ),
           'xclip-base-patch16-ucf-16-shot': (
               'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
           ),
           # zero shot
           'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
       }

       _UpperCAmelCase  :						Dict			     = model_to_url[model_name]
       _UpperCAmelCase  :						List[str]			     = 8
       if "16-frames" in model_name:
              _UpperCAmelCase  :						Any			     = 16
       elif "shot" in model_name:
              _UpperCAmelCase  :						str			     = 32

       _UpperCAmelCase  :						str			     = get_xclip_config(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_		)
       _UpperCAmelCase  :						Any			     = XCLIPModel(SCREAMING_SNAKE_CASE_		)
       model.eval()

       if "drive" in checkpoint_url:
              _UpperCAmelCase  :						Any			     = 'pytorch_model.bin'
              gdown.cached_download(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   quiet=SCREAMING_SNAKE_CASE_		)
              _UpperCAmelCase  :						Tuple			     = torch.load(SCREAMING_SNAKE_CASE_       ,   map_location="""cpu"""		)['model']
       else:
              _UpperCAmelCase  :						Optional[Any]			     = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		)['model']

       _UpperCAmelCase  :						Optional[Any]			     = convert_state_dict(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_		)

       _UpperCAmelCase  :						Optional[int]			     = XCLIPModel(SCREAMING_SNAKE_CASE_		)
       _UpperCAmelCase  :						str			     = model.load_state_dict(SCREAMING_SNAKE_CASE_       ,   strict=SCREAMING_SNAKE_CASE_		)
       assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
       model.eval()

       _UpperCAmelCase  :						Optional[int]			     = 336 if model_name == 'xclip-large-patch14-16-frames' else 224
       _UpperCAmelCase  :						List[str]			     = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_		)
       _UpperCAmelCase  :						Tuple			     = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""		)
       _UpperCAmelCase  :						List[Any]			     = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""		)
       _UpperCAmelCase  :						Optional[Any]			     = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_       ,   tokenizer=SCREAMING_SNAKE_CASE_		)

       _UpperCAmelCase  :						List[Any]			     = prepare_video(SCREAMING_SNAKE_CASE_		)
       _UpperCAmelCase  :						str			     = processor(
           text=["""playing sports""", """eating spaghetti""", """go shopping"""]       ,   videos=SCREAMING_SNAKE_CASE_       ,   return_tensors="""pt"""       ,   padding=SCREAMING_SNAKE_CASE_		)

       print("""Shape of pixel values:"""       ,   inputs.pixel_values.shape		)

       with torch.no_grad():
              _UpperCAmelCase  :						Union[str, Any]			     = model(**SCREAMING_SNAKE_CASE_		)

       # Verify outputs
       _UpperCAmelCase  :						Optional[Any]			     = outputs.logits_per_video
       _UpperCAmelCase  :						Tuple			     = logits_per_video.softmax(dim=1		)
       print("""Probs:"""       ,   SCREAMING_SNAKE_CASE_		)
       # kinetics-400
       if model_name == "xclip-base-patch32":
              _UpperCAmelCase  :						str			     = torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]		)
       elif model_name == "xclip-base-patch32-16-frames":
              _UpperCAmelCase  :						Dict			     = torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]		)
       elif model_name == "xclip-base-patch16":
              _UpperCAmelCase  :						int			     = torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]		)
       elif model_name == "xclip-base-patch16-16-frames":
              _UpperCAmelCase  :						Any			     = torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]		)
       elif model_name == "xclip-large-patch14":
              _UpperCAmelCase  :						Any			     = torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]		)
       elif model_name == "xclip-large-patch14-16-frames":
              _UpperCAmelCase  :						Union[str, Any]			     = torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]		)
       # kinetics-600
       elif model_name == "xclip-base-patch16-kinetics-600":
              _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]		)
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              _UpperCAmelCase  :						int			     = torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]		)
       elif model_name == "xclip-large-patch14-kinetics-600":
              _UpperCAmelCase  :						Dict			     = torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]		)
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]		)
       elif model_name == "xclip-base-patch16-hmdb-4-shot":
              _UpperCAmelCase  :						Tuple			     = torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]		)
       elif model_name == "xclip-base-patch16-hmdb-8-shot":
              _UpperCAmelCase  :						int			     = torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]		)
       elif model_name == "xclip-base-patch16-hmdb-16-shot":
              _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]		)
       elif model_name == "xclip-base-patch16-ucf-2-shot":
              _UpperCAmelCase  :						str			     = torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]		)
       elif model_name == "xclip-base-patch16-ucf-4-shot":
              _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]		)
       elif model_name == "xclip-base-patch16-ucf-8-shot":
              _UpperCAmelCase  :						int			     = torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]		)
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              _UpperCAmelCase  :						Union[str, Any]			     = torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]		)
       # zero shot
       elif model_name == "xclip-base-patch16-zero-shot":
              _UpperCAmelCase  :						List[Any]			     = torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]		)
       else:
              raise ValueError(F'''Model name {model_name} not supported'''		)
       assert torch.allclose(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   atol=1e-3		)
       print("""Looks ok!"""		)

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
              model.save_pretrained(SCREAMING_SNAKE_CASE_		)

       if push_to_hub:
              print("""Pushing model, processor and slow tokenizer files to the hub..."""		)
              model.push_to_hub(SCREAMING_SNAKE_CASE_       ,   organization="""nielsr"""		)
              processor.push_to_hub(SCREAMING_SNAKE_CASE_       ,   organization="""nielsr"""		)
              slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_       ,   organization="""nielsr"""		)


if __name__ == "__main__":
   A_							: str						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""xclip-base-patch32""",
       type=str,
       help="""Name of the model.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   A_							: str						 =	parser.parse_args()
   convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class      A__							(     __magic_name__	):
				lowercase			    =  'gptj'
				lowercase			    =  {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}






				def __init__(						self					:      Optional[Any]    ,     a					:      Dict=50_400    ,     a					:      Union[str, Any]=2_048    ,     a					:      List[str]=4_096    ,     a					:      Any=28    ,     a					:      Optional[Any]=16    ,     a					:      Optional[Any]=64    ,     a					:      int=None    ,     a					:      Any="gelu_new"    ,     a					:      Union[str, Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      Optional[Any]=1E-5    ,     a					:      Optional[int]=0.0_2    ,     a					:      int=True    ,     a					:      str=50_256    ,     a					:      str=50_256    ,     a					:      Any=False    ,     **a					:      Dict    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		vocab_size
							lowerCAmelCase__      :      List[Any]         =		n_positions
							lowerCAmelCase__      :      str         =		n_embd
							lowerCAmelCase__      :      str         =		n_layer
							lowerCAmelCase__      :      str         =		n_head
							lowerCAmelCase__      :      Dict         =		n_inner
							lowerCAmelCase__      :      Union[str, Any]         =		rotary_dim
							lowerCAmelCase__      :      Optional[int]         =		activation_function
							lowerCAmelCase__      :      Any         =		resid_pdrop
							lowerCAmelCase__      :      int         =		embd_pdrop
							lowerCAmelCase__      :      int         =		attn_pdrop
							lowerCAmelCase__      :      List[Any]         =		layer_norm_epsilon
							lowerCAmelCase__      :      str         =		initializer_range
							lowerCAmelCase__      :      Dict         =		use_cache

							lowerCAmelCase__      :      str         =		bos_token_id
							lowerCAmelCase__      :      int         =		eos_token_id

							super().__init__(
							    bos_token_id=a    ,     eos_token_id=a    ,     tie_word_embeddings=a    ,     **a   )







class      A__							(     __magic_name__	):






				def __init__(						self					:      str    ,     a					:      PretrainedConfig    ,     a					:      str = "default"    ,     a					:      List[PatchingSpec] = None    ,     a					:      bool = False    ,     ):






							'''simple docstring'''







							super().__init__(a    ,     task=a    ,     patching_specs=a    ,     use_past=a   )
							if not getattr(self._config    ,     'pad_token_id'    ,     a   ):
										# TODO: how to do that better?
										lowerCAmelCase__      :      int         =		0






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
							if self.use_past:
										self.fill_with_past_key_values_(a    ,     direction='inputs'   )
										lowerCAmelCase__      :      Optional[Any]         =		{0: 'batch', 1: 'past_sequence + sequence'}
							else:
										lowerCAmelCase__      :      Tuple         =		{0: 'batch', 1: 'sequence'}

							return common_inputs






				@property
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							return self._config.n_layer






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return self._config.n_head






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      PreTrainedTokenizer    ,     a					:      int = -1    ,     a					:      int = -1    ,     a					:      bool = False    ,     a					:      Optional[TensorType] = None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		super(a    ,     self   ).generate_dummy_inputs(
							    a    ,     batch_size=a    ,     seq_length=a    ,     is_pair=a    ,     framework=a   )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase__      :      Optional[int]         =		OrderedDict({'input_ids': common_inputs['input_ids']}   )

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
										else:
													import torch

													lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													lowerCAmelCase__      :      Optional[int]         =		seqlen + 2
													lowerCAmelCase__      :      Tuple         =		(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													lowerCAmelCase__      :      Tuple         =		[
													    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
													]

							lowerCAmelCase__      :      Any         =		common_inputs['attention_mask']
							if self.use_past:
										lowerCAmelCase__      :      List[str]         =		ordered_inputs['attention_mask'].dtype
										lowerCAmelCase__      :      Optional[Any]         =		torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(a    ,     a    ,     dtype=a   )]    ,     dim=1   )

							return ordered_inputs






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return 13