





import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
UpperCAmelCase__    :      Tuple       					=logging.getLogger(__name__)

UpperCAmelCase__    :      Any       					=int(1_00_00)  # Hardcoded max length to avoid infinite loop

UpperCAmelCase__    :      int       					={
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
UpperCAmelCase__    :      List[str]       					='''In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>'''





def   _lowercase       (    _UpperCAmelCase					)    ->	List[Any]:
				np.random.seed(args.seed					)
				torch.manual_seed(args.seed					)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed					)





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				if args.temperature > 0.7:
								logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""					)

				lowerCamelCase									=tokenizer.encode(_UpperCAmelCase      ,   add_special_tokens=_UpperCAmelCase					)
				if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()					):
								logger.info("""WARNING! You are not starting your generation from a control code so you won't get good results"""					)
				return prompt_text





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Tuple:
				# kwargs = {"language": None, "mask_token_id": None}

				# Set the language
				lowerCamelCase									=hasattr(model.config      ,   """use_lang_emb"""					) and model.config.use_lang_emb
				if hasattr(model.config      ,   """lang2id"""					) and use_lang_emb:
								lowerCamelCase									=model.config.langaid.keys()
								if args.xlm_language in available_languages:
												lowerCamelCase									=args.xlm_language
								else:
												lowerCamelCase									=None
												while language not in available_languages:
																lowerCamelCase									=input("""Using XLM. Select language in """ + str(list(_UpperCAmelCase					)					) + """ >>> """					)

								lowerCamelCase									=model.config.langaid[language]
								# kwargs["language"] = tokenizer.lang2id[language]

				# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
				# XLM masked-language modeling (MLM) models need masked token
				# is_xlm_mlm = "mlm" in args.model_name_or_path
				# if is_xlm_mlm:
				#     kwargs["mask_token_id"] = tokenizer.mask_token_id

				return prompt_text





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				lowerCamelCase									=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				lowerCamelCase									=prefix + prompt_text
				return prompt_text





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	List[Any]:
				lowerCamelCase									=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				lowerCamelCase									=prefix + prompt_text
				return prompt_text


UpperCAmelCase__    :      str       					={
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				if length < 0 and max_sequence_length > 0:
								lowerCamelCase									=max_sequence_length
				elif 0 < max_sequence_length < length:
								lowerCamelCase									=max_sequence_length  # No generation bigger than model size
				elif length < 0:
								lowerCamelCase									=MAX_LENGTH  # avoid infinite loop
				return length





def   _lowercase       (    _UpperCAmelCase					)    ->	Any:
				lowerCamelCase									=None
				if hasattr(_UpperCAmelCase      ,   """hidden_size"""					):
								lowerCamelCase									=model_config.hidden_size
				elif hasattr(_UpperCAmelCase      ,   """n_embed"""					):
								lowerCamelCase									=model_config.n_embed
				elif hasattr(_UpperCAmelCase      ,   """n_embd"""					):
								lowerCamelCase									=model_config.n_embd

				lowerCamelCase									=None
				if hasattr(_UpperCAmelCase      ,   """num_attention_heads"""					):
								lowerCamelCase									=model_config.num_attention_heads
				elif hasattr(_UpperCAmelCase      ,   """n_head"""					):
								lowerCamelCase									=model_config.n_head

				if embedding_size is None or num_head is None or num_head == 0:
								raise ValueError("""Check the model config"""					)

				lowerCamelCase									=int(embedding_size / num_head					)
				if hasattr(_UpperCAmelCase      ,   """n_layer"""					):
								lowerCamelCase									=model_config.n_layer
				elif hasattr(_UpperCAmelCase      ,   """num_hidden_layers"""					):
								lowerCamelCase									=model_config.num_hidden_layers
				else:
								raise ValueError("""Number of hidden layers couldn't be determined from the model config"""					)

				return num_layer, num_head, num_embedding_size_per_head





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Optional[Any]:
				lowerCamelCase			,	lowerCamelCase			,	lowerCamelCase									=sparse_model_config(model.config					)
				if model.config.model_type == "bloom":
								lowerCamelCase									=tuple(
								    (
								        torch.empty(int(num_attention_heads * batch_size					)      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
								        .to(model.dtype					)
								        .to(model.device					),
								        torch.empty(int(num_attention_heads * batch_size					)      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
								        .to(model.dtype					)
								        .to(model.device					),
								    )
								    for _ in range(_UpperCAmelCase					)					)
				else:
								lowerCamelCase									=tuple(
								    (
								        torch.empty(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
								        .to(model.dtype					)
								        .to(model.device					),
								        torch.empty(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
								        .to(model.dtype					)
								        .to(model.device					),
								    )
								    for _ in range(_UpperCAmelCase					)					)
				return past_key_values





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Any:
				lowerCamelCase									=len(_UpperCAmelCase					)
				lowerCamelCase									=tokenizer.batch_encode_plus(_UpperCAmelCase      ,   return_tensors="""pt"""					)
				lowerCamelCase									=dummy_input.to(model.device					)
				if model.config.use_cache:
								lowerCamelCase									=generate_past_key_values(_UpperCAmelCase      ,   _UpperCAmelCase      ,   1					)
				lowerCamelCase									=torch.cat(
				    [
				        torch.zeros(dummy_input["""attention_mask"""].shape[0]      ,   1					)
				        .to(dummy_input["""attention_mask"""].dtype					)
				        .to(model.device					),
				        dummy_input["""attention_mask"""],
				    ]      ,   -1      ,   )
				return dummy_input

class    __A  (    a     ):
				__A  =					("""_optimized""", """_default""")


				def __init__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=optimized
								lowerCamelCase									=default


				def __call__(		self ,			*UpperCAmelCase_ ,			**UpperCAmelCase_				):
								if kwargs["past_key_values"] is None and self._default.config.use_cache:
												lowerCamelCase									=generate_past_key_values(self._default ,			kwargs["""input_ids"""].shape[0] ,			0				)
								kwargs.pop("""position_ids""" ,			UpperCAmelCase_				)
								for k in list(kwargs.keys()				):
												if kwargs[k] is None or isinstance(kwargs[k] ,			UpperCAmelCase_				):
																kwargs.pop(UpperCAmelCase_				)
								lowerCamelCase									=self._optimized(**UpperCAmelCase_				)
								lowerCamelCase									=outputs[0]
								lowerCamelCase									=outputs[1]
								lowerCamelCase									=CausalLMOutputWithPast(
								    loss=UpperCAmelCase_ ,			logits=UpperCAmelCase_ ,			past_key_values=UpperCAmelCase_ ,			hidden_states=UpperCAmelCase_ ,			attentions=UpperCAmelCase_ ,			)
								return fixed_output


				def __getattr__(		self ,			UpperCAmelCase_				):
								return getattr(self._default ,			UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			**UpperCAmelCase_				):
								return self._default.prepare_inputs_for_generation(
								    UpperCAmelCase_ ,			past_key_values=UpperCAmelCase_ ,			inputs_embeds=UpperCAmelCase_ ,			use_cache=UpperCAmelCase_ ,			**UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								return self._default._reorder_cache(UpperCAmelCase_ ,			UpperCAmelCase_				)





def   _lowercase       (    )    ->	List[str]:
				lowerCamelCase									=argparse.ArgumentParser()
				parser.add_argument(
				    """--model_type"""      ,   default=_UpperCAmelCase      ,   type=_UpperCAmelCase      ,   required=_UpperCAmelCase      ,   help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()					)      ,   )
				parser.add_argument(
				    """--model_name_or_path"""      ,   default=_UpperCAmelCase      ,   type=_UpperCAmelCase      ,   required=_UpperCAmelCase      ,   help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()					)      ,   )

				parser.add_argument("""--prompt"""      ,   type=_UpperCAmelCase      ,   default=""""""					)
				parser.add_argument("""--length"""      ,   type=_UpperCAmelCase      ,   default=20					)
				parser.add_argument("""--stop_token"""      ,   type=_UpperCAmelCase      ,   default=_UpperCAmelCase      ,   help="""Token at which text generation is stopped"""					)

				parser.add_argument(
				    """--temperature"""      ,   type=_UpperCAmelCase      ,   default=1.0      ,   help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""      ,   )
				parser.add_argument(
				    """--repetition_penalty"""      ,   type=_UpperCAmelCase      ,   default=1.0      ,   help="""primarily useful for CTRL model; in that case, use 1.2"""					)
				parser.add_argument("""--k"""      ,   type=_UpperCAmelCase      ,   default=0					)
				parser.add_argument("""--p"""      ,   type=_UpperCAmelCase      ,   default=0.9					)

				parser.add_argument("""--prefix"""      ,   type=_UpperCAmelCase      ,   default=""""""      ,   help="""Text added prior to input."""					)
				parser.add_argument("""--padding_text"""      ,   type=_UpperCAmelCase      ,   default=""""""      ,   help="""Deprecated, the use of `--prefix` is preferred."""					)
				parser.add_argument("""--xlm_language"""      ,   type=_UpperCAmelCase      ,   default=""""""      ,   help="""Optional language when used with the XLM model."""					)

				parser.add_argument("""--seed"""      ,   type=_UpperCAmelCase      ,   default=42      ,   help="""random seed for initialization"""					)
				parser.add_argument("""--no_cuda"""      ,   action="""store_true"""      ,   help="""Avoid using CUDA when available"""					)
				parser.add_argument("""--num_return_sequences"""      ,   type=_UpperCAmelCase      ,   default=1      ,   help="""The number of samples to generate."""					)
				parser.add_argument(
				    """--fp16"""      ,   action="""store_true"""      ,   help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,   )
				parser.add_argument("""--jit"""      ,   action="""store_true"""      ,   help="""Whether or not to use jit trace to accelerate inference"""					)
				lowerCamelCase									=parser.parse_args()

				lowerCamelCase									=torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
				lowerCamelCase									=0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""					)

				set_seed(_UpperCAmelCase					)

				# Initialize the model and tokenizer
				try:
								lowerCamelCase									=args.model_type.lower()
								lowerCamelCase			,	lowerCamelCase									=MODEL_CLASSES[args.model_type]
				except KeyError:
								raise KeyError("""the model {} you specified is not supported. You are welcome to add it and open a PR :)"""					)

				lowerCamelCase									=tokenizer_class.from_pretrained(args.model_name_or_path					)
				if tokenizer.pad_token is None:
								lowerCamelCase									=tokenizer.eos_token
				lowerCamelCase									=model_class.from_pretrained(args.model_name_or_path					)
				model.to(args.device					)

				if args.fpaa:
								model.half()
				lowerCamelCase									=getattr(model.config      ,   """max_position_embeddings"""      ,   0					)
				lowerCamelCase									=adjust_length_to_model(args.length      ,   max_sequence_length=_UpperCAmelCase					)
				logger.info(_UpperCAmelCase					)

				lowerCamelCase									=args.prompt if args.prompt else input("""Model prompt >>> """					)

				# Different models need different input formatting and/or extra arguments
				lowerCamelCase									=args.model_type in PREPROCESSING_FUNCTIONS.keys()
				if requires_preprocessing:
								lowerCamelCase									=PREPROCESSING_FUNCTIONS.get(args.model_type					)
								lowerCamelCase									=prepare_input(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)

								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												lowerCamelCase									={"""add_space_before_punct_symbol""": True}
								else:
												lowerCamelCase									={}

								lowerCamelCase									=tokenizer.encode(
								    _UpperCAmelCase      ,   add_special_tokens=_UpperCAmelCase      ,   return_tensors="""pt"""      ,   **_UpperCAmelCase					)
				else:
								lowerCamelCase									=args.prefix if args.prefix else args.padding_text
								lowerCamelCase									=tokenizer.encode(prefix + prompt_text      ,   add_special_tokens=_UpperCAmelCase      ,   return_tensors="""pt"""					)
				lowerCamelCase									=encoded_prompt.to(args.device					)

				if encoded_prompt.size()[-1] == 0:
								lowerCamelCase									=None
				else:
								lowerCamelCase									=encoded_prompt

				if args.jit:
								lowerCamelCase									=["""enable jit"""]
								lowerCamelCase									=prepare_jit_inputs(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
								torch._C._jit_set_texpr_fuser_enabled(_UpperCAmelCase					)
								lowerCamelCase									=False
								if hasattr(_UpperCAmelCase      ,   """forward"""					):
												lowerCamelCase									=inspect.signature(model.forward					)
								else:
												lowerCamelCase									=inspect.signature(model.__call__					)
								lowerCamelCase									=tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(_UpperCAmelCase      ,   _UpperCAmelCase					) is not None					)
								lowerCamelCase									=torch.jit.trace(_UpperCAmelCase      ,   _UpperCAmelCase      ,   strict=_UpperCAmelCase					)
								lowerCamelCase									=torch.jit.freeze(traced_model.eval()					)
								traced_model(*_UpperCAmelCase					)
								traced_model(*_UpperCAmelCase					)

								lowerCamelCase									=_ModelFallbackWrapper(_UpperCAmelCase      ,   _UpperCAmelCase					)

				lowerCamelCase									=model.generate(
				    input_ids=_UpperCAmelCase      ,   max_length=args.length + len(encoded_prompt[0]					)      ,   temperature=args.temperature      ,   top_k=args.k      ,   top_p=args.p      ,   repetition_penalty=args.repetition_penalty      ,   do_sample=_UpperCAmelCase      ,   num_return_sequences=args.num_return_sequences      ,   )

				# Remove the batch dimension when returning multiple sequences
				if len(output_sequences.shape					) > 2:
								output_sequences.squeeze_()

				lowerCamelCase									=[]

				for generated_sequence_idx, generated_sequence in enumerate(_UpperCAmelCase					):
								print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""					)
								lowerCamelCase									=generated_sequence.tolist()

								# Decode text
								lowerCamelCase									=tokenizer.decode(_UpperCAmelCase      ,   clean_up_tokenization_spaces=_UpperCAmelCase					)

								# Remove all text after the stop token
								lowerCamelCase									=text[: text.find(args.stop_token					) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								lowerCamelCase									=(
								    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      ,   clean_up_tokenization_spaces=_UpperCAmelCase					)					) :]
								)

								generated_sequences.append(_UpperCAmelCase					)
								print(_UpperCAmelCase					)

				return generated_sequences


if __name__ == "__main__":
							main()












import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__    :      List[Any]       					=logging.get_logger(__name__)





def   _lowercase       (    )    ->	List[Any]:
				lowerCamelCase									=DetaConfig(
				    num_queries=9_00      ,   encoder_ffn_dim=20_48      ,   decoder_ffn_dim=20_48      ,   num_feature_levels=5      ,   assign_first_stage=_UpperCAmelCase      ,   with_box_refine=_UpperCAmelCase      ,   two_stage=_UpperCAmelCase      ,   )

				# set labels
				lowerCamelCase									=91
				lowerCamelCase									="""huggingface/label-files"""
				lowerCamelCase									="""coco-detection-id2label.json"""
				lowerCamelCase									=json.load(open(cached_download(hf_hub_url(_UpperCAmelCase      ,   _UpperCAmelCase      ,   repo_type="""dataset"""					)					)      ,   """r"""					)					)
				lowerCamelCase									={int(_UpperCAmelCase					): v for k, v in idalabel.items()}
				lowerCamelCase									=idalabel
				lowerCamelCase									={v: k for k, v in idalabel.items()}

				return config





def   _lowercase       (    _UpperCAmelCase					)    ->	List[str]:
				lowerCamelCase									=[]

				# stem
				# fmt: off
				rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")					)
				rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")					)
				rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")					)
				rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")					)
				rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																    )					)
    # transformer encoder
				for i in range(config.encoder_layers					):
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")					)
								rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")					)

				# transformer decoder
				for i in range(config.decoder_layers					):
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")					)
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")					)

				# fmt: on

				return rename_keys





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase									=dct.pop(_UpperCAmelCase					)
				lowerCamelCase									=val





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				# transformer decoder self-attention layers
				lowerCamelCase									=config.d_model
				for i in range(config.decoder_layers					):
								# read in weights + bias of input projection layer of self-attention
								lowerCamelCase									=state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
								lowerCamelCase									=state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase									=in_proj_weight[:hidden_size, :]
								lowerCamelCase									=in_proj_bias[:hidden_size]
								lowerCamelCase									=in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								lowerCamelCase									=in_proj_bias[hidden_size : hidden_size * 2]
								lowerCamelCase									=in_proj_weight[-hidden_size:, :]
								lowerCamelCase									=in_proj_bias[-hidden_size:]





def   _lowercase       (    )    ->	List[str]:
				lowerCamelCase									="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase									=Image.open(requests.get(_UpperCAmelCase      ,   stream=_UpperCAmelCase					).raw					)

				return im





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Optional[int]:
				lowerCamelCase									=get_deta_config()

				# load original state dict
				if model_name == "deta-resnet-50":
								lowerCamelCase									="""adet_checkpoint0011.pth"""
				elif model_name == "deta-resnet-50-24-epochs":
								lowerCamelCase									="""adet_2x_checkpoint0023.pth"""
				else:
								raise ValueError(F"""Model name {model_name} not supported"""					)
				lowerCamelCase									=hf_hub_download(repo_id="""nielsr/deta-checkpoints"""      ,   filename=_UpperCAmelCase					)
				lowerCamelCase									=torch.load(_UpperCAmelCase      ,   map_location="""cpu"""					)["""model"""]

				# rename keys
				lowerCamelCase									=create_rename_keys(_UpperCAmelCase					)
				for src, dest in rename_keys:
								rename_key(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
				read_in_decoder_q_k_v(_UpperCAmelCase      ,   _UpperCAmelCase					)

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val
								if "input_proj" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val

    # finally, create HuggingFace model and load state dict
				lowerCamelCase									=DetaForObjectDetection(_UpperCAmelCase					)
				model.load_state_dict(_UpperCAmelCase					)
				model.eval()

				lowerCamelCase									="""cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(_UpperCAmelCase					)

				# load image processor
				lowerCamelCase									=DetaImageProcessor(format="""coco_detection"""					)

				# verify our conversion on image
				lowerCamelCase									=prepare_img()
				lowerCamelCase									=processor(images=_UpperCAmelCase      ,   return_tensors="""pt"""					)
				lowerCamelCase									=encoding["""pixel_values"""]
				lowerCamelCase									=model(pixel_values.to(_UpperCAmelCase					)					)

				# verify logits
				if model_name == "deta-resnet-50":
								lowerCamelCase									=torch.tensor(
								    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]					)
								lowerCamelCase									=torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]					)
				elif model_name == "deta-resnet-50-24-epochs":
								lowerCamelCase									=torch.tensor(
								    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]					)
								lowerCamelCase									=torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]					)

				assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(_UpperCAmelCase					)      ,   atol=1e-4					)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(_UpperCAmelCase					)      ,   atol=1e-4					)
				print("""Everything ok!"""					)

				if pytorch_dump_folder_path:
								# Save model and processor
								logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""					)
								Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
								model.save_pretrained(_UpperCAmelCase					)
								processor.save_pretrained(_UpperCAmelCase					)

				# Push to hub
				if push_to_hub:
								print("""Pushing model and processor to hub..."""					)
								model.push_to_hub(F"""jozhang97/{model_name}"""					)
								processor.push_to_hub(F"""jozhang97/{model_name}"""					)


if __name__ == "__main__":
							UpperCAmelCase__    :      Any       					=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    type=str,
							    default='''deta-resnet-50''',
							    choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
							    help='''Name of the 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 or not to push the converted model to the 🤗 hub.'''
							)
							UpperCAmelCase__    :      Dict       					=parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





