
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''image_processor''', '''tokenizer''']
				_lowercase     						='''OwlViTImageProcessor'''
				_lowercase     						=('''CLIPTokenizer''', '''CLIPTokenizerFast''')




				def __init__(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							**_UpperCamelCase	)							-> Tuple:
							lowerCAmelCase_         =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."							,							_UpperCamelCase							,							)
										lowerCAmelCase_         =      kwargs.pop("feature_extractor"	)

							lowerCAmelCase_         =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."	)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."	)

							super().__init__(_UpperCamelCase							,							_UpperCamelCase	)




				def __call__(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase="max_length"							,							_UpperCamelCase="np"							,							**_UpperCamelCase	)							-> List[str]:

							if text is None and query_images is None and images is None:
										raise ValueError(
										    "You have to specify at least one text or query image or image. All three cannot be none."	)

							if text is not None:
										if isinstance(_UpperCamelCase							,							_UpperCamelCase	) or (isinstance(_UpperCamelCase							,							_UpperCamelCase	) and not isinstance(text[0]							,							_UpperCamelCase	)):
													lowerCAmelCase_         =      [self.tokenizer(_UpperCamelCase							,							padding=_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	)]

										elif isinstance(_UpperCamelCase							,							_UpperCamelCase	) and isinstance(text[0]							,							_UpperCamelCase	):
													lowerCAmelCase_         =      []

													# Maximum number of queries across batch
													lowerCAmelCase_         =      max([len(_UpperCamelCase	) for t in text]	)

													# Pad all batch samples to max number of text queries
													for t in text:
																if len(_UpperCamelCase	) != max_num_queries:
																			lowerCAmelCase_         =      t + [" "] * (max_num_queries - len(_UpperCamelCase	))

																lowerCAmelCase_         =      self.tokenizer(_UpperCamelCase							,							padding=_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	)
																encodings.append(_UpperCamelCase	)
										else:
													raise TypeError("Input text should be a string, a list of strings or a nested list of strings"	)

										if return_tensors == "np":
													lowerCAmelCase_         =      np.concatenate([encoding["input_ids"] for encoding in encodings]							,							axis=0	)
													lowerCAmelCase_         =      np.concatenate([encoding["attention_mask"] for encoding in encodings]							,							axis=0	)

										elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													lowerCAmelCase_         =      jnp.concatenate([encoding["input_ids"] for encoding in encodings]							,							axis=0	)
													lowerCAmelCase_         =      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]							,							axis=0	)

										elif return_tensors == "pt" and is_torch_available():
													import torch

													lowerCAmelCase_         =      torch.cat([encoding["input_ids"] for encoding in encodings]							,							dim=0	)
													lowerCAmelCase_         =      torch.cat([encoding["attention_mask"] for encoding in encodings]							,							dim=0	)

										elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													lowerCAmelCase_         =      tf.stack([encoding["input_ids"] for encoding in encodings]							,							axis=0	)
													lowerCAmelCase_         =      tf.stack([encoding["attention_mask"] for encoding in encodings]							,							axis=0	)

										else:
													raise ValueError("Target return tensor type could not be returned"	)

										lowerCAmelCase_         =      BatchEncoding()
										lowerCAmelCase_         =      input_ids
										lowerCAmelCase_         =      attention_mask

							if query_images is not None:
										lowerCAmelCase_         =      BatchEncoding()
										lowerCAmelCase_         =      self.image_processor(
										    _UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	).pixel_values
										lowerCAmelCase_         =      query_pixel_values

							if images is not None:
										lowerCAmelCase_         =      self.image_processor(_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	)

							if text is not None and images is not None:
										lowerCAmelCase_         =      image_features.pixel_values
										return encoding
							elif query_images is not None and images is not None:
										lowerCAmelCase_         =      image_features.pixel_values
										return encoding
							elif text is not None or query_images is not None:
										return encoding
							else:
										return BatchEncoding(data=dict(**_UpperCamelCase	)							,							tensor_type=_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> List[Any]:
							return self.image_processor.post_process(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Optional[Any]:
							return self.image_processor.post_process_object_detection(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> List[Any]:
							return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> List[str]:
							return self.tokenizer.batch_decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> List[Any]:
							return self.tokenizer.decode(*_UpperCamelCase							,							**_UpperCamelCase	)




				@property
				def 	__a		(   self	)							-> Tuple:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,							_UpperCamelCase							,							)
							return self.image_processor_class




				@property
				def 	__a		(   self	)							-> List[str]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,							_UpperCamelCase							,							)
							return self.image_processor





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "decoder.project_in_dim.weight": "decoder.project_in.weight",
			    "decoder.project_out_dim.weight": "decoder.project_out.weight",
			    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
			    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      list(sd.keys()							)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCAmelCase_         =      sd[key]
									# We split QKV in separate Q,K,V

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

									lowerCAmelCase_         =      q
									lowerCAmelCase_         =      k
									lowerCAmelCase_         =      v
									del sd[key]

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check results
			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



