"""simple docstring"""







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


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

_a					:					Dict=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_a					:					Tuple=			{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_a					:					Optional[Any]=			{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_a					:					Union[str, Any]=			{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

_a					:					Optional[Any]=			{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_a					:					Any=			{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_a					:					List[Any]=			{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_a					:					Union[str, Any]=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_a					:					str=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_a					:					List[str]=			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Optional[int]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				List[str]   =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				Optional[int]   =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				List[Any]   =		DPRContextEncoderTokenizer







class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				List[str]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				int   =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[Any]   =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				str   =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Optional[int]   =		DPRQuestionEncoderTokenizer







_a					:					Dict=			collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_a					:					int=			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_a					:					int=			R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "

@add_start_docstrings(lowercase						)
class 		UpperCamelCase      :



				def __call__(self    :				int     ,  _A    :				Any     ,  _A    :				Optional[str] = None     ,  _A    :				Optional[str] = None     ,  _A    :				Union[bool, str] = False     ,  _A    :				Union[bool, str] = False     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[Union[str, TensorType]] = None     ,  _A    :				Optional[bool] = None     ,  **_A    :				Tuple     ,  )   ->     BatchEncoding:
					if titles is None and texts is None:
						return super().__call__(
						    _A     ,  padding=_A     ,  truncation=_A     ,  max_length=_A     ,  return_tensors=_A     ,  return_attention_mask=_A     ,  **_A     ,  )
					elif titles is None or texts is None:
						__snake_case      :							List[Any]						=			titles if texts is None else texts
						return super().__call__(
						    _A     ,  _A     ,  padding=_A     ,  truncation=_A     ,  max_length=_A     ,  return_tensors=_A     ,  return_attention_mask=_A     ,  **_A     ,  )
					__snake_case      :							Optional[int]						=			titles if not isinstance(_A     ,  _A) else [titles]
					__snake_case      :							Optional[Any]						=			texts if not isinstance(_A     ,  _A) else [texts]
					__snake_case      :							List[str]						=			len(_A)
					__snake_case      :							Union[str, Any]						=			questions if not isinstance(_A     ,  _A) else [questions] * n_passages
					assert len(_A) == len(
					    _A), f"There should be as many titles than texts but got {len(_A)} titles and {len(_A)} texts."
					__snake_case      :							Any						=			super().__call__(_A     ,  _A     ,  padding=_A     ,  truncation=_A)['input_ids']
					__snake_case      :							Optional[int]						=			super().__call__(_A     ,  add_special_tokens=_A     ,  padding=_A     ,  truncation=_A)['input_ids']
					__snake_case      :							int						=			{
					    'input_ids': [
					        (encoded_question_and_title + encoded_text)[:max_length]
					        if max_length is not None and truncation
					        else encoded_question_and_title + encoded_text
					        for encoded_question_and_title, encoded_text in zip(_A     ,  _A)
					    ]
					}
					if return_attention_mask is not False:
						__snake_case      :							Any						=			[]
						for input_ids in encoded_inputs["input_ids"]:
							attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
						__snake_case      :							Union[str, Any]						=			attention_mask
					return self.pad(_A     ,  padding=_A     ,  max_length=_A     ,  return_tensors=_A)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				BatchEncoding     ,  _A    :				DPRReaderOutput     ,  _A    :				int = 16     ,  _A    :				int = 64     ,  _A    :				int = 4     ,  )   ->     List[DPRSpanPrediction]:
					__snake_case      :							List[Any]						=			reader_input['input_ids']
					__snake_case		,    __snake_case		,    __snake_case      :							Optional[Any]						=			reader_output[:3]
					__snake_case      :							Optional[int]						=			len(_A)
					__snake_case      :							Any						=			sorted(range(_A)     ,  reverse=_A     ,  key=relevance_logits.__getitem__)
					__snake_case      :							List[DPRReaderOutput]						=			[]
					for doc_id in sorted_docs:
						__snake_case      :							Optional[int]						=			list(input_ids[doc_id])
						# assuming question & title information is at the beginning of the sequence
						__snake_case      :							Any						=			sequence_ids.index(self.sep_token_id     ,  2) + 1  # second sep id
						if sequence_ids[-1] == self.pad_token_id:
							__snake_case      :							str						=			sequence_ids.index(self.pad_token_id)
						else:
							__snake_case      :							Any						=			len(_A)

						__snake_case      :							List[str]						=			self._get_best_spans(
						    start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,  end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,  max_answer_length=_A     ,  top_spans=_A     ,  )
						for start_index, end_index in best_spans:
							start_index += passage_offset
							end_index += passage_offset
							nbest_spans_predictions.append(
							    DPRSpanPrediction(
							        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,  relevance_score=relevance_logits[doc_id]     ,  doc_id=_A     ,  start_index=_A     ,  end_index=_A     ,  text=self.decode(sequence_ids[start_index : end_index + 1])     ,  ))
						if len(_A) >= num_spans:
							break
					return nbest_spans_predictions[:num_spans]






				def  _lowercase					(self    :				Optional[int]     ,  _A    :				List[int]     ,  _A    :				List[int]     ,  _A    :				int     ,  _A    :				int     ,  )   ->     List[DPRSpanPrediction]:
					__snake_case      :							List[Any]						=			[]
					for start_index, start_score in enumerate(_A):
						for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
							scores.append(((start_index, start_index + answer_length), start_score + end_score))
					__snake_case      :							str						=			sorted(_A     ,  key=lambda _A: x[1]     ,  reverse=_A)
					__snake_case      :							Any						=			[]
					for (start_index, end_index), score in scores:
						assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
						__snake_case      :							Any						=			end_index - start_index + 1
						assert length <= max_answer_length, f"Span is too long: {length} > {max_answer_length}"
						if any(
						    start_index <= prev_start_index <= prev_end_index <= end_index
						    or prev_start_index <= start_index <= end_index <= prev_end_index
						    for (prev_start_index, prev_end_index) in chosen_span_intervals):
							continue
						chosen_span_intervals.append((start_index, end_index))

						if len(_A) == top_spans:
							break
					return chosen_span_intervals







@add_end_docstrings(lowercase						)
class 		UpperCamelCase      (   lowercase				,     lowercase						):
				UpperCAmelCase    :				str   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				str   =		READER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				Dict   =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				List[Any]   =		READER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Dict   =		["""input_ids""", """attention_mask"""]
				UpperCAmelCase    :				List[str]   =		DPRReaderTokenizer






"""simple docstring"""







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_a					:					Optional[int]=			logging.get_logger()

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			len(list(m.modules())) == 1 or isinstance(_A     ,  nn.Convad) or isinstance(_A     ,  nn.BatchNormad)
					if has_not_submodules:
						self.traced.append(_A)



				def __call__(self    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
					self.module(_A)
					[x.remove() for x in self.handles]
					return self






				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0     ,  self.traced))







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			list(filter(lambda _A: type(_A) not in self.dest_skip     ,  _A))

					if len(_A) != len(_A):
						raise Exception(
						    f"Numbers of operations are different. Source module has {len(_A)} operations while"
						    f" destination module has {len(_A)}.")

					for dest_m, src_m in zip(_A     ,  _A):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
							print(f"Transfered from={src_m} to={dest_m}")







def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

	assert torch.allclose(from_model(UpperCAmelCase_  )      ,							our_model(UpperCAmelCase_  ).logits  ), "The model logits don't match the original one."

	__snake_case      :							str						=			F"resnet{'-'.join(name.split('resnet'  )  )}"
	print(UpperCAmelCase_  )

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add model'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		# we can use the convnext one
		__snake_case      :							int						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'  )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add image processor'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		print(F"Pushed {checkpoint_name}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

	__snake_case      :							List[Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			num_labels
	__snake_case      :							Any						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Any						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	}

	if model_name:
		convert_weight_and_push(UpperCAmelCase_      ,							names_to_config[model_name]      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	return config, expected_shape


if __name__ == "__main__":
	_a					:					Optional[Any]=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default=None,
	    type=str,
	    help=(
	        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
	        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=Path,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--push_to_hub",
	    default=True,
	    type=bool,
	    required=False,
	    help="If True, push model and image processor to the hub.",
	)

	_a					:					Union[str, Any]=			parser.parse_args()
	_a					:					Path=			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





