

'''simple docstring'''







import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


_lowerCamelCase    =  collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	print(F"""Reading saved model from {model_file}"""	)
	_lowercase							=     torch.load(snake_case_							,       map_location=lambda snake_case_							,       snake_case_	: default_restore_location(snake_case_							,       """cpu"""	)	)
	return CheckpointState(**snake_case_	)
class 					__a   :

						def __init__(		self	:						List[str]    ,			lowercase__	:						Path)						->str:

							"""simple docstring"""



							_lowercase							=     src_file

						def 	_UpperCAmelCase							(		self	:						int)						->Optional[Any]:

							"""simple docstring"""



							raise NotImplementedError


						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						str    ,			*lowercase__	:						Optional[Any]    ,			**lowercase__	:						Any)						->"DPRState":

							"""simple docstring"""



							if comp_type.startswith("""c"""):
								return DPRContextEncoderState(*lowercase__    ,			**lowercase__)
							if comp_type.startswith("""q"""):
								return DPRQuestionEncoderState(*lowercase__    ,			**lowercase__)
							if comp_type.startswith("""r"""):
								return DPRReaderState(*lowercase__    ,			**lowercase__)
							else:
								raise ValueError("""Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'.""")






class 					__a   (		_snake_case  ):

						def 	_UpperCAmelCase							(		self	:						Dict)						->Tuple:

							"""simple docstring"""



							_lowercase							=     DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased""")[0]))
							print(f"""Loading DPR biencoder from {self.src_file}""")
							_lowercase							=     load_states_from_checkpoint(self.src_file)
							_lowercase		,		_lowercase							=     model.ctx_encoder, """ctx_model."""
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							_lowercase							=     {"""bert_model.embeddings.position_ids""": model.ctx_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
								if key.startswith(lowercase__):
									_lowercase							=     key[len(lowercase__) :]
									if not key.startswith("""encode_proj."""):
										_lowercase							=     """bert_model.""" + key
									_lowercase							=     value
							encoder.load_state_dict(lowercase__)
							return model






class 					__a   (		_snake_case  ):

						def 	_UpperCAmelCase							(		self	:						int)						->Dict:

							"""simple docstring"""



							_lowercase							=     DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased""")[0]))
							print(f"""Loading DPR biencoder from {self.src_file}""")
							_lowercase							=     load_states_from_checkpoint(self.src_file)
							_lowercase		,		_lowercase							=     model.question_encoder, """question_model."""
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							_lowercase							=     {"""bert_model.embeddings.position_ids""": model.question_encoder.bert_model.embeddings.position_ids}
							for key, value in saved_state.model_dict.items():
								if key.startswith(lowercase__):
									_lowercase							=     key[len(lowercase__) :]
									if not key.startswith("""encode_proj."""):
										_lowercase							=     """bert_model.""" + key
									_lowercase							=     value
							encoder.load_state_dict(lowercase__)
							return model






class 					__a   (		_snake_case  ):

						def 	_UpperCAmelCase							(		self	:						str)						->int:

							"""simple docstring"""



							_lowercase							=     DPRReader(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased""")[0]))
							print(f"""Loading DPR reader from {self.src_file}""")
							_lowercase							=     load_states_from_checkpoint(self.src_file)
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							_lowercase							=     {
							    """encoder.bert_model.embeddings.position_ids""": model.span_predictor.encoder.bert_model.embeddings.position_ids
							}
							for key, value in saved_state.model_dict.items():
								if key.startswith("""encoder.""") and not key.startswith("""encoder.encode_proj"""):
									_lowercase							=     """encoder.bert_model.""" + key[len("""encoder.""") :]
								_lowercase							=     value
							model.span_predictor.load_state_dict(lowercase__)
							return model






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     Path(snake_case_	)
	dest_dir.mkdir(exist_ok=snake_case_	)

	_lowercase							=     DPRState.from_type(snake_case_							,       src_file=snake_case_	)
	_lowercase							=     dpr_state.load_dpr_model()
	model.save_pretrained(snake_case_	)
	model.from_pretrained(snake_case_	)  # sanity check


if __name__ == "__main__":
							_lowerCamelCase    =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
							)
							parser.add_argument(
							    '--src',
							    type=str,
							    help=(
							        'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
							        ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
							        ' \'retriever\' checkpoints.'
							    ),
							)
							parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
							_lowerCamelCase    =  parser.parse_args()

							_lowerCamelCase    =  Path(args.src)
							_lowerCamelCase    =  F"""converted-{src_file.name}""" if args.dest is None else args.dest
							_lowerCamelCase    =  Path(dest_dir)
							assert src_file.exists()
							assert (
							    args.type is not None
							), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
							convert(args.type, src_file, dest_dir)







'''simple docstring'''







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     set()
	# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
	for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
		_lowercase							=     getattr(transformers.models.auto							,       snake_case_							,       snake_case_	)
		if module is None:
			continue
		# all mappings in a single auto modeling file
		_lowercase							=     [
		    x
		    for x in dir(snake_case_	)
		    if x.endswith("""_MAPPING_NAMES"""	)
		    and (x.startswith("""MODEL_"""	) or x.startswith("""TF_MODEL_"""	) or x.startswith("""FLAX_MODEL_"""	))
		]
		for name in mapping_names:
			_lowercase							=     getattr(snake_case_							,       snake_case_	)
			if mapping is not None:
				for v in mapping.values():
					if isinstance(snake_case_							,       (list, tuple)	):
						model_names.update(snake_case_	)
					elif isinstance(snake_case_							,       snake_case_	):
						model_names.add(snake_case_	)

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						):
	# All model names defined in auto mappings
	_lowercase							=     set(get_all_model_names()	)

	with open("""tests/utils/tiny_model_summary.json"""	) as fp:
		_lowercase							=     json.load(snake_case_	)
	_lowercase							=     set()
	for model_base_name in tiny_model_info:
		tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]	)

	# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
	_lowercase							=     model_names.difference(snake_case_	)
	for model_name in copy.copy(snake_case_	):
		if not model_name.startswith("""TF"""	) and F"""TF{model_name}""" in not_on_hub:
			tiny_models_names.remove(snake_case_	)
		elif model_name.startswith("""TF"""	) and model_name[2:] in not_on_hub:
			tiny_models_names.remove(snake_case_	)

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     COMPOSITE_MODELS.values()

	# All tiny model base names on Hub
	_lowercase							=     get_all_model_names()
	_lowercase							=     hf_api.list_models(
	    filter=ModelFilter(
	        author="""hf-internal-testing"""							,       )	)
	_lowercase							=     set()
	for x in models:
		_lowercase							=     x.modelId
		_lowercase		,		_lowercase							=     model.split("""/"""	)
		if not model.startswith("""tiny-random-"""	):
			continue
		_lowercase							=     model.replace("""tiny-random-"""							,       """"""	)
		if not model[0].isupper():
			continue
		if model not in model_names and model not in special_models:
			continue
		_models.add(snake_case_	)

	_lowercase							=     sorted(_models	)
	# All tiny model names on Hub
	_lowercase							=     {}
	for model in models:
		_lowercase							=     F"""hf-internal-testing/tiny-random-{model}"""
		_lowercase							=     model.split("""-"""	)[0]
		try:
			_lowercase							=     hf_api.repo_info(snake_case_	)
			_lowercase							=     {
			    """tokenizer_classes""": set(),
			    """processor_classes""": set(),
			    """model_classes""": set(),
			    """sha""": repo_info.sha,
			}
		except Exception:
			continue
		try:
			time.sleep(1	)
			_lowercase							=     AutoTokenizer.from_pretrained(snake_case_	)
			content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     AutoTokenizer.from_pretrained(snake_case_							,       use_fast=snake_case_	)
			content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     AutoImageProcessor.from_pretrained(snake_case_	)
			content["processor_classes"].add(img_p.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     AutoFeatureExtractor.from_pretrained(snake_case_	)
			if not isinstance(snake_case_							,       snake_case_	):
				content["processor_classes"].add(feat_p.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     getattr(snake_case_							,       snake_case_	)
			_lowercase							=     model_class.from_pretrained(snake_case_	)
			content["model_classes"].add(m.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     getattr(snake_case_							,       F"""TF{model}"""	)
			_lowercase							=     model_class.from_pretrained(snake_case_	)
			content["model_classes"].add(m.__class__.__name__	)
		except Exception:
			pass

		_lowercase							=     sorted(content["""tokenizer_classes"""]	)
		_lowercase							=     sorted(content["""processor_classes"""]	)
		_lowercase							=     sorted(content["""model_classes"""]	)

		_lowercase							=     content
		with open(os.path.join(snake_case_							,       """hub_tiny_model_summary.json"""	)							,       """w"""	) as fp:
			json.dump(snake_case_							,       snake_case_							,       ensure_ascii=snake_case_							,       indent=4	)


if __name__ == "__main__":
							_lowerCamelCase    =  argparse.ArgumentParser()
							parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
							_lowerCamelCase    =  parser.parse_args()

							# This has to be `spawn` to avoid hanging forever!
							multiprocessing.set_start_method('spawn')

							_lowerCamelCase    =  'tiny_models'
							_lowerCamelCase    =  True
							_lowerCamelCase    =  None
							_lowerCamelCase    =  get_tiny_model_names_from_repo()
							_lowerCamelCase    =  True
							_lowerCamelCase    =  True
							_lowerCamelCase    =  'hf-internal-testing'

							create_tiny_models(
							    output_path,
							    all,
							    model_types,
							    models_to_skip,
							    no_check,
							    upload,
							    organization,
							    token=os.environ.get('TOKEN', None),
							    num_workers=args.num_workers,
							)




