


"""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 __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					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"]:
								SCREAMING_SNAKE_CASE__  				=					getattr(transformers.models.auto    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								SCREAMING_SNAKE_CASE__  				=					[
								    x
								    for x in dir(lowerCamelCase_	)
								    if x.endswith("_MAPPING_NAMES"	)
								    and (x.startswith("MODEL_"	) or x.startswith("TF_MODEL_"	) or x.startswith("FLAX_MODEL_"	))
								]
								for name in mapping_names:
												SCREAMING_SNAKE_CASE__  				=					getattr(lowerCamelCase_    ,       lowerCamelCase_	)
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(lowerCamelCase_    ,       (list, tuple)	):
																								model_names.update(lowerCamelCase_	)
																				elif isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
																								model_names.add(lowerCamelCase_	)

				return sorted(lowerCamelCase_	)


def __lowercase  (   ):
				# All model names defined in auto mappings
				SCREAMING_SNAKE_CASE__  				=					set(get_all_model_names()	)

				with open("tests/utils/tiny_model_summary.json"	) as fp:
								SCREAMING_SNAKE_CASE__  				=					json.load(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					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.
				SCREAMING_SNAKE_CASE__  				=					model_names.difference(lowerCamelCase_	)
				for model_name in copy.copy(lowerCamelCase_	):
								if not model_name.startswith("TF"	) and F'''TF{model_name}''' in not_on_hub:
												tiny_models_names.remove(lowerCamelCase_	)
								elif model_name.startswith("TF"	) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(lowerCamelCase_	)

				return sorted(lowerCamelCase_	)


def __lowercase  (   lowerCamelCase_	:   List[str]	):
				SCREAMING_SNAKE_CASE__  				=					COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				SCREAMING_SNAKE_CASE__  				=					get_all_model_names()
				SCREAMING_SNAKE_CASE__  				=					hf_api.list_models(
				    filter=ModelFilter(
				        author="hf-internal-testing"    ,       )	)
				SCREAMING_SNAKE_CASE__  				=					set()
				for x in models:
								SCREAMING_SNAKE_CASE__  				=					x.modelId
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					model.split("/"	)
								if not model.startswith("tiny-random-"	):
												continue
								SCREAMING_SNAKE_CASE__  				=					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(lowerCamelCase_	)

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

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

								SCREAMING_SNAKE_CASE__  				=					content
								with open(os.path.join(lowerCamelCase_    ,       "hub_tiny_model_summary.json"	)    ,       "w"	) as fp:
												json.dump(lowerCamelCase_    ,       lowerCamelCase_    ,       ensure_ascii=lowerCamelCase_    ,       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,
	)




"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
