




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  snake_case (							)							->							str:




	'''simple docstring'''


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

	return sorted(A      )






def  snake_case (							)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							set(get_all_model_names()      )

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

	return sorted(A      )






def  snake_case (							A      )							->							Dict:




	'''simple docstring'''


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

	# All tiny model base names on Hub
	lowerCamelCase_							:		List[Any]							       =							get_all_model_names()
	lowerCamelCase_							:		int							       =							hf_api.list_models(
	    filter=ModelFilter(
	        author='hf-internal-testing'						,       )      )
	lowerCamelCase_							:		int							       =							set()
	for x in models:
		lowerCamelCase_							:		Dict							       =							x.modelId
		lowerCamelCase_							:		Optional[int]							       =							model.split('/'      )
		if not model.startswith('tiny-random-'      ):
			continue
		lowerCamelCase_							:		Tuple							       =							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(A      )

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

		lowerCamelCase_							:		Optional[Any]							       =							sorted(content['tokenizer_classes']      )
		lowerCamelCase_							:		Union[str, Any]							       =							sorted(content['processor_classes']      )
		lowerCamelCase_							:		Union[str, Any]							       =							sorted(content['model_classes']      )

		lowerCamelCase_							:		List[Any]							       =							content
		with open(os.path.join(A						,       'hub_tiny_model_summary.json'      )						,       'w'      ) as fp:
			json.dump(A						,       A						,       ensure_ascii=A						,       indent=4      )


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

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

			UpperCamelCase__		=						'tiny_models'
			UpperCamelCase__		=						True
			UpperCamelCase__		=						None
			UpperCamelCase__		=						get_tiny_model_names_from_repo()
			UpperCamelCase__		=						True
			UpperCamelCase__		=						True
			UpperCamelCase__		=						'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,
			)






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__		=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}






@lru_cache()
def  snake_case (							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							(
	    list(range(ord('!'      )						,       ord('~'      ) + 1      )      ) + list(range(ord('¡'      )						,       ord('¬'      ) + 1      )      ) + list(range(ord('®'      )						,       ord('ÿ'      ) + 1      )      )
	)
	lowerCamelCase_							:		Dict							       =							bs[:]
	lowerCamelCase_							:		Dict							       =							0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(A      )
			cs.append(2**8 + n      )
			n += 1
	lowerCamelCase_							:		List[str]							       =							[chr(A      ) for n in cs]
	return dict(zip(A						,       A      )      )






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							set()
	lowerCamelCase_							:		Optional[int]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Optional[Any]							       =							char
	return pairs



class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			["""input_ids""", """attention_mask"""]

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else bos_token
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else eos_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else sep_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else cls_token
						lowerCamelCase_							:		Any							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else unk_token
						lowerCamelCase_							:		Tuple							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else mask_token

						super().__init__(
						    errors=__lowercase			,	bos_token=__lowercase			,	eos_token=__lowercase			,	unk_token=__lowercase			,	sep_token=__lowercase			,	cls_token=__lowercase			,	pad_token=__lowercase			,	mask_token=__lowercase			,	add_prefix_space=__lowercase			,	**__lowercase			,	)

						with open(__lowercase			,	encoding='utf-8' ) as vocab_handle:
							lowerCamelCase_							:		Tuple							       =							json.load(__lowercase )
						lowerCamelCase_							:		Dict							       =							{v: k for k, v in self.encoder.items()}
						lowerCamelCase_							:		Any							       =							errors  # how to handle errors in decoding
						lowerCamelCase_							:		Dict							       =							bytes_to_unicode()
						lowerCamelCase_							:		Any							       =							{v: k for k, v in self.byte_encoder.items()}
						with open(__lowercase			,	encoding='utf-8' ) as merges_handle:
							lowerCamelCase_							:		Dict							       =							merges_handle.read().split('\n' )[1:-1]
						lowerCamelCase_							:		Tuple							       =							[tuple(merge.split() ) for merge in bpe_merges]
						lowerCamelCase_							:		Tuple							       =							dict(zip(__lowercase			,	range(len(__lowercase ) ) ) )
						lowerCamelCase_							:		List[str]							       =							{}
						lowerCamelCase_							:		Union[str, Any]							       =							add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						lowerCamelCase_							:		Dict							       =							re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return dict(self.encoder			,	**self.added_tokens_encoder )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		Any							       =							tuple(__lowercase )
						lowerCamelCase_							:		Tuple							       =							get_pairs(__lowercase )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:		str							       =							min(__lowercase			,	key=lambda __lowercase : self.bpe_ranks.get(__lowercase			,	float('inf' ) ) )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							bigram
							lowerCamelCase_							:		str							       =							[]
							lowerCamelCase_							:		int							       =							0
							while i < len(__lowercase ):
								try:
									lowerCamelCase_							:		Tuple							       =							word.index(__lowercase			,	__lowercase )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									lowerCamelCase_							:		Any							       =							j

								if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							lowerCamelCase_							:		List[Any]							       =							tuple(__lowercase )
							lowerCamelCase_							:		Optional[Any]							       =							new_word
							if len(__lowercase ) == 1:
								break
							else:
								lowerCamelCase_							:		Optional[Any]							       =							get_pairs(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							' '.join(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							word
						return word

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							[]
						for token in re.findall(self.pat			,	__lowercase ):
							lowerCamelCase_							:		int							       =							''.join(
							    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.encoder.get(__lowercase			,	self.encoder.get(self.unk_token ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							''.join(__lowercase )
						lowerCamelCase_							:		Tuple							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if not os.path.isdir(__lowercase ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
						lowerCamelCase_							:		List[str]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		Union[str, Any]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as f:
							f.write(json.dumps(self.encoder			,	indent=2			,	sort_keys=__lowercase			,	ensure_ascii=__lowercase ) + '\n' )

						lowerCamelCase_							:		Dict							       =							0
						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as writer:
							writer.write('#version: 0.2\n' )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda __lowercase : kv[1] ):
								if index != token_index:
									logger.warning(
									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
									    ' Please check that the tokenizer is not corrupted!' )
									lowerCamelCase_							:		Tuple							       =							token_index
								writer.write(' '.join(__lowercase ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.cls_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowercase			,	token_ids_a=__lowercase			,	already_has_special_tokens=__lowercase )

						if token_ids_a is None:
							return [1] + ([0] * len(__lowercase )) + [1]
						return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)
