

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        _lowerCAmelCase      (						):


					'''simple docstring'''






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

					return sorted(UpperCAmelCase				)



def        _lowerCAmelCase      (						):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =set(get_all_model_names()				)

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

					return sorted(UpperCAmelCase				)



def        _lowerCAmelCase      (						UpperCAmelCase     :							Union[str, Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Union[str, Any]         =COMPOSITE_MODELS.values()

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

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

										UpperCamelCase__		:    Dict         =sorted(content['''tokenizer_classes''']				)
										UpperCamelCase__		:    Optional[int]         =sorted(content['''processor_classes''']				)
										UpperCamelCase__		:    List[str]         =sorted(content['''model_classes''']				)

										UpperCamelCase__		:    Dict         =content
										with open(os.path.join(UpperCAmelCase      ,							'''hub_tiny_model_summary.json'''				)      ,							'''w'''				) as fp:
															json.dump(UpperCAmelCase      ,							UpperCAmelCase      ,							ensure_ascii=UpperCAmelCase      ,							indent=4				)


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  List[str]        = argparse.ArgumentParser()
				parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = parser.parse_args()

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

				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """tiny_models"""
				_SCREAMING_SNAKE_CASE       :  int        = True
				_SCREAMING_SNAKE_CASE       :  int        = None
				_SCREAMING_SNAKE_CASE       :  List[Any]        = get_tiny_model_names_from_repo()
				_SCREAMING_SNAKE_CASE       :  int        = True
				_SCREAMING_SNAKE_CASE       :  Any        = True
				_SCREAMING_SNAKE_CASE       :  Dict        = """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 io
import json
import subprocess


_SCREAMING_SNAKE_CASE       :  List[Any]        = [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_SCREAMING_SNAKE_CASE       :  str        = 8



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         ={}
					for src, tgt in pairs:
										UpperCamelCase__		:    Optional[int]         =F'''{src}-{tgt}'''
										UpperCamelCase__		:    int         =F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    List[Any]         =F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    Any         ={'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


_SCREAMING_SNAKE_CASE       :  List[str]        = get_all_data(pairs, n_objs)
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
				_SCREAMING_SNAKE_CASE       :  Any        = json.dump(text, f, indent=2, ensure_ascii=False)


