



'''simple docstring'''






import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

A_				:					Any							=      [[1_2, 1_0], [3, 9]]
A_				:					Tuple							=      [[3, 4], [7, 4]]
A_				:					List[str]							=      [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
A_				:					Union[str, Any]							=      [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
A_				:					Optional[int]							=      [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
A_				:					Tuple							=      [1]
A_				:					List[Any]							=      [2]

A_				:					Dict							=      logging.getLogger()
A_				:					Optional[int]							=      logging.DEBUG
A_				:					List[Any]							=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("""mat1""", """mat2""")			,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						str:





       '''simple docstring'''


       if (np.array(lowerCAmelCase_						)).shape < (2, 2) or (np.array(lowerCAmelCase_						)).shape < (2, 2):
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(F'''\n\t{test_addition.__name__} returned integer'''						)
                     matop.add(lowerCAmelCase_			,  lowerCAmelCase_						)
       elif (np.array(lowerCAmelCase_						)).shape == (np.array(lowerCAmelCase_						)).shape:
              logger.info(F'''\n\t{test_addition.__name__} with same matrix dims'''						)
              _UpperCAmelCase    :		int     	=					(np.array(lowerCAmelCase_						) + np.array(lowerCAmelCase_						)).tolist()
              _UpperCAmelCase    :		int     	=					matop.add(lowerCAmelCase_			,  lowerCAmelCase_						)
              assert theo == act
       else:
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(F'''\n\t{test_addition.__name__} with different matrix dims'''						)
                     matop.add(lowerCAmelCase_			,  lowerCAmelCase_						)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("""mat1""", """mat2""")			,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[Any]:





       '''simple docstring'''


       if (np.array(lowerCAmelCase_						)).shape < (2, 2) or (np.array(lowerCAmelCase_						)).shape < (2, 2):
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(F'''\n\t{test_subtraction.__name__} returned integer'''						)
                     matop.subtract(lowerCAmelCase_			,  lowerCAmelCase_						)
       elif (np.array(lowerCAmelCase_						)).shape == (np.array(lowerCAmelCase_						)).shape:
              logger.info(F'''\n\t{test_subtraction.__name__} with same matrix dims'''						)
              _UpperCAmelCase    :		Tuple     	=					(np.array(lowerCAmelCase_						) - np.array(lowerCAmelCase_						)).tolist()
              _UpperCAmelCase    :		int     	=					matop.subtract(lowerCAmelCase_			,  lowerCAmelCase_						)
              assert theo == act
       else:
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(F'''\n\t{test_subtraction.__name__} with different matrix dims'''						)
                     assert matop.subtract(lowerCAmelCase_			,  lowerCAmelCase_						)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("""mat1""", """mat2""")			,  [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if (np.array(lowerCAmelCase_						)).shape < (2, 2) or (np.array(lowerCAmelCase_						)).shape < (2, 2):
              logger.info(F'''\n\t{test_multiplication.__name__} returned integer'''						)
              with pytest.raises(lowerCAmelCase_						):
                     matop.add(lowerCAmelCase_			,  lowerCAmelCase_						)
       elif (np.array(lowerCAmelCase_						)).shape == (np.array(lowerCAmelCase_						)).shape:
              logger.info(F'''\n\t{test_multiplication.__name__} meets dim requirements'''						)
              _UpperCAmelCase    :		str     	=					(np.matmul(lowerCAmelCase_			,  lowerCAmelCase_						)).tolist()
              _UpperCAmelCase    :		Union[str, Any]     	=					matop.multiply(lowerCAmelCase_			,  lowerCAmelCase_						)
              assert theo == act
       else:
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(
                         F'''\n\t{test_multiplication.__name__} does not meet dim requirements'''						)
                     assert matop.subtract(lowerCAmelCase_			,  lowerCAmelCase_						)




@pytest.mark.mat_ops
def        snake_case_   (	)->						Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[Any]     	=					(3.5 * np.array(lowerCAmelCase_						)).tolist()
       _UpperCAmelCase    :		List[Any]     	=					matop.scalar_multiply(lowerCAmelCase_			,  3.5						)
       assert theo == act




@pytest.mark.mat_ops
def        snake_case_   (	)->						Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					(np.identity(5						)).tolist()
       _UpperCAmelCase    :		Union[str, Any]     	=					matop.identity(5						)
       assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize("""mat"""			,  [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]						)
def        snake_case_   (	lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if (np.array(lowerCAmelCase_						)).shape < (2, 2):
              with pytest.raises(lowerCAmelCase_						):
                     logger.info(F'''\n\t{test_transpose.__name__} returned integer'''						)
                     matop.transpose(lowerCAmelCase_						)
       else:
              _UpperCAmelCase    :		str     	=					(np.transpose(lowerCAmelCase_						)).tolist()
              _UpperCAmelCase    :		List[str]     	=					matop.transpose(lowerCAmelCase_			,  return_map=lowerCAmelCase_						)
              assert theo == act






'''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,
							)




