
'''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     snake_case__							(      )   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  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        =  getattr(transformers.models.auto				,       _A				,       _A			)
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowerCAmelCase        =  [
				    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        =  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__							(      )   ->     str:

		'''simple docstring'''


		lowerCAmelCase        =  set(get_all_model_names()			)

		with open("""tests/utils/tiny_model_summary.json"""			) as fp:
				lowerCAmelCase        =  json.load(_A			)
		lowerCAmelCase        =  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        =  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: List[Any]			)   ->     List[str]:

		'''simple docstring'''


		lowerCAmelCase        =  COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		lowerCAmelCase        =  get_all_model_names()
		lowerCAmelCase        =  hf_api.list_models(
		    filter=ModelFilter(
		        author="""hf-internal-testing"""				,       )			)
		lowerCAmelCase        =  set()
		for x in models:
				lowerCAmelCase        =  x.modelId
				lowerCAmelCase ,					lowerCAmelCase        =  model.split("""/"""			)
				if not model.startswith("""tiny-random-"""			):
						continue
				lowerCAmelCase        =  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        =  sorted(_models			)
		# All tiny model names on Hub
		lowerCAmelCase        =  {}
		for model in models:
				lowerCAmelCase        =  f"hf-internal-testing/tiny-random-{model}"
				lowerCAmelCase        =  model.split("""-"""			)[0]
				try:
						lowerCAmelCase        =  hf_api.repo_info(_A			)
						lowerCAmelCase        =  {
						    """tokenizer_classes""": set(),
						    """processor_classes""": set(),
						    """model_classes""": set(),
						    """sha""": repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1			)
						lowerCAmelCase        =  AutoTokenizer.from_pretrained(_A			)
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase        =  AutoTokenizer.from_pretrained(_A				,       use_fast=_A			)
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase        =  AutoImageProcessor.from_pretrained(_A			)
						content["processor_classes"].add(img_p.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase        =  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        =  getattr(_A				,       _A			)
						lowerCAmelCase        =  model_class.from_pretrained(_A			)
						content["model_classes"].add(m.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase        =  getattr(_A				,       f"TF{model}"			)
						lowerCAmelCase        =  model_class.from_pretrained(_A			)
						content["model_classes"].add(m.__class__.__name__			)
				except Exception:
						pass

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

				lowerCAmelCase        =  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__":
						__lowercase				   =  argparse.ArgumentParser()
						parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
						__lowercase				   =  parser.parse_args()

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

						__lowercase				   =  "tiny_models"
						__lowercase				   =  True
						__lowercase				   =  None
						__lowercase				   =  get_tiny_model_names_from_repo()
						__lowercase				   =  True
						__lowercase				   =  True
						__lowercase				   =  "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 argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




