

'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    Tuple						,lowerCamelCase  :    Union[str, Any]     ):
       _A  :			str											=      TaConfig.from_pretrained(lowerCamelCase     )
       _A  :			Dict											=      FlaxTaForConditionalGeneration(config=lowerCamelCase     )
       _A  :			Optional[Any]											=      checkpoints.load_tax_checkpoint(lowerCamelCase     )

       _A  :			Union[str, Any]											=      'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

       # Encoder
       for layer_index in range(config.num_layers     ):
              _A  :			Tuple											=      F'layers_{str(lowerCamelCase     )}'

              # Self-Attention
              _A  :			str											=      tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
              _A  :			Tuple											=      tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
              _A  :			str											=      tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
              _A  :			Union[str, Any]											=      tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

              # Layer Normalization
              _A  :			Optional[int]											=      tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

              if split_mlp_wi:
                     _A  :			Union[str, Any]											=      tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                     _A  :			int											=      tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
              else:
                     _A  :			Tuple											=      tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

              _A  :			List[Any]											=      tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

              # Layer Normalization
              _A  :			Union[str, Any]											=      tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

              # Assigning
              _A  :			Union[str, Any]											=      tax_attention_key
              _A  :			Any											=      tax_attention_out
              _A  :			str											=      tax_attention_query
              _A  :			Optional[int]											=      tax_attention_value

              _A  :			List[str]											=      tax_attention_layer_norm

              if split_mlp_wi:
                     _A  :			List[str]											=      tax_mlp_wi_a
                     _A  :			Any											=      tax_mlp_wi_a
              else:
                     _A  :			Optional[Any]											=      tax_mlp_wi

              _A  :			Optional[Any]											=      tax_mlp_wo
              _A  :			Optional[Any]											=      tax_mlp_layer_norm

       # Only for layer 0:
       _A  :			List[Any]											=      tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
       _A  :			Any											=      tax_encoder_rel_embedding

       # Assigning
       _A  :			Optional[int]											=      tax_model['target']['encoder']['encoder_norm']['scale']
       _A  :			Union[str, Any]											=      tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers     ):
              _A  :			str											=      F'layers_{str(lowerCamelCase     )}'

              # Self-Attention
              _A  :			Dict											=      tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
              _A  :			Union[str, Any]											=      tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
              _A  :			Optional[int]											=      tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
              _A  :			Optional[Any]											=      tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

              # Layer Normalization
              _A  :			List[Any]											=      tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                  'scale'
              ]

              # Encoder-Decoder-Attention
              _A  :			Union[str, Any]											=      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
                  'kernel'
              ]
              _A  :			Tuple											=      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                  'kernel'
              ]
              _A  :			str											=      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                  'kernel'
              ]
              _A  :			Dict											=      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                  'kernel'
              ]

              # Layer Normalization
              _A  :			Any											=      tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

              # MLP
              if split_mlp_wi:
                     _A  :			Any											=      tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                     _A  :			List[Any]											=      tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
              else:
                     _A  :			Dict											=      tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

              _A  :			Any											=      tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

              # Layer Normalization
              _A  :			int											=      tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

              # Assigning
              _A  :			str											=      tax_attention_key
              _A  :			List[str]											=      tax_attention_out
              _A  :			Union[str, Any]											=      tax_attention_query
              _A  :			Optional[Any]											=      tax_attention_value

              _A  :			Tuple											=      tax_pre_attention_layer_norm

              _A  :			int											=      tax_enc_dec_attention_key
              _A  :			Optional[int]											=      tax_enc_dec_attention_out
              _A  :			List[str]											=      tax_enc_dec_attention_query
              _A  :			List[str]											=      tax_enc_dec_attention_value

              _A  :			Any											=      tax_cross_layer_norm

              if split_mlp_wi:
                     _A  :			int											=      tax_mlp_wi_a
                     _A  :			int											=      tax_mlp_wi_a
              else:
                     _A  :			List[Any]											=      tax_mlp_wi

              _A  :			str											=      tax_mlp_wo

              _A  :			Optional[Any]											=      txa_mlp_layer_norm

       # Decoder Normalization
       _A  :			Any											=      tax_model['target']['decoder']['decoder_norm']['scale']
       _A  :			List[Any]											=      txa_decoder_norm

       # Only for layer 0:
       _A  :			Tuple											=      tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
       _A  :			Optional[Any]											=      tax_decoder_rel_embedding

       # Token Embeddings
       _A  :			str											=      tax_model['target']['token_embedder']['embedding']
       _A  :			List[str]											=      txa_token_embeddings

       # LM Head (only in v1.1 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              _A  :			Optional[int]											=      tax_model['target']['decoder']['logits_dense']['kernel']

       flax_model.save_pretrained(lowerCamelCase     )
       print('T5X Model was sucessfully converted!'     )


if __name__ == "__main__":
   A   :  List[str]       		=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
   )
   parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
   parser.add_argument(
       '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
   )
   A   :  List[Any]       		=				parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










'''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    lowerCAmelCase__ (       ):
       _A  :			Union[str, Any]											=      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"]:
              _A  :			Optional[Any]											=      getattr(transformers.models.auto						,lowerCamelCase						,lowerCamelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _A  :			Any											=      [
                  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:
                     _A  :			Tuple											=      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    lowerCAmelCase__ (       ):
       # All model names defined in auto mappings
       _A  :			Union[str, Any]											=      set(get_all_model_names()     )

       with open('tests/utils/tiny_model_summary.json'     ) as fp:
              _A  :			Any											=      json.load(lowerCamelCase     )
       _A  :			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.
       _A  :			List[Any]											=      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    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]     ):
       _A  :			Optional[int]											=      COMPOSITE_MODELS.values()

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

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

              _A  :			Any											=      sorted(content['tokenizer_classes']     )
              _A  :			Tuple											=      sorted(content['processor_classes']     )
              _A  :			List[str]											=      sorted(content['model_classes']     )

              _A  :			int											=      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__":
   A   :  Dict       		=				argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A   :  Optional[int]       		=				parser.parse_args()

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

   A   :  Any       		=				'''tiny_models'''
   A   :  Union[str, Any]       		=				True
   A   :  List[Any]       		=				None
   A   :  Any       		=				get_tiny_model_names_from_repo()
   A   :  List[str]       		=				True
   A   :  Union[str, Any]       		=				True
   A   :  Tuple       		=				'''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,
   )






