
"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      TaConfig.from_pretrained(snake_case_     )
					SCREAMING_SNAKE_CASE           =      FlaxTaForConditionalGeneration(config=snake_case_     )
					SCREAMING_SNAKE_CASE           =      checkpoints.load_tax_checkpoint(snake_case_     )

					SCREAMING_SNAKE_CASE           =      'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

					# Encoder
					for layer_index in range(config.num_layers     ):
										SCREAMING_SNAKE_CASE           =      f'''layers_{str(snake_case_     )}'''

										# Self-Attention
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

										# Layer Normalization
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

										if split_mlp_wi:
															SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
															SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
										else:
															SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

										# Layer Normalization
										SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

										# Assigning
										SCREAMING_SNAKE_CASE           =      tax_attention_key
										SCREAMING_SNAKE_CASE           =      tax_attention_out
										SCREAMING_SNAKE_CASE           =      tax_attention_query
										SCREAMING_SNAKE_CASE           =      tax_attention_value

										SCREAMING_SNAKE_CASE           =      tax_attention_layer_norm

										if split_mlp_wi:
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi_a
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi_a
										else:
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi

										SCREAMING_SNAKE_CASE           =      tax_mlp_wo
										SCREAMING_SNAKE_CASE           =      tax_mlp_layer_norm

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

					# Assigning
					SCREAMING_SNAKE_CASE           =      tax_model['target']['encoder']['encoder_norm']['scale']
					SCREAMING_SNAKE_CASE           =      tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_decoder_layers     ):
										SCREAMING_SNAKE_CASE           =      f'''layers_{str(snake_case_     )}'''

										# Self-Attention
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

										# Layer Normalization
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
										    'scale'
										]

										# Encoder-Decoder-Attention
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
										    'kernel'
										]
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
										    'kernel'
										]
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
										    'kernel'
										]
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
										    'kernel'
										]

										# Layer Normalization
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

										# MLP
										if split_mlp_wi:
															SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
															SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
										else:
															SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

										# Layer Normalization
										SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

										# Assigning
										SCREAMING_SNAKE_CASE           =      tax_attention_key
										SCREAMING_SNAKE_CASE           =      tax_attention_out
										SCREAMING_SNAKE_CASE           =      tax_attention_query
										SCREAMING_SNAKE_CASE           =      tax_attention_value

										SCREAMING_SNAKE_CASE           =      tax_pre_attention_layer_norm

										SCREAMING_SNAKE_CASE           =      tax_enc_dec_attention_key
										SCREAMING_SNAKE_CASE           =      tax_enc_dec_attention_out
										SCREAMING_SNAKE_CASE           =      tax_enc_dec_attention_query
										SCREAMING_SNAKE_CASE           =      tax_enc_dec_attention_value

										SCREAMING_SNAKE_CASE           =      tax_cross_layer_norm

										if split_mlp_wi:
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi_a
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi_a
										else:
															SCREAMING_SNAKE_CASE           =      tax_mlp_wi

										SCREAMING_SNAKE_CASE           =      tax_mlp_wo

										SCREAMING_SNAKE_CASE           =      txa_mlp_layer_norm

					# Decoder Normalization
					SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder']['decoder_norm']['scale']
					SCREAMING_SNAKE_CASE           =      txa_decoder_norm

					# Only for layer 0:
					SCREAMING_SNAKE_CASE           =      tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
					SCREAMING_SNAKE_CASE           =      tax_decoder_rel_embedding

					# Token Embeddings
					SCREAMING_SNAKE_CASE           =      tax_model['target']['token_embedder']['embedding']
					SCREAMING_SNAKE_CASE           =      txa_token_embeddings

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

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


if __name__ == "__main__":
	snake_case					  =						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.'
	)
	snake_case					  =						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 					_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,
							)




