"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AutoConfig.from_pretrained(UpperCamelCase__ )
							A__		   =							FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__ )
							A__		   =							checkpoints.load_tax_checkpoint(UpperCamelCase__ )

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

							if config.model_type == "t5":
														A__		   =							'SelfAttention'
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														A__		   =							'LocalSelfAttention'
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														A__		   =							'TransientGlobalSelfAttention'
							else:
														raise ValueError(
														    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
														    ' attribute with a value from [\'local\', \'transient-global].' )

							# Encoder
							for layer_index in range(config.num_layers ):
														A__		   =							F'''layers_{str(UpperCamelCase__ )}'''

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

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					A__		   =							tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

														# Assigning
														A__		   =							flax_model.params['encoder']['block'][str(UpperCamelCase__ )]['layer']
														A__		   =							tax_attention_key
														A__		   =							tax_attention_out
														A__		   =							tax_attention_query
														A__		   =							tax_attention_value

														A__		   =							tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					A__		   =							tax_global_layer_norm

														if split_mlp_wi:
																					A__		   =							tax_mlp_wi_a
																					A__		   =							tax_mlp_wi_a
														else:
																					A__		   =							tax_mlp_wi

														A__		   =							tax_mlp_wo
														A__		   =							tax_mlp_layer_norm

														A__		   =							flax_model_encoder_layer_block

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

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														A__		   =							tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
														A__		   =							tax_encoder_global_rel_embedding

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

							# Decoder
							for layer_index in range(config.num_layers ):
														A__		   =							F'''layers_{str(UpperCamelCase__ )}'''

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

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

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

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

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

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

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

														# Assigning
														A__		   =							flax_model.params['decoder']['block'][str(UpperCamelCase__ )]['layer']
														A__		   =							tax_attention_key
														A__		   =							tax_attention_out
														A__		   =							tax_attention_query
														A__		   =							tax_attention_value

														A__		   =							tax_pre_attention_layer_norm

														A__		   =							tax_enc_dec_attention_key
														A__		   =							tax_enc_dec_attention_out
														A__		   =							tax_enc_dec_attention_query
														A__		   =							tax_enc_dec_attention_value

														A__		   =							tax_cross_layer_norm

														if split_mlp_wi:
																					A__		   =							tax_mlp_wi_a
																					A__		   =							tax_mlp_wi_a
														else:
																					A__		   =							tax_mlp_wi

														A__		   =							tax_mlp_wo

														A__		   =							txa_mlp_layer_norm

														A__		   =							flax_model_decoder_layer_block

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

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

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

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														A__		   =							tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
				)
				parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
				parser.add_argument(
				    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






"""simple docstring"""




import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
				from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
				from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
				from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__lowerCamelCase							 =     DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCamelCase							 =     "main"
# Default branch name
__lowerCamelCase							 =     "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__lowerCamelCase							 =     "aaaaaaa"
# This commit does not exist, so we should 404.

__lowerCamelCase							 =     "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCamelCase							 =     "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"






@contextlib.contextmanager
def      UpperCAmelCase    (		):






							"""simple docstring"""





							print('Welcome!' )
							yield
							print('Bye!' )






@contextlib.contextmanager
def      UpperCAmelCase    (		):






							"""simple docstring"""





							print('Bonjour!' )
							yield
							print('Au revoir!' )
class 			UpperCamelCase__(	unittest.TestCase       ):





						def  snake_case__		(				self   )    -> List[str]:
													# If the spec is missing, importlib would not be able to import the module dynamically.
													assert transformers.__spec__ is not None
													assert importlib.util.find_spec('transformers'   ) is not None






class 			UpperCamelCase__(	unittest.TestCase       ):





						@unittest.mock.patch('sys.stdout'						,new_callable=io.StringIO   )
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													with ContextManagers([]   ):
																				print('Transformers are awesome!'   )
													# The print statement adds a new line at the end of the output
													self.assertEqual(mock_stdout.getvalue()						,'Transformers are awesome!\n'   )





						@unittest.mock.patch('sys.stdout'						,new_callable=io.StringIO   )
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[str]:
													with ContextManagers([context_en()]   ):
																				print('Transformers are awesome!'   )
													# The output should be wrapped with an English welcome and goodbye
													self.assertEqual(mock_stdout.getvalue()						,'Welcome!\nTransformers are awesome!\nBye!\n'   )





						@unittest.mock.patch('sys.stdout'						,new_callable=io.StringIO   )
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Any:
													with ContextManagers([context_fr(), context_en()]   ):
																				print('Transformers are awesome!'   )
													# The output should be wrapped with an English and French welcome and goodbye
													self.assertEqual(mock_stdout.getvalue()						,'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'   )





						@require_torch
						def  snake_case__		(				self   )    -> Union[str, Any]:
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels']   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels', 'next_sentence_label']   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['start_positions', 'end_positions']   )
													class 			UpperCamelCase__(	__A       ):
																			pass






													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels']   )





						@require_tf
						def  snake_case__		(				self   )    -> str:
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels']   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels', 'next_sentence_label']   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,['start_positions', 'end_positions']   )
													class 			UpperCamelCase__(	__A       ):
																			pass






													self.assertEqual(find_labels(__UpperCAmelCase   )						,['labels']   )





						@require_flax
						def  snake_case__		(				self   )    -> List[Any]:
													# Flax models don't have labels
													self.assertEqual(find_labels(__UpperCAmelCase   )						,[]   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,[]   )
													self.assertEqual(find_labels(__UpperCAmelCase   )						,[]   )
													class 			UpperCamelCase__(	__A       ):
																			pass






													self.assertEqual(find_labels(__UpperCAmelCase   )						,[]   )





