


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Optional[int]:



							'''simple docstring'''





							UpperCAmelCase__										=     AutoConfig.from_pretrained(__A			)
							UpperCAmelCase__										=     FlaxAutoModelForSeqaSeqLM.from_config(config=__A			)
							UpperCAmelCase__										=     checkpoints.load_tax_checkpoint(__A			)

							UpperCAmelCase__										=     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

							if config.model_type == "t5":
														UpperCAmelCase__										=     "SelfAttention"
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														UpperCAmelCase__										=     "LocalSelfAttention"
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCAmelCase__										=     "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			):
														UpperCAmelCase__										=     F"""layers_{str(__A			)}"""

														# Self-Attention
														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
														UpperCAmelCase__										=     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":
																					UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

														# Layer Normalization
														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														UpperCAmelCase__										=     flax_model.params["encoder"]["block"][str(__A			)]["layer"]
														UpperCAmelCase__										=     tax_attention_key
														UpperCAmelCase__										=     tax_attention_out
														UpperCAmelCase__										=     tax_attention_query
														UpperCAmelCase__										=     tax_attention_value

														UpperCAmelCase__										=     tax_attention_layer_norm

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

														if split_mlp_wi:
																					UpperCAmelCase__										=     tax_mlp_wi_a
																					UpperCAmelCase__										=     tax_mlp_wi_a
														else:
																					UpperCAmelCase__										=     tax_mlp_wi

														UpperCAmelCase__										=     tax_mlp_wo
														UpperCAmelCase__										=     tax_mlp_layer_norm

														UpperCAmelCase__										=     flax_model_encoder_layer_block

							# Only for layer 0:
							UpperCAmelCase__										=     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
							UpperCAmelCase__										=     tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCAmelCase__										=     tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
														UpperCAmelCase__										=     tax_encoder_global_rel_embedding

							# Assigning
							UpperCAmelCase__										=     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
							UpperCAmelCase__										=     tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers			):
														UpperCAmelCase__										=     F"""layers_{str(__A			)}"""

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

														# Layer Normalization
														UpperCAmelCase__										=     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
														    "scale"
														]

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

														# Layer Normalization
														UpperCAmelCase__										=     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

														UpperCAmelCase__										=     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

														# Layer Normalization
														UpperCAmelCase__										=     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

														# Assigning
														UpperCAmelCase__										=     flax_model.params["decoder"]["block"][str(__A			)]["layer"]
														UpperCAmelCase__										=     tax_attention_key
														UpperCAmelCase__										=     tax_attention_out
														UpperCAmelCase__										=     tax_attention_query
														UpperCAmelCase__										=     tax_attention_value

														UpperCAmelCase__										=     tax_pre_attention_layer_norm

														UpperCAmelCase__										=     tax_enc_dec_attention_key
														UpperCAmelCase__										=     tax_enc_dec_attention_out
														UpperCAmelCase__										=     tax_enc_dec_attention_query
														UpperCAmelCase__										=     tax_enc_dec_attention_value

														UpperCAmelCase__										=     tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCAmelCase__										=     tax_mlp_wi_a
																					UpperCAmelCase__										=     tax_mlp_wi_a
														else:
																					UpperCAmelCase__										=     tax_mlp_wi

														UpperCAmelCase__										=     tax_mlp_wo

														UpperCAmelCase__										=     txa_mlp_layer_norm

														UpperCAmelCase__										=     flax_model_decoder_layer_block

							# Decoder Normalization
							UpperCAmelCase__										=     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
							UpperCAmelCase__										=     txa_decoder_norm

							# Only for layer 0:
							UpperCAmelCase__										=     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
							UpperCAmelCase__										=     tax_decoder_rel_embedding

							# Token Embeddings
							UpperCAmelCase__										=     tax_model["target"]["token_embedder"]["embedding"]
							UpperCAmelCase__										=     txa_token_embeddings

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

							flax_model.save_pretrained(__A			)
							print("T5X Model was sucessfully converted!"			)


if __name__ == "__main__":
				A      = 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."
				)
				A      = parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Dict		,						_lowercase      :					int		,						_lowercase      :					List[Any]=None		,						_lowercase      :					str=True		,						_lowercase      :					str=None		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     config_class
         UpperCAmelCase__										=     has_text_modality
         UpperCAmelCase__										=     kwargs
         UpperCAmelCase__										=     common_properties






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         UpperCAmelCase__										=     (
             ["hidden_size", "num_attention_heads", "num_hidden_layers"]
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
                common_properties.extend(["vocab_size"] )

         # Test that config has the common properties as getters
         for prop in common_properties:
                self.parent.assertTrue(hasattr(_lowercase		,						_lowercase )		,						msg=F"""`{prop}` does not exist""" )

         # Test that config has the common properties as setter
         for idx, name in enumerate(_lowercase ):
                try:
                       setattr(_lowercase		,						_lowercase		,						_lowercase )
                       self.parent.assertEqual(
                           getattr(_lowercase		,						_lowercase )		,						_lowercase		,						msg=F"""`{name} value {idx} expected, but was {getattr(_lowercase		,						_lowercase )}""" )
                except NotImplementedError:
                       # Some models might not be able to implement setters for common_properties
                       # In that case, a NotImplementedError is raised
                       pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(_lowercase ):
                try:
                       UpperCAmelCase__										=     self.config_class(**{name: idx} )
                       self.parent.assertEqual(
                           getattr(_lowercase		,						_lowercase )		,						_lowercase		,						msg=F"""`{name} value {idx} expected, but was {getattr(_lowercase		,						_lowercase )}""" )
                except NotImplementedError:
                       # Some models might not be able to implement setters for common_properties
                       # In that case, a NotImplementedError is raised
                       pass






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         UpperCAmelCase__										=     json.loads(config.to_json_string() )
         for key, value in self.inputs_dict.items():
                self.parent.assertEqual(obj[key]		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCAmelCase__										=     os.path.join(_lowercase		,						"config.json" )
                config_first.to_json_file(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_json_file(_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
                config_first.save_pretrained(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_pretrained(_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         UpperCAmelCase__										=     "test"
         with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCAmelCase__										=     os.path.join(_lowercase		,						_lowercase )
                config_first.save_pretrained(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_pretrained(_lowercase		,						subfolder=_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict		,						num_labels=5 )
         self.parent.assertEqual(len(config.idalabel )		,						5 )
         self.parent.assertEqual(len(config.labelaid )		,						5 )

         UpperCAmelCase__										=     3
         self.parent.assertEqual(len(config.idalabel )		,						3 )
         self.parent.assertEqual(len(config.labelaid )		,						3 )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         if self.config_class.is_composition:
                return
         UpperCAmelCase__										=     self.config_class()
         self.parent.assertIsNotNone(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     copy.deepcopy(_lowercase )
         UpperCAmelCase__										=     self.config_class(**_lowercase )
         UpperCAmelCase__										=     []
         for key, value in config_common_kwargs.items():
                if key == "torch_dtype":
                       if not is_torch_available():
                              continue
                       else:
                              import torch

                              if config.torch_dtype != torch.floataa:
                                     wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) )
                elif getattr(_lowercase		,						_lowercase ) != value:
                       wrong_values.append((key, getattr(_lowercase		,						_lowercase ), value) )

         if len(_lowercase ) > 0:
                UpperCAmelCase__										=     "\n".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
                raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()





