



"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   TaConfig.from_pretrained(_lowerCamelCase      )
					_lowerCAmelCase      =   FlaxTaForConditionalGeneration(config=_lowerCamelCase      )
					_lowerCAmelCase      =   checkpoints.load_tax_checkpoint(_lowerCamelCase      )

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

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

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

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

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

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

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

										# Assigning
										_lowerCAmelCase      =   tax_attention_key
										_lowerCAmelCase      =   tax_attention_out
										_lowerCAmelCase      =   tax_attention_query
										_lowerCAmelCase      =   tax_attention_value

										_lowerCAmelCase      =   tax_attention_layer_norm

										if split_mlp_wi:
															_lowerCAmelCase      =   tax_mlp_wi_a
															_lowerCAmelCase      =   tax_mlp_wi_a
										else:
															_lowerCAmelCase      =   tax_mlp_wi

										_lowerCAmelCase      =   tax_mlp_wo
										_lowerCAmelCase      =   tax_mlp_layer_norm

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

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

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

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

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

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

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

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

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

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

										# Assigning
										_lowerCAmelCase      =   tax_attention_key
										_lowerCAmelCase      =   tax_attention_out
										_lowerCAmelCase      =   tax_attention_query
										_lowerCAmelCase      =   tax_attention_value

										_lowerCAmelCase      =   tax_pre_attention_layer_norm

										_lowerCAmelCase      =   tax_enc_dec_attention_key
										_lowerCAmelCase      =   tax_enc_dec_attention_out
										_lowerCAmelCase      =   tax_enc_dec_attention_query
										_lowerCAmelCase      =   tax_enc_dec_attention_value

										_lowerCAmelCase      =   tax_cross_layer_norm

										if split_mlp_wi:
															_lowerCAmelCase      =   tax_mlp_wi_a
															_lowerCAmelCase      =   tax_mlp_wi_a
										else:
															_lowerCAmelCase      =   tax_mlp_wi

										_lowerCAmelCase      =   tax_mlp_wo

										_lowerCAmelCase      =   txa_mlp_layer_norm

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

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

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

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

					flax_model.save_pretrained(_lowerCamelCase      )
					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 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__     				=	parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image





class  __lowerCAmelCase			(					unittest.TestCase	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   load_tool("""image-question-answering"""		)
									self.tool.setup()
									_lowerCAmelCase      =   load_tool("""image-question-answering"""						,    remote=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)



