





'''simple docstring'''


def    __a (     UpperCAmelCase    )      ->bool:



			"""simple docstring"""







			A							  =      [list(UpperCAmelCase    ) for row in matrix]
			if matrix and isinstance(UpperCAmelCase       ,			UpperCAmelCase    ):
						if isinstance(matrix[0]       ,			UpperCAmelCase    ):
									A							  =      0
									for row in matrix:
												if prev_len == 0:
															A							  =      len(UpperCAmelCase    )
															A							  =      True
												else:
															A							  =      prev_len == len(UpperCAmelCase    )
						else:
									A							  =      True
			else:
						A							  =      False

			return result




def    __a (     UpperCAmelCase    )      ->None:



			"""simple docstring"""







			if check_matrix(UpperCAmelCase    ) and len(UpperCAmelCase    ) > 0:
						A							  =      [list(UpperCAmelCase    ) for row in a]
						A							  =      len(UpperCAmelCase    )
						if isinstance(a[0]       ,			UpperCAmelCase    ):
									A							  =      len(a[0]    )
						else:
									for dat in a:
												print(UpperCAmelCase    )
									return

						# horizotal printing increasing
						for i in range(0       ,			UpperCAmelCase    ):
									print(a[0][i]    )
						# vertical printing down
						for i in range(1       ,			UpperCAmelCase    ):
									print(a[i][mat_col - 1]    )
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2       ,			-1       ,			-1    ):
												print(a[mat_row - 1][i]    )
        # vertical printing up
						for i in range(mat_row - 2       ,			0       ,			-1    ):
									print(a[i][0]    )
						A							  =      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(UpperCAmelCase    ) > 0:
									spiral_print_clockwise(UpperCAmelCase    )
						else:
									return
			else:
						print("""Not a valid matrix"""    )
						return




def    __a (     UpperCAmelCase    )      ->list[int]:



			"""simple docstring"""







			if matrix:
						return list(matrix.pop(0    )    ) + spiral_traversal(list(zip(*UpperCAmelCase    )    )[::-1]    )
			else:
						return []


# driver code
if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCamelCase				:	Union[str, Any]				    =       [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
				spiral_print_clockwise(a)








'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:



	"""simple docstring"""







	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )

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

	# 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"""]

		# 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							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_attention_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

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

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

	# Decoder
	for layer_index in range(config.num_decoder_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"""]["""key"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""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							  =      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

	# 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 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				:	Tuple				    =       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.'
				)
				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

