



"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: int     =   TaConfig.from_pretrained(__UpperCAmelCase      )
		_lowercase				: Optional[int]     =   FlaxTaForConditionalGeneration(config=__UpperCAmelCase      )
		_lowercase				: List[Any]     =   checkpoints.load_tax_checkpoint(__UpperCAmelCase      )

		_lowercase				: Union[str, Any]     =   """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

		# Encoder
		for layer_index in range(config.num_layers      ):
				_lowercase				: Dict     =   F"""layers_{str(__UpperCAmelCase      )}"""

				# Self-Attention
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
				_lowercase				: Any     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: List[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

				if split_mlp_wi:
						_lowercase				: List[str]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						_lowercase				: List[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						_lowercase				: Dict     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Dict     =   tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				_lowercase				: int     =   tax_attention_key
				_lowercase				: List[Any]     =   tax_attention_out
				_lowercase				: Dict     =   tax_attention_query
				_lowercase				: Any     =   tax_attention_value

				_lowercase				: List[Any]     =   tax_attention_layer_norm

				if split_mlp_wi:
						_lowercase				: List[Any]     =   tax_mlp_wi_a
						_lowercase				: Optional[Any]     =   tax_mlp_wi_a
				else:
						_lowercase				: int     =   tax_mlp_wi

				_lowercase				: Optional[Any]     =   tax_mlp_wo
				_lowercase				: Optional[Any]     =   tax_mlp_layer_norm

		# Only for layer 0:
		_lowercase				: int     =   tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
		_lowercase				: Union[str, Any]     =   tax_encoder_rel_embedding

		# Assigning
		_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
		_lowercase				: List[Any]     =   tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_decoder_layers      ):
				_lowercase				: List[str]     =   F"""layers_{str(__UpperCAmelCase      )}"""

				# Self-Attention
				_lowercase				: Any     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
				_lowercase				: int     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
				    """scale"""
				]

				# Encoder-Decoder-Attention
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
				    """kernel"""
				]
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
				    """kernel"""
				]
				_lowercase				: Any     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
				    """kernel"""
				]
				_lowercase				: List[str]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
				    """kernel"""
				]

				# Layer Normalization
				_lowercase				: int     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

				# MLP
				if split_mlp_wi:
						_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						_lowercase				: Optional[int]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						_lowercase				: Dict     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				_lowercase				: List[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				_lowercase				: List[Any]     =   tax_attention_key
				_lowercase				: Tuple     =   tax_attention_out
				_lowercase				: List[Any]     =   tax_attention_query
				_lowercase				: Any     =   tax_attention_value

				_lowercase				: Optional[int]     =   tax_pre_attention_layer_norm

				_lowercase				: List[str]     =   tax_enc_dec_attention_key
				_lowercase				: Tuple     =   tax_enc_dec_attention_out
				_lowercase				: List[Any]     =   tax_enc_dec_attention_query
				_lowercase				: Union[str, Any]     =   tax_enc_dec_attention_value

				_lowercase				: int     =   tax_cross_layer_norm

				if split_mlp_wi:
						_lowercase				: str     =   tax_mlp_wi_a
						_lowercase				: Optional[int]     =   tax_mlp_wi_a
				else:
						_lowercase				: Optional[Any]     =   tax_mlp_wi

				_lowercase				: int     =   tax_mlp_wo

				_lowercase				: int     =   txa_mlp_layer_norm

		# Decoder Normalization
		_lowercase				: Tuple     =   tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
		_lowercase				: Union[str, Any]     =   txa_decoder_norm

		# Only for layer 0:
		_lowercase				: List[str]     =   tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
		_lowercase				: Union[str, Any]     =   tax_decoder_rel_embedding

		# Token Embeddings
		_lowercase				: Tuple     =   tax_model["""target"""]["""token_embedder"""]["""embedding"""]
		_lowercase				: List[str]     =   txa_token_embeddings

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

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


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






import unittest

from cer import CER


lowerCAmelCase__             =		CER()







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		Any								=    ['White House']
			_UpperCamelCase :		Tuple								=    ['white house']
			# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
			_UpperCamelCase :		Tuple								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['were wolf']
			_UpperCamelCase :		List[Any]								=    ['werewolf']
			# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1) < 1e-6)

			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weae     wolf']
			# S = 1, D = 1, I = 0, N = 8, CER = 0.25
			_UpperCamelCase :		str								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.2_5) < 1e-6)

			# consecutive whitespaces case 1
			_UpperCamelCase :		Tuple								=    ['were wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)

			# consecutive whitespaces case 2
			_UpperCamelCase :		Dict								=    ['were   wolf']
			_UpperCamelCase :		Dict								=    ['were               wolf']
			# S = 0, D = 0, I = 0, N = 9, CER = 0
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['weaewolf']
			# S = 1, D = 0, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		List[str]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		int								=    ['werewolf']
			_UpperCamelCase :		Any								=    ['wereawolf']
			# S = 0, D = 1, I = 0, N = 8, CER = 0.125
			_UpperCamelCase :		Optional[int]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		str								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    ['wereolf']
			# S = 0, D = 0, I = 1, N = 8, CER = 0.125
			_UpperCamelCase :		List[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.1_2_5) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Optional[Any]								=    ['werewolf']
			_UpperCamelCase :		Optional[Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['werewolf', 'I am your father']
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertEqual(__snake_case						,							0.0)

			_UpperCamelCase :		List[Any]								=    ['werewolf', 'I am your father', 'doge']
			_UpperCamelCase :		Optional[int]								=    ['werxwolf', 'I       am your father', 'doge']
			# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['My hovercraft', 'is full of eels']
			_UpperCamelCase :		List[Any]								=    ['My hovercraft is full', ' of eels']
			# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
			# whitespace at the front of " of eels" will be strip during preporcessing
			# so need to insert 2 whitespaces
			_UpperCamelCase :		Dict								=    cer.compute(predictions=__snake_case						,							references=__snake_case						,							concatenate_texts=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8) < 1e-6)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		Optional[Any]								=    [' 能吞虾玻璃而 不霜身体啦']
			# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Any								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Union[str, Any]								=    ['我能吞下玻璃', '而不伤身体']
			_UpperCamelCase :		List[str]								=    ['我    能 吞 下 玻 璃', '而不伤身体']
			# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
			_UpperCamelCase :		Union[str, Any]								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5) < 1e-6)

			_UpperCamelCase :		Optional[int]								=    ['我能吞下玻璃而不伤身体']
			_UpperCamelCase :		int								=    cer.compute(predictions=__snake_case						,							references=__snake_case)
			self.assertFalse(__snake_case						,							0.0)






		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    ['']
			_UpperCamelCase :		Dict								=    ['Hypothesis']
			with self.assertRaises(__snake_case):
				cer.compute(predictions=__snake_case						,							references=__snake_case)


if __name__ == "__main__":
							unittest.main()




