

"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							int				):


					'''simple docstring'''






					UpperCamelCase__		:    Tuple         =TaConfig.from_pretrained(UpperCAmelCase				)
					UpperCamelCase__		:    Optional[Any]         =FlaxTaForConditionalGeneration(config=UpperCAmelCase				)
					UpperCamelCase__		:    str         =checkpoints.load_tax_checkpoint(UpperCAmelCase				)

					UpperCamelCase__		:    Optional[Any]         ='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

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

										# Self-Attention
										UpperCamelCase__		:    Tuple         =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
										UpperCamelCase__		:    Optional[int]         =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
										UpperCamelCase__		:    Optional[Any]         =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
										UpperCamelCase__		:    List[str]         =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

										# Layer Normalization
										UpperCamelCase__		:    List[Any]         =tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

										if split_mlp_wi:
															UpperCamelCase__		:    str         =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															UpperCamelCase__		:    Optional[Any]         =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															UpperCamelCase__		:    int         =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										UpperCamelCase__		:    str         =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										UpperCamelCase__		:    str         =tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

										UpperCamelCase__		:    Tuple         =tax_attention_layer_norm

										if split_mlp_wi:
															UpperCamelCase__		:    List[Any]         =tax_mlp_wi_a
															UpperCamelCase__		:    List[str]         =tax_mlp_wi_a
										else:
															UpperCamelCase__		:    List[str]         =tax_mlp_wi

										UpperCamelCase__		:    Union[str, Any]         =tax_mlp_wo
										UpperCamelCase__		:    Any         =tax_mlp_layer_norm

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

					# Assigning
					UpperCamelCase__		:    List[str]         =tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
					UpperCamelCase__		:    List[Any]         =tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_decoder_layers				):
										UpperCamelCase__		:    List[Any]         =F'''layers_{str(UpperCAmelCase				)}'''

										# Self-Attention
										UpperCamelCase__		:    str         =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
										UpperCamelCase__		:    List[str]         =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
										UpperCamelCase__		:    Optional[Any]         =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
										UpperCamelCase__		:    Tuple         =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

										# Layer Normalization
										UpperCamelCase__		:    int         =tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
										    '''scale'''
										]

										# Encoder-Decoder-Attention
										UpperCamelCase__		:    int         =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
										    '''kernel'''
										]
										UpperCamelCase__		:    Optional[Any]         =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
										    '''kernel'''
										]
										UpperCamelCase__		:    List[str]         =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
										    '''kernel'''
										]
										UpperCamelCase__		:    Any         =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
										    '''kernel'''
										]

										# Layer Normalization
										UpperCamelCase__		:    Any         =tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

										# MLP
										if split_mlp_wi:
															UpperCamelCase__		:    List[str]         =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
															UpperCamelCase__		:    Optional[int]         =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
										else:
															UpperCamelCase__		:    Optional[int]         =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

										UpperCamelCase__		:    Dict         =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

										# Layer Normalization
										UpperCamelCase__		:    List[Any]         =tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

										# Assigning
										UpperCamelCase__		:    List[Any]         =tax_attention_key
										UpperCamelCase__		:    str         =tax_attention_out
										UpperCamelCase__		:    Optional[Any]         =tax_attention_query
										UpperCamelCase__		:    Optional[int]         =tax_attention_value

										UpperCamelCase__		:    List[str]         =tax_pre_attention_layer_norm

										UpperCamelCase__		:    Tuple         =tax_enc_dec_attention_key
										UpperCamelCase__		:    Dict         =tax_enc_dec_attention_out
										UpperCamelCase__		:    List[Any]         =tax_enc_dec_attention_query
										UpperCamelCase__		:    Optional[Any]         =tax_enc_dec_attention_value

										UpperCamelCase__		:    str         =tax_cross_layer_norm

										if split_mlp_wi:
															UpperCamelCase__		:    int         =tax_mlp_wi_a
															UpperCamelCase__		:    List[str]         =tax_mlp_wi_a
										else:
															UpperCamelCase__		:    Dict         =tax_mlp_wi

										UpperCamelCase__		:    List[str]         =tax_mlp_wo

										UpperCamelCase__		:    Tuple         =txa_mlp_layer_norm

					# Decoder Normalization
					UpperCamelCase__		:    Dict         =tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
					UpperCamelCase__		:    List[str]         =txa_decoder_norm

					# Only for layer 0:
					UpperCamelCase__		:    Optional[Any]         =tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
					UpperCamelCase__		:    List[str]         =tax_decoder_rel_embedding

					# Token Embeddings
					UpperCamelCase__		:    int         =tax_model['''target''']['''token_embedder''']['''embedding''']
					UpperCamelCase__		:    int         =txa_token_embeddings

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

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


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  Dict        = 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."""
				)
				_SCREAMING_SNAKE_CASE       :  Optional[int]        = parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""





import unittest

from transformers.image_processing_utils import get_size_dict

class     __a   (      unittest.TestCase    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Dict						):
								# Test a dict with the wrong keys raises an error
								UpperCamelCase__		:    str         ={'''wrong_key''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								UpperCamelCase__		:    Any         ={'''height''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								UpperCamelCase__		:    List[str]         ={'''width''': 224, '''shortest_edge''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								# Test a dict with the correct keys is returned as is
								UpperCamelCase__		:    int         ={'''height''': 224, '''width''': 224}
								UpperCamelCase__		:    str         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  lowercase_						)

								UpperCamelCase__		:    List[Any]         ={'''shortest_edge''': 224}
								UpperCamelCase__		:    List[str]         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224}						)

								UpperCamelCase__		:    List[Any]         ={'''longest_edge''': 224, '''shortest_edge''': 224}
								UpperCamelCase__		:    Tuple         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  {'''longest_edge''': 224, '''shortest_edge''': 224}						)

								# Test a single int value which  represents (size, size)
								UpperCamelCase__		:    Optional[int]         =get_size_dict(224						)
								self.assertEqual(lowercase_						,  {'''height''': 224, '''width''': 224}						)

								# Test a single int value which represents the shortest edge
								UpperCamelCase__		:    str         =get_size_dict(224						,  default_to_square=lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224}						)

								# Test a tuple of ints which represents (height, width)
								UpperCamelCase__		:    List[str]         =get_size_dict((150, 200)						)
								self.assertEqual(lowercase_						,  {'''height''': 150, '''width''': 200}						)

								# Test a tuple of ints which represents (width, height)
								UpperCamelCase__		:    Optional[int]         =get_size_dict((150, 200)						,  height_width_order=lowercase_						)
								self.assertEqual(lowercase_						,  {'''height''': 200, '''width''': 150}						)

								# Test an int representing the shortest edge and max_size which represents the longest edge
								UpperCamelCase__		:    int         =get_size_dict(224						,  max_size=256						,  default_to_square=lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224, '''longest_edge''': 256}						)

								# Test int with default_to_square=True and max_size fails
								with self.assertRaises(lowercase_						):
													get_size_dict(224						,  max_size=256						,  default_to_square=lowercase_						)


