






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  int	,			__magic_name__					:  int					)		->			Tuple:






	"""simple docstring"""



	return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Any="attention"					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :str          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]					)
	UpperCamelCase     :Optional[Any]          =	k_tmp.reshape(k_tmp.shape[0]	,			k_tmp.shape[1] * k_tmp.shape[2]					)
	UpperCamelCase     :Dict          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]					)
	UpperCamelCase     :List[str]          =	o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,			o_tmp.shape[2]					)
	UpperCamelCase     :List[str]          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]					)
	UpperCamelCase     :int          =	q_tmp.reshape(q_tmp.shape[0]	,			q_tmp.shape[1] * q_tmp.shape[2]					)
	UpperCamelCase     :Optional[Any]          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]					)
	UpperCamelCase     :int          =	v_tmp.reshape(v_tmp.shape[0]	,			v_tmp.shape[1] * v_tmp.shape[2]					)
	return k, o, q, v

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Any=False					)		->			Tuple:






	"""simple docstring"""



	if split_mlp_wi:
		UpperCamelCase     :Optional[Any]          =	params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		UpperCamelCase     :List[str]          =	params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		UpperCamelCase     :Dict          =	(wi_a, wi_a)
	else:
		UpperCamelCase     :Union[str, Any]          =	params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	UpperCamelCase     :Tuple          =	params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  List[Any]	,			__magic_name__					:  List[str]					)		->			List[str]:






	"""simple docstring"""



	return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  dict	,			*, __magic_name__					:  int	,			__magic_name__					:  bool	,			__magic_name__					:  bool = False					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	traverse_util.flatten_dict(variables["""target"""]					)
	UpperCamelCase     :int          =	{"""/""".join(__magic_name__					): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	UpperCamelCase     :str          =	"""encoder/encoder/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""	,			__magic_name__					)

	UpperCamelCase     :Optional[int]          =	collections.OrderedDict()

	# Shared embeddings.
	UpperCamelCase     :Dict          =	old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(__magic_name__					):
		# Block i, layer 0 (Self Attention).
		UpperCamelCase     :Optional[int]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_attention_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""attention"""					)
		UpperCamelCase     :Union[str, Any]          =	layer_norm
		UpperCamelCase     :Tuple          =	k.T
		UpperCamelCase     :Optional[Any]          =	o.T
		UpperCamelCase     :Dict          =	q.T
		UpperCamelCase     :List[str]          =	v.T

		# Block i, layer 1 (MLP).
		UpperCamelCase     :Optional[Any]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_mlp_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			__magic_name__					)
		UpperCamelCase     :List[Any]          =	layer_norm
		if split_mlp_wi:
			UpperCamelCase     :Any          =	wi[0].T
			UpperCamelCase     :Optional[Any]          =	wi[1].T
		else:
			UpperCamelCase     :Optional[int]          =	wi.T
		UpperCamelCase     :Any          =	wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			UpperCamelCase     :Optional[Any]          =	tax_relpos_bias_lookup(
			    __magic_name__	,			__magic_name__	,			"""encoder"""					).T

	UpperCamelCase     :Tuple          =	old["""encoder/encoder_norm/scale"""]

	if not scalable_attention:
		UpperCamelCase     :Dict          =	tax_relpos_bias_lookup(
		    __magic_name__	,			0	,			"""encoder"""					).T
		UpperCamelCase     :Tuple          =	tax_relpos_bias_lookup(
		    __magic_name__	,			0	,			"""decoder"""					).T

	if not is_encoder_only:
		# Decoder.
		for i in range(__magic_name__					):
			# Block i, layer 0 (Self Attention).
			UpperCamelCase     :Dict          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_self_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""self_attention"""					)
			UpperCamelCase     :int          =	layer_norm
			UpperCamelCase     :Optional[Any]          =	k.T
			UpperCamelCase     :Tuple          =	o.T
			UpperCamelCase     :Optional[Any]          =	q.T
			UpperCamelCase     :int          =	v.T

			# Block i, layer 1 (Cross Attention).
			UpperCamelCase     :str          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_cross_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Tuple          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""encoder_decoder_attention"""					)
			UpperCamelCase     :str          =	layer_norm
			UpperCamelCase     :List[str]          =	k.T
			UpperCamelCase     :Optional[int]          =	o.T
			UpperCamelCase     :Union[str, Any]          =	q.T
			UpperCamelCase     :Tuple          =	v.T

			# Block i, layer 2 (MLP).
			UpperCamelCase     :Optional[Any]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_mlp_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			__magic_name__					)
			UpperCamelCase     :str          =	layer_norm
			if split_mlp_wi:
				UpperCamelCase     :Any          =	wi[0].T
				UpperCamelCase     :int          =	wi[1].T
			else:
				UpperCamelCase     :List[str]          =	wi.T
			UpperCamelCase     :Union[str, Any]          =	wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				UpperCamelCase     :Union[str, Any]          =	tax_relpos_bias_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""					).T

		UpperCamelCase     :int          =	old["""decoder/decoder_norm/scale"""]

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			UpperCamelCase     :Optional[Any]          =	old["""decoder/logits_dense/kernel"""].T

	return new

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  bool					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		UpperCamelCase     :int          =	state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			UpperCamelCase     :List[str]          =	state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""					)
			UpperCamelCase     :Optional[Any]          =	state_dict["""shared.weight"""]

	return state_dict

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :Any          =	checkpoints.load_tax_checkpoint(__magic_name__					)
	UpperCamelCase     :int          =	convert_tax_to_pytorch(
	    __magic_name__	,			num_layers=config.num_layers	,			is_encoder_only=__magic_name__	,			scalable_attention=__magic_name__					)
	UpperCamelCase     :Any          =	make_state_dict(__magic_name__	,			__magic_name__					)
	model.load_state_dict(__magic_name__	,			strict=__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  Dict	,			__magic_name__					:  str	,			__magic_name__					:  bool = False	,			__magic_name__					:  bool = False	,			)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	MTaConfig.from_json_file(__magic_name__					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		UpperCamelCase     :List[str]          =	UMTaEncoderModel(__magic_name__					)
	else:
		UpperCamelCase     :List[Any]          =	UMTaForConditionalGeneration(__magic_name__					)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	model.save_pretrained(__magic_name__					)

	# Verify that we can load the checkpoint.
	model.from_pretrained(__magic_name__					)
	print("""Done"""					)


if __name__ == "__main__":
	UpperCAmelCase_ :						str      =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
	# Required parameters
	parser.add_argument(
	    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
	)
	parser.add_argument(
	    '''--scalable_attention''',
	    action='''store_true''',
	    help='''Whether the model uses scaled attention (umt5 model)''',
	    default=False,
	)
	UpperCAmelCase_ :						Tuple      =      parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)



import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.esm.modeling_esmfold import EsmForProteinFolding


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=13						,   a=7						,   a=False						,   a=True						,   a=False						,   a=False						,   a=19						,   a=32						,   a=5						,   a=4						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=16						,   a=2						,   a=0.02						,   a=3						,   a=4						,   a=None						,   ):
         lowercase__    :   Optional[Any]      =       parent
         lowercase__    :   Dict      =       batch_size
         lowercase__    :   Union[str, Any]      =       seq_length
         lowercase__    :   Optional[Any]      =       is_training
         lowercase__    :   Tuple      =       use_input_mask
         lowercase__    :   List[str]      =       use_token_type_ids
         lowercase__    :   Optional[Any]      =       use_labels
         lowercase__    :   List[str]      =       vocab_size
         lowercase__    :   Optional[int]      =       hidden_size
         lowercase__    :   List[str]      =       num_hidden_layers
         lowercase__    :   Any      =       num_attention_heads
         lowercase__    :   int      =       intermediate_size
         lowercase__    :   Any      =       hidden_act
         lowercase__    :   Any      =       hidden_dropout_prob
         lowercase__    :   str      =       attention_probs_dropout_prob
         lowercase__    :   List[Any]      =       max_position_embeddings
         lowercase__    :   int      =       type_vocab_size
         lowercase__    :   List[Any]      =       type_sequence_label_size
         lowercase__    :   str      =       initializer_range
         lowercase__    :   List[str]      =       num_labels
         lowercase__    :   Union[str, Any]      =       num_choices
         lowercase__    :   Optional[int]      =       scope





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)

         lowercase__    :   List[Any]      =       None
         if self.use_input_mask:
              lowercase__    :   Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length])

         lowercase__    :   int      =       None
         lowercase__    :   Optional[int]      =       None
         lowercase__    :   Optional[int]      =       None
         if self.use_labels:
              lowercase__    :   Optional[int]      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
              lowercase__    :   str      =       ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels)
              lowercase__    :   str      =       ids_tensor([self.batch_size]						,   self.num_choices)

         lowercase__    :   int      =       self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def       snake_case_				(      self):
         lowercase__    :   str      =       EsmConfig(
             vocab_size=33						,   hidden_size=self.hidden_size						,   pad_token_id=1						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   initializer_range=self.initializer_range						,   is_folding_model=a						,   esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}						,   )
         return config





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Dict      =       EsmForProteinFolding(config=a).float()
         model.to(a)
         model.eval()
         lowercase__    :   Union[str, Any]      =       model(a						,   attention_mask=a)
         lowercase__    :   Dict      =       model(a)
         lowercase__    :   int      =       model(a)

         self.parent.assertEqual(result.positions.shape						,   (8, self.batch_size, self.seq_length, 14, 3))
         self.parent.assertEqual(result.angles.shape						,   (8, self.batch_size, self.seq_length, 7, 2))





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   int      =       config_and_inputs
         lowercase__    :   Optional[int]      =       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     False

    __lowerCamelCase						:       Dict														=     (EsmForProteinFolding,) if is_torch_available() else ()
    __lowerCamelCase						:       Union[str, Any]														=     ()
    __lowerCamelCase						:       List[Any]														=     {} if is_torch_available() else {}
    __lowerCamelCase						:       Optional[Any]														=     False





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       EsmFoldModelTester(self)
         lowercase__    :   List[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    @unittest.skip('Does not support attention outputs')
    def       snake_case_				(      self):
         pass





    @unittest.skip
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('Esm does not support embedding resizing')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support passing input embeds!')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMfold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold only has one output format.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support input chunking.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support data parallel.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
    def       snake_case_				(      self):
         pass



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @slow
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1').float()
         model.eval()
         lowercase__    :   Optional[Any]      =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         lowercase__    :   Optional[int]      =       model(a)['positions']
         lowercase__    :   Dict      =       torch.tensor([2.5_828, 0.7_993, -10.9_334]						,   dtype=torch.floataa)
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]						,   a						,   atol=1e-4))

