




from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import numpy
					import tensorflow as tf

					from transformers.models.esm.modeling_tf_esm import (
					    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFEsmForMaskedLM,
					    TFEsmForSequenceClassification,
					    TFEsmForTokenClassification,
					    TFEsmModel,
					)
class 			a__    :






						def __init__(   self		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       1_3
											__lowerCAmelCase				   =       7
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       9_9
											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       3_7
											__lowerCAmelCase				   =       "gelu"
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       5_1_2
											__lowerCAmelCase				   =       1_6
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       0.02
											__lowerCAmelCase				   =       3
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       None






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

											__lowerCAmelCase				   =       None
											if self.use_input_mask:
																__lowerCAmelCase				   =       random_attention_mask([self.batch_size, self.seq_length]			)

											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       None
											if self.use_labels:
																__lowerCAmelCase				   =       ids_tensor([self.batch_size]		,			self.type_sequence_label_size			)
																__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels			)
																__lowerCAmelCase				   =       ids_tensor([self.batch_size]		,			self.num_choices			)

											__lowerCAmelCase				   =       EsmConfig(
											    vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			pad_token_id=1		,			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		,			)

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       self.prepare_config_and_inputs()

											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2			)

											return (
											    config,
											    input_ids,
											    input_mask,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       model(_A			)

											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       True

											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "encoder_hidden_states": encoder_hidden_states,
											    "encoder_attention_mask": encoder_attention_mask,
											}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A		,			encoder_hidden_states=_A			)

											# Also check the case where encoder outputs are not passed
											__lowerCAmelCase				   =       model(_A		,			attention_mask=_A			)

											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM(config=_A			)
											__lowerCAmelCase				   =       model([input_ids, input_mask]			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFEsmForTokenClassification(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       config_and_inputs
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_tf
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    Any					=    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:    List[Any]					=    (
						    {
						        """feature-extraction""": TFEsmModel,
						        """fill-mask""": TFEsmForMaskedLM,
						        """text-classification""": TFEsmForSequenceClassification,
						        """token-classification""": TFEsmForTokenClassification,
						        """zero-shot""": TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:    Union[str, Any]					=    False
						_a							:    List[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       TFEsmModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       model_class(_A			)
																assert isinstance(model.get_input_embeddings()		,			tf.keras.layers.Layer			)
																if model_class is TFEsmForMaskedLM:
																					# Output embedding test differs from the main test because they're a matrix, not a layer
																					__lowerCAmelCase				   =       model.get_bias()
																					assert isinstance(_A		,			_A			)
																					for k, v in name.items():
																										assert isinstance(_A		,			tf.Variable			)
																else:
																					__lowerCAmelCase				   =       model.get_output_embeddings()
																					assert x is None
																					__lowerCAmelCase				   =       model.get_bias()
																					assert name is None






@require_tf
class 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 1, 2, 3, 4, 5]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											__lowerCAmelCase				   =       [1, 6, 3_3]
											self.assertEqual(list(output.numpy().shape			)		,			_A			)
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       tf.constant(
											    [
											        [
											            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
											            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
											            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
											        ]
											    ]			)
											self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,			expected_slice.numpy()		,			atol=1E-2			)			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       tf.constant(
											    [
											        [
											            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
											            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
											            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
											        ]
											    ]			)
											self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,			expected_slice.numpy()		,			atol=1E-4			)			)











"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).read()					)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""					)
	if not args.output.endswith(".pt"					):
		a      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).astype(np.floataa					)
			if key_name.endswith("/adam_m"					) or key_name.endswith("/adam_v"					):
				continue
			if key_name.startswith("pasts/"					):
				if key_name.startswith("pasts/mlp"					):
					a      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Dict	=				(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			argparse.ArgumentParser(
		    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
		parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


