"""simple docstring"""
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 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Dict   )   ->			List[Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Tuple="attention"   )   ->			Dict:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Dict=False   )   ->			List[str]:





						"""simple docstring"""






						if split_mlp_wi:
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
												snake_case        =     (wi_a, wi_a)
						else:
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			str   )   ->			Dict:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			dict	,     *, _UpperCamelCase    :			int	,     _UpperCamelCase    :			bool	,     _UpperCamelCase    :			bool = False   )   ->			str:





						"""simple docstring"""






						snake_case        =     traverse_util.flatten_dict(variables['target']   )
						snake_case        =     {'/'.join(_UpperCamelCase   ): v for k, v in old.items()}

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

						snake_case        =     collections.OrderedDict()

						# Shared embeddings.
						snake_case        =     old['token_embedder/embedding']

						# Encoder.
						for i in range(_UpperCamelCase   ):
												# Block i, layer 0 (Self Attention).
												snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     'pre_attention_layer_norm'   )
												snake_case   ,snake_case   ,snake_case   ,snake_case        =     tax_attention_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     'attention'   )
												snake_case        =     layer_norm
												snake_case        =     k.T
												snake_case        =     o.T
												snake_case        =     q.T
												snake_case        =     v.T

												# Block i, layer 1 (MLP).
												snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     'pre_mlp_layer_norm'   )
												snake_case   ,snake_case        =     tax_mlp_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     _UpperCamelCase   )
												snake_case        =     layer_norm
												if split_mlp_wi:
																		snake_case        =     wi[0].T
																		snake_case        =     wi[1].T
												else:
																		snake_case        =     wi.T
												snake_case        =     wo.T
												if scalable_attention:
																		# convert the rel_embedding of each layer
																		snake_case        =     tax_relpos_bias_lookup(
																		    _UpperCamelCase	,     _UpperCamelCase	,     'encoder'   ).T

						snake_case        =     old['encoder/encoder_norm/scale']

						if not scalable_attention:
												snake_case        =     tax_relpos_bias_lookup(
												    _UpperCamelCase	,     0	,     'encoder'   ).T
												snake_case        =     tax_relpos_bias_lookup(
												    _UpperCamelCase	,     0	,     'decoder'   ).T

						if not is_encoder_only:
												# Decoder.
												for i in range(_UpperCamelCase   ):
																		# Block i, layer 0 (Self Attention).
																		snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     'pre_self_attention_layer_norm'   )
																		snake_case   ,snake_case   ,snake_case   ,snake_case        =     tax_attention_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     'self_attention'   )
																		snake_case        =     layer_norm
																		snake_case        =     k.T
																		snake_case        =     o.T
																		snake_case        =     q.T
																		snake_case        =     v.T

																		# Block i, layer 1 (Cross Attention).
																		snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     'pre_cross_attention_layer_norm'   )
																		snake_case   ,snake_case   ,snake_case   ,snake_case        =     tax_attention_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     'encoder_decoder_attention'   )
																		snake_case        =     layer_norm
																		snake_case        =     k.T
																		snake_case        =     o.T
																		snake_case        =     q.T
																		snake_case        =     v.T

																		# Block i, layer 2 (MLP).
																		snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     'pre_mlp_layer_norm'   )
																		snake_case   ,snake_case        =     tax_mlp_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'	,     _UpperCamelCase   )
																		snake_case        =     layer_norm
																		if split_mlp_wi:
																								snake_case        =     wi[0].T
																								snake_case        =     wi[1].T
																		else:
																								snake_case        =     wi.T
																		snake_case        =     wo.T

																		if scalable_attention:
																								# convert the rel_embedding of each layer
																								snake_case        =     tax_relpos_bias_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'   ).T

												snake_case        =     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:
																		snake_case        =     old['decoder/logits_dense/kernel'].T

						return new





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			bool   )   ->			str:





						"""simple docstring"""






						snake_case        =     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:
												snake_case        =     state_dict['shared.weight']

						if not is_encoder_only:
												if "decoder.embed_tokens.weight" not in state_dict:
																		snake_case        =     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.'   )
																		snake_case        =     state_dict['shared.weight']

						return state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Any   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case        =     checkpoints.load_tax_checkpoint(_UpperCamelCase   )
						snake_case        =     convert_tax_to_pytorch(
						    _UpperCamelCase	,     num_layers=config.num_layers	,     is_encoder_only=_UpperCamelCase	,     scalable_attention=_UpperCamelCase   )
						snake_case        =     make_state_dict(_UpperCamelCase	,     _UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase	,     strict=_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False	,     )   ->			Optional[int]:





						"""simple docstring"""






						snake_case        =     MTaConfig.from_json_file(_UpperCamelCase   )
						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:
												snake_case        =     UMTaEncoderModel(_UpperCamelCase   )
						else:
												snake_case        =     UMTaForConditionalGeneration(_UpperCamelCase   )

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

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

						# Verify that we can load the checkpoint.
						model.from_pretrained(_UpperCamelCase   )
						print('Done'   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					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,
					)
					SCREAMING_SNAKE_CASE__   =					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,
					)



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


