


'''simple docstring'''





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    LevitForImageClassification,
					    LevitForImageClassificationWithTeacher,
					    LevitModel,
					)
					from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor
class 						a__					(					__lowercase   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Any	)      ->		Tuple:
						__A=	self.config_class(**self.inputs_dict	)
						self.parent.assertTrue(hasattr(_a			,    'hidden_sizes'	)	)
						self.parent.assertTrue(hasattr(_a			,    'num_attention_heads'	)	)


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			Any=13			,    lowerCAmelCase_							:			str=64			,    lowerCAmelCase_							:			Any=3			,    lowerCAmelCase_							:			Optional[Any]=3			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			Dict=1			,    lowerCAmelCase_							:			Optional[int]=16			,    lowerCAmelCase_							:			Tuple=[128, 256, 384]			,    lowerCAmelCase_							:			Optional[Any]=[4, 6, 8]			,    lowerCAmelCase_							:			Optional[Any]=[2, 3, 4]			,    lowerCAmelCase_							:			Optional[Any]=[16, 16, 16]			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=[2, 2, 2]			,    lowerCAmelCase_							:			Union[str, Any]=[2, 2, 2]			,    lowerCAmelCase_							:			str=0.02			,    lowerCAmelCase_							:			Optional[Any]=True			,    lowerCAmelCase_							:			str=True			,    lowerCAmelCase_							:			Dict=2			,    )      ->		Tuple:
						__A=	parent
						__A=	batch_size
						__A=	image_size
						__A=	num_channels
						__A=	kernel_size
						__A=	stride
						__A=	padding
						__A=	hidden_sizes
						__A=	num_attention_heads
						__A=	depths
						__A=	key_dim
						__A=	drop_path_rate
						__A=	patch_size
						__A=	attention_ratio
						__A=	mlp_ratio
						__A=	initializer_range
						__A=	[
						    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
						    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
						]
						__A=	is_training
						__A=	use_labels
						__A=	num_labels
						__A=	initializer_range
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		List[str]:
						__A=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						__A=	None
						if self.use_labels:
									__A=	ids_tensor([self.batch_size]			,    self.num_labels	)

						__A=	self.get_config()
						return config, pixel_values, labels
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						return LevitConfig(
						    image_size=self.image_size			,    num_channels=self.num_channels			,    kernel_size=self.kernel_size			,    stride=self.stride			,    padding=self.padding			,    patch_size=self.patch_size			,    hidden_sizes=self.hidden_sizes			,    num_attention_heads=self.num_attention_heads			,    depths=self.depths			,    key_dim=self.key_dim			,    drop_path_rate=self.drop_path_rate			,    mlp_ratio=self.mlp_ratio			,    attention_ratio=self.attention_ratio			,    initializer_range=self.initializer_range			,    down_ops=self.down_ops			,    )
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[Any]:
						__A=	LevitModel(config=_a	)
						model.to(_a	)
						model.eval()
						__A=	model(_a	)
						__A=	(self.image_size, self.image_size)
						__A=	image_size[0], image_size[1]
						for _ in range(4	):
									__A=	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
									__A=	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,    (self.batch_size, ceil(height / 4	) * ceil(width / 4	), self.hidden_sizes[-1])			,    )
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			str	)      ->		str:
						__A=	self.num_labels
						__A=	LevitForImageClassification(_a	)
						model.to(_a	)
						model.eval()
						__A=	model(_a			,    labels=_a	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)	)

			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Dict:
						__A=	self.prepare_config_and_inputs()
						__A=	config_and_inputs
						__A=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_torch
class 						a__					(					__lowercase      ,  __lowercase      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Tuple					      =			(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			A						:				int					      =			(
			    {
			        "feature-extraction": LevitModel,
			        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			A						:				Any					      =			False
			A						:				Union[str, Any]					      =			False
			A						:				Tuple					      =			False
			A						:				List[str]					      =			False
			A						:				List[Any]					      =			False
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[int]:
						__A=	LevitModelTester(self	)
						__A=	ConfigTester(self			,    config_class=_a			,    has_text_modality=_a			,    hidden_size=37	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		str:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()
			def 					lowerCAmelCase     (  self							:			str	)      ->		int:
						return
			@unittest.skip(reason='Levit does not use inputs_embeds'	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[Any]:
						pass
			@unittest.skip(reason='Levit does not support input and output embeddings'	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						pass
			@unittest.skip(reason='Levit does not output attentions'	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		List[str]:
						pass
			def 					lowerCAmelCase     (  self							:			str	)      ->		Tuple:
						__A=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__A=	model_class(_a	)
									__A=	inspect.signature(model.forward	)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__A=	[*signature.parameters.keys()]

									__A=	['''pixel_values''']
									self.assertListEqual(arg_names[:1]			,    _a	)
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						def check_hidden_states_output(lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			int	):
									__A=	model_class(_a	)
									model.to(_a	)
									model.eval()

									with torch.no_grad():
												__A=	model(**self._prepare_for_class(_a			,    _a	)	)

									__A=	outputs.hidden_states

									__A=	len(self.model_tester.depths	) + 1
									self.assertEqual(len(_a	)			,    _a	)

									__A=	(self.model_tester.image_size, self.model_tester.image_size)
									__A=	image_size[0], image_size[1]
									for _ in range(4	):
												__A=	floor(
												    (
												        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
												        / self.model_tester.stride
												    )
												    + 1	)
												__A=	floor(
												    (
												        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
												        / self.model_tester.stride
												    )
												    + 1	)
									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]	)			,    [
									        height * width,
									        self.model_tester.hidden_sizes[0],
									    ]			,    )

						__A=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__A=	True
									check_hidden_states_output(_a			,    _a			,    _a	)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__A=	True

									check_hidden_states_output(_a			,    _a			,    _a	)
			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Tuple:
						pass
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			str=False	)      ->		str:
						__A=	super()._prepare_for_class(_a			,    _a			,    return_labels=_a	)

						if return_labels:
									if model_class.__name__ == "LevitForImageClassificationWithTeacher":
												del inputs_dict["labels"]

						return inputs_dict
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_a	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		str:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_a	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Tuple:
						if not self.model_tester.is_training:
									return

						__A=	self.model_tester.prepare_config_and_inputs_for_common()
						__A=	True

						for model_class in self.all_model_classes:
									# LevitForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(_a	)
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
												continue
									__A=	model_class(_a	)
									model.to(_a	)
									model.train()
									__A=	self._prepare_for_class(_a			,    _a			,    return_labels=_a	)
									__A=	model(**_a	).loss
									loss.backward()
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
									return

						__A=	False
						__A=	True

						for model_class in self.all_model_classes:
									if model_class in get_values(_a	) or not model_class.supports_gradient_checkpointing:
												continue
									# LevitForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "LevitForImageClassificationWithTeacher":
												continue
									__A=	model_class(_a	)
									model.gradient_checkpointing_enable()
									model.to(_a	)
									model.train()
									__A=	self._prepare_for_class(_a			,    _a			,    return_labels=_a	)
									__A=	model(**_a	).loss
									loss.backward()
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs_for_common()

						__A=	[
						    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
						    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
						    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
						]

						for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(_a	),
									    ]
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
												continue

									for problem_type in problem_types:
												with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""	):
															__A=	problem_type['''title''']
															__A=	problem_type['''num_labels''']

															__A=	model_class(_a	)
															model.to(_a	)
															model.train()

															__A=	self._prepare_for_class(_a			,    _a			,    return_labels=_a	)

															if problem_type["num_labels"] > 1:
																		__A=	inputs['''labels'''].unsqueeze(1	).repeat(1			,    problem_type['num_labels']	)

															__A=	inputs['''labels'''].to(problem_type['dtype']	)

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=_a	) as warning_list:
																		__A=	model(**_a	).loss
															for w in warning_list:
																		if "Using a target size that is different to the input size" in str(w.message	):
																					raise ValueError(
																					    F"""Something is going wrong in the regression problem: intercepted {w.message}"""	)

															loss.backward()

			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	LevitModel.from_pretrained(_a	)
									self.assertIsNotNone(_a	)






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image






@require_torch
@require_vision
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@cached_property
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[Any]:
						return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

			@slow
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict:
						__A=	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
						    _a	)

						__A=	self.default_image_processor
						__A=	prepare_img()
						__A=	image_processor(images=_a			,    return_tensors='pt'	).to(_a	)

						# forward pass
						with torch.no_grad():
									__A=	model(**_a	)

						# verify the logits
						__A=	torch.Size((1, 1_000)	)
						self.assertEqual(outputs.logits.shape			,    _a	)

						__A=	torch.tensor([1.04_48, -0.37_45, -1.83_17]	).to(_a	)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]			,    _a			,    atol=1E-4	)	)






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   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Optional[int]      )     ->	Dict:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Union[str, Any]						,__a :     List[Any]						,__a :     Any=False      )     ->	Any:

					"""simple docstring"""

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

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.join(__a      ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

					# Encoder.
					for i in range(__a      ):
										# Block i, layer 0 (Self Attention).
										_a      :       Optional[Any]   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_attention_layer_norm'''      )
										_a ,   _a ,   _a ,   _a      :       List[str]   =						tax_attention_lookup(__a						,__a						,'''encoder'''						,'''attention'''      )
										_a      :       List[str]   =						layer_norm
										_a      :       Optional[Any]   =						k.T
										_a      :       str   =						o.T
										_a      :       List[Any]   =						q.T
										_a      :       Tuple   =						v.T

										# Block i, layer 1 (MLP).
										_a      :       str   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_mlp_layer_norm'''      )
										_a ,   _a      :       Any   =						tax_mlp_lookup(__a						,__a						,'''encoder'''						,__a      )
										_a      :       str   =						layer_norm
										if split_mlp_wi:
															_a      :       List[Any]   =						wi[0].T
															_a      :       Any   =						wi[1].T
										else:
															_a      :       Any   =						wi.T
										_a      :       Optional[Any]   =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_a      :       Dict   =						tax_relpos_bias_lookup(
															    __a						,__a						,'''encoder'''      ).T

					_a      :       List[str]   =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

					if not is_encoder_only:
										# Decoder.
										for i in range(__a      ):
															# Block i, layer 0 (Self Attention).
															_a      :       Union[str, Any]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_self_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       Optional[Any]   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''self_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Dict   =						k.T
															_a      :       str   =						o.T
															_a      :       str   =						q.T
															_a      :       List[str]   =						v.T

															# Block i, layer 1 (Cross Attention).
															_a      :       Any   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_cross_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       str   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''encoder_decoder_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Optional[int]   =						k.T
															_a      :       Dict   =						o.T
															_a      :       str   =						q.T
															_a      :       int   =						v.T

															# Block i, layer 2 (MLP).
															_a      :       Optional[int]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_mlp_layer_norm'''      )
															_a ,   _a      :       Tuple   =						tax_mlp_lookup(__a						,__a						,'''decoder'''						,__a      )
															_a      :       Optional[Any]   =						layer_norm
															if split_mlp_wi:
																				_a      :       List[str]   =						wi[0].T
																				_a      :       List[Any]   =						wi[1].T
															else:
																				_a      :       Dict   =						wi.T
															_a      :       str   =						wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       Tuple   =						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:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						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:
										_a      :       Any   =						state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_a      :       Optional[int]   =						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.'''      )
															_a      :       str   =						state_dict['''shared.weight''']

					return state_dict



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Dict						,__a :     Union[str, Any]						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.from_json_file(__a      )
					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:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

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

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

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


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



