"""simple docstring"""





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	str="attention"      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
							__lowerCamelCase					:		Any		   =     params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
							__lowerCamelCase					:		int		   =     params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
							__lowerCamelCase					:		Any		   =     params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
							return k, o, q, v


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]=False      ) ->      Optional[int]:



							'''simple docstring'''

							if split_mlp_wi:
														__lowerCamelCase					:		Optional[int]		   =     params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
														__lowerCamelCase					:		Dict		   =     params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
														__lowerCamelCase					:		int		   =     (wi_a, wi_a)
							else:
														__lowerCamelCase					:		Optional[Any]		   =     params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

							__lowerCamelCase					:		str		   =     params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
							return wi, wo


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Any      ) ->      Optional[Any]:



							'''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	dict						,   *, _lowerCamelCase:	int						,   _lowerCamelCase:	bool      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     traverse_util.flatten_dict(variables["target"]      )
							__lowerCamelCase					:		Optional[Any]		   =     {"/".join(_lowerCamelCase      ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__lowerCamelCase					:		Optional[int]		   =     "encoder/layers_0/mlp/wi_0/kernel" in old
							print("Split MLP:"						,   _lowerCamelCase      )

							__lowerCamelCase					:		Union[str, Any]		   =     collections.OrderedDict()

							# Shared embeddings.
							__lowerCamelCase					:		Optional[int]		   =     old["token_embedder/embedding"]

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

														# Block i, layer 1 (MLP).
														__lowerCamelCase					:		str		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   "pre_mlp_layer_norm"      )
														__lowerCamelCase					:		List[str]		   =     tax_mlp_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   _lowerCamelCase      )
														__lowerCamelCase					:		List[Any]		   =     layer_norm
														if split_mlp_wi:
																					__lowerCamelCase					:		Dict		   =     wi[0].T
																					__lowerCamelCase					:		Tuple		   =     wi[1].T
														else:
																					__lowerCamelCase					:		str		   =     wi.T
														__lowerCamelCase					:		int		   =     wo.T

							__lowerCamelCase					:		Optional[int]		   =     old[
							    "encoder/relpos_bias/rel_embedding"
							].T
							__lowerCamelCase					:		Tuple		   =     old["encoder/encoder_norm/scale"]

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

																					# Block i, layer 1 (Cross Attention).
																					__lowerCamelCase					:		Dict		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "pre_cross_attention_layer_norm"      )
																					__lowerCamelCase					:		Tuple		   =     tax_attention_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "encoder_decoder_attention"      )
																					__lowerCamelCase					:		Optional[Any]		   =     layer_norm
																					__lowerCamelCase					:		Dict		   =     k.T
																					__lowerCamelCase					:		Tuple		   =     o.T
																					__lowerCamelCase					:		Optional[int]		   =     q.T
																					__lowerCamelCase					:		Optional[Any]		   =     v.T

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

														__lowerCamelCase					:		Optional[Any]		   =     old["decoder/decoder_norm/scale"]
														__lowerCamelCase					:		int		   =     old[
														    "decoder/relpos_bias/rel_embedding"
														].T

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

							return new


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	bool      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		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:
														__lowerCamelCase					:		Dict		   =     state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__lowerCamelCase					:		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."      )
																					__lowerCamelCase					:		List[Any]		   =     state_dict["shared.weight"]

							return state_dict


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	List[str]      ) ->      Union[str, Any]:



							'''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	bool = False      ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     TaConfig.from_json_file(_lowerCamelCase      )
							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:
														__lowerCamelCase					:		Union[str, Any]		   =     TaEncoderModel(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		List[str]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(_lowerCamelCase      )
							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
				)
				__A  =				parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)