






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							,       _UpperCAmelCase							,       _UpperCAmelCase):
			return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase="attention"):
			SCREAMING_SNAKE_CASE						= SCREAMING_SNAKE_CASE						= np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :])
			SCREAMING_SNAKE_CASE						= k_tmp.reshape(k_tmp.shape[0]							,       k_tmp.shape[1] * k_tmp.shape[2])
			SCREAMING_SNAKE_CASE						= np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :])
			SCREAMING_SNAKE_CASE						= o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,       o_tmp.shape[2])
			SCREAMING_SNAKE_CASE						= np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :])
			SCREAMING_SNAKE_CASE						= q_tmp.reshape(q_tmp.shape[0]							,       q_tmp.shape[1] * q_tmp.shape[2])
			SCREAMING_SNAKE_CASE						= np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :])
			SCREAMING_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							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=False):
			if split_mlp_wi:
						SCREAMING_SNAKE_CASE						= params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
						SCREAMING_SNAKE_CASE						= params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
						SCREAMING_SNAKE_CASE						= (wi_a, wi_a)
			else:
						SCREAMING_SNAKE_CASE						= params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

			SCREAMING_SNAKE_CASE						= params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
			return wi, wo






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def 				lowerCamelCase__				(_UpperCAmelCase							,       *, _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase = False):
			SCREAMING_SNAKE_CASE						= traverse_util.flatten_dict(variables['target'])
			SCREAMING_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
			SCREAMING_SNAKE_CASE						= 'encoder/encoder/mlp/wi_0/kernel' in old
			print('Split MLP:'							,       _UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= collections.OrderedDict()

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

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

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

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

			if not scalable_attention:
						SCREAMING_SNAKE_CASE						= tax_relpos_bias_lookup(
						    _UpperCAmelCase							,       0							,       'encoder').T
						SCREAMING_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).
									SCREAMING_SNAKE_CASE						= tax_layer_norm_lookup(_UpperCAmelCase							,       _UpperCAmelCase							,       'decoder'							,       'pre_self_attention_layer_norm')
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= tax_attention_lookup(_UpperCAmelCase							,       _UpperCAmelCase							,       'decoder'							,       'self_attention')
									SCREAMING_SNAKE_CASE						= layer_norm
									SCREAMING_SNAKE_CASE						= k.T
									SCREAMING_SNAKE_CASE						= o.T
									SCREAMING_SNAKE_CASE						= q.T
									SCREAMING_SNAKE_CASE						= v.T

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

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

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

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

			return new






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_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:
						SCREAMING_SNAKE_CASE						= state_dict['shared.weight']

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

			return state_dict






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= checkpoints.load_tax_checkpoint(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= convert_tax_to_pytorch(
			    _UpperCAmelCase							,       num_layers=config.num_layers							,       is_encoder_only=_UpperCAmelCase							,       scalable_attention=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= make_state_dict(_UpperCAmelCase							,       _UpperCAmelCase)
			model.load_state_dict(_UpperCAmelCase							,       strict=_UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase = False							,       _UpperCAmelCase = False							,       ):
			SCREAMING_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:
						SCREAMING_SNAKE_CASE						= UMTaEncoderModel(_UpperCAmelCase)
			else:
						SCREAMING_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__":
				a_		:						Tuple             =    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_		:						Union[str, Any]             =    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

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 MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})

						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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'],
						    )		,					)




