



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     TaConfig.from_pretrained(__UpperCAmelCase	)
						UpperCAmelCase_     							=     FlaxTaForConditionalGeneration(config=__UpperCAmelCase	)
						UpperCAmelCase_     							=     checkpoints.load_tax_checkpoint(__UpperCAmelCase	)

						UpperCAmelCase_     							=     '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers	):
												UpperCAmelCase_     							=     f"layers_{str(__UpperCAmelCase	)}"

												# Self-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												UpperCAmelCase_     							=     tax_attention_key
												UpperCAmelCase_     							=     tax_attention_out
												UpperCAmelCase_     							=     tax_attention_query
												UpperCAmelCase_     							=     tax_attention_value

												UpperCAmelCase_     							=     tax_attention_layer_norm

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_mlp_wi_a
																		UpperCAmelCase_     							=     tax_mlp_wi_a
												else:
																		UpperCAmelCase_     							=     tax_mlp_wi

												UpperCAmelCase_     							=     tax_mlp_wo
												UpperCAmelCase_     							=     tax_mlp_layer_norm

						# Only for layer 0:
						UpperCAmelCase_     							=     tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						UpperCAmelCase_     							=     tax_encoder_rel_embedding

						# Assigning
						UpperCAmelCase_     							=     tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						UpperCAmelCase_     							=     tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers	):
												UpperCAmelCase_     							=     f"layers_{str(__UpperCAmelCase	)}"

												# Self-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												UpperCAmelCase_     							=     tax_attention_key
												UpperCAmelCase_     							=     tax_attention_out
												UpperCAmelCase_     							=     tax_attention_query
												UpperCAmelCase_     							=     tax_attention_value

												UpperCAmelCase_     							=     tax_pre_attention_layer_norm

												UpperCAmelCase_     							=     tax_enc_dec_attention_key
												UpperCAmelCase_     							=     tax_enc_dec_attention_out
												UpperCAmelCase_     							=     tax_enc_dec_attention_query
												UpperCAmelCase_     							=     tax_enc_dec_attention_value

												UpperCAmelCase_     							=     tax_cross_layer_norm

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_mlp_wi_a
																		UpperCAmelCase_     							=     tax_mlp_wi_a
												else:
																		UpperCAmelCase_     							=     tax_mlp_wi

												UpperCAmelCase_     							=     tax_mlp_wo

												UpperCAmelCase_     							=     txa_mlp_layer_norm

						# Decoder Normalization
						UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						UpperCAmelCase_     							=     txa_decoder_norm

						# Only for layer 0:
						UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						UpperCAmelCase_     							=     tax_decoder_rel_embedding

						# Token Embeddings
						UpperCAmelCase_     							=     tax_model['''target''']['''token_embedder''']['''embedding''']
						UpperCAmelCase_     							=     txa_token_embeddings

						# LM Head (only in v1.1 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

						flax_model.save_pretrained(__UpperCAmelCase	)
						print('''T5X Model was sucessfully converted!'''	)


if __name__ == "__main__":
							UpperCamelCase_	       =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
							)
							parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
							parser.add_argument(
							    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
							)
							UpperCamelCase_	       =   parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import CLIPImageProcessor





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=7			,  __lowerCamelCase=3			,  __lowerCamelCase=1_8			,  __lowerCamelCase=3_0			,  __lowerCamelCase=4_0_0			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]			,  __lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,  __lowerCamelCase=True			,  )	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							size if size is not None else {"shortest_edge": 2_0}
            _SCREAMING_SNAKE_CASE				:		int  			=							crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _SCREAMING_SNAKE_CASE				:		str  			=							parent
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_channels
            _SCREAMING_SNAKE_CASE				:		Any  			=							image_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							min_resolution
            _SCREAMING_SNAKE_CASE				:		int  			=							max_resolution
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							do_resize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_center_crop
            _SCREAMING_SNAKE_CASE				:		Dict  			=							crop_size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_normalize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_mean
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_std
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							do_convert_rgb




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_convert_rgb": self.do_convert_rgb,
            }







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)	->   Tuple:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
                 for i in range(self.batch_size							):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5			,  size=(self.num_channels, self.max_resolution, self.max_resolution)			,  dtype=np.uinta							)							)
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							[]
                 for i in range(self.batch_size							):
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							np.random.choice(np.arange(self.min_resolution			,  self.max_resolution							)			,  2							)
                      image_inputs.append(np.random.randint(2_5_5			,  size=(self.num_channels, width, height)			,  dtype=np.uinta							)							)

            if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            if torchify:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[torch.from_numpy(__lowerCamelCase							) for x in image_inputs]

            return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							CLIPImageProcessingTester(self							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 2_0}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 1_8, "width": 1_8}							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict			,  size=4_2			,  crop_size=8_4							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 4_2}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 8_4, "width": 8_4}							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            pass




       def 			UpperCamelCase_	(					self							)	->   str:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		int  			=							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				:		List[str]  			=							image_processing(__lowerCamelCase			,  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 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random numpy tensors
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  numpify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  np.ndarray							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		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
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							image_processing(__lowerCamelCase			,  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 			UpperCamelCase_	(					self							)	->   Any:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PyTorch tensors
            _SCREAMING_SNAKE_CASE				:		int  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  torchify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  torch.Tensor							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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				:		List[str]  			=							image_processing(__lowerCamelCase			,  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"],
                )			,  )





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							CLIPImageProcessingTester(self			,  num_channels=4							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							3




       @property
       def 			UpperCamelCase_	(					self							)	->   Dict:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            pass







       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

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

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