
import unittest

import numpy as np

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

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 DPTImageProcessor
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Dict=7       ,							SCREAMING_SNAKE_CASE_  :			str=3       ,							SCREAMING_SNAKE_CASE_  :			Any=1_8       ,							SCREAMING_SNAKE_CASE_  :			Dict=3_0       ,							SCREAMING_SNAKE_CASE_  :			List[str]=4_0_0       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=True       ,							SCREAMING_SNAKE_CASE_  :			Dict=[0.5, 0.5, 0.5]       ,							SCREAMING_SNAKE_CASE_  :			Tuple=[0.5, 0.5, 0.5]       ,							)       ->     List[Any]:
					lowercase_       =					size if size is not None else {'''height''': 1_8, '''width''': 1_8}
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					num_channels
					lowercase_       =					image_size
					lowercase_       =					min_resolution
					lowercase_       =					max_resolution
					lowercase_       =					do_resize
					lowercase_       =					size
					lowercase_       =					do_normalize
					lowercase_       =					image_mean
					lowercase_       =					image_std




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}

@require_torch
@require_vision
class  lowercase__(				_snake_case ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	DPTImageProcessor if is_vision_available() else None
	def 						_lowercase  (       self  :			str       )       ->     Dict:
					lowercase_       =					DPTImageProcessingTester(self       )
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Dict:
					return self.image_processor_tester.prepare_image_processor_dict()
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[str]:
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(_lowerCamelCase       ,							'''image_mean'''       )       )
					self.assertTrue(hasattr(_lowerCamelCase       ,							'''image_std'''       )       )
					self.assertTrue(hasattr(_lowerCamelCase       ,							'''do_normalize'''       )       )
					self.assertTrue(hasattr(_lowerCamelCase       ,							'''do_resize'''       )       )
					self.assertTrue(hasattr(_lowerCamelCase       ,							'''size'''       )       )
	def 						_lowercase  (       self  :			Dict       )       ->     List[Any]:
					lowercase_       =					self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size       ,							{'''height''': 1_8, '''width''': 1_8}       )

					lowercase_       =					self.image_processing_class.from_dict(self.image_processor_dict       ,							size=4_2       )
					self.assertEqual(image_processor.size       ,							{'''height''': 4_2, '''width''': 4_2}       )
	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_lowerCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCamelCase       ,							Image.Image       )

					# Test not batched input
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)

					# Test batched
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[str]:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_lowerCamelCase       ,							numpify=_lowerCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCamelCase       ,							np.ndarray       )

					# Test not batched input
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)

					# Test batched
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)




	def 						_lowercase  (       self  :			Any       )       ->     Optional[Any]:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=_lowerCamelCase       ,							torchify=_lowerCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCamelCase       ,							torch.Tensor       )

					# Test not batched input
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)

					# Test batched
					lowercase_       =					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)








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 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





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

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.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
      a						:Any 							=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

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

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

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

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

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

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       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:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[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:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       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						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.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:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(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__":
     snake_case				:    Any       				=     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
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





