




'''simple docstring'''



import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType





@require_torch
class       lowerCAmelCase				:
       __lowercase					: Any	    =     ()
       __lowercase					: List[Any]	    =     True


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					self.config_class

            # test default config
            __snake_case         =					config_class()
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					['''stem'''] + [F'''stage{idx}''' for idx in range(1      ,					len(config.depths					) + 1					)]
            self.assertEqual(config.stage_names      ,					__SCREAMING_SNAKE_CASE					)
            self.assertTrue(set(config.out_features					).issubset(set(config.stage_names					)					)					)

            # Test out_features and out_indices are correctly set
            # out_features and out_indices both None
            __snake_case         =					config_class(out_features=__SCREAMING_SNAKE_CASE      ,					out_indices=__SCREAMING_SNAKE_CASE					)
            self.assertEqual(config.out_features      ,					[config.stage_names[-1]]					)
            self.assertEqual(config.out_indices      ,					[len(config.stage_names					) - 1]					)

            # out_features and out_indices both set
            __snake_case         =					config_class(out_features=['''stem''', '''stage1''']      ,					out_indices=[0, 1]					)
            self.assertEqual(config.out_features      ,					['''stem''', '''stage1''']					)
            self.assertEqual(config.out_indices      ,					[0, 1]					)

            # Only out_features set
            __snake_case         =					config_class(out_features=['''stage1''', '''stage3''']					)
            self.assertEqual(config.out_features      ,					['''stage1''', '''stage3''']					)
            self.assertEqual(config.out_indices      ,					[1, 3]					)

            # Only out_indices set
            __snake_case         =					config_class(out_indices=[0, 2]					)
            self.assertEqual(config.out_features      ,					[config.stage_names[0], config.stage_names[2]]					)
            self.assertEqual(config.out_indices      ,					[0, 2]					)

            # Error raised when out_indices do not correspond to out_features
            with self.assertRaises(__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					config_class(out_features=['''stage1''', '''stage2''']      ,					out_indices=[0, 2]					)


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case         =					[*signature.parameters.keys()]
                 __snake_case         =					['''pixel_values''']
                 self.assertListEqual(arg_names[:1]      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 self.assertEqual(len(model.channels					)      ,					len(config.out_features					)					)
                 __snake_case         =					model.num_features
                 __snake_case         =					[config.stage_names.index(__SCREAMING_SNAKE_CASE					) for feat in config.out_features]
                 __snake_case         =					[num_features[idx] for idx in out_indices]
                 self.assertListEqual(model.channels      ,					__SCREAMING_SNAKE_CASE					)

                 __snake_case         =					copy.deepcopy(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					None
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 self.assertEqual(len(model.channels					)      ,					1					)
                 self.assertListEqual(model.channels      ,					[num_features[-1]]					)

                 __snake_case         =					copy.deepcopy(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					None
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 self.assertEqual(len(model.channels					)      ,					1					)
                 self.assertListEqual(model.channels      ,					[num_features[-1]]					)


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)

                 self.assertEqual(len(result.feature_maps					)      ,					len(config.out_features					)					)
                 self.assertEqual(len(model.channels					)      ,					len(config.out_features					)					)
                 self.assertEqual(len(result.feature_maps					)      ,					len(config.out_indices					)					)
                 self.assertEqual(len(model.channels					)      ,					len(config.out_indices					)					)

                 # Check output of last stage is taken if out_features=None, out_indices=None
                 __snake_case         =					copy.deepcopy(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					None
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)

                 self.assertEqual(len(result.feature_maps					)      ,					1					)
                 self.assertEqual(len(model.channels					)      ,					1					)

                 __snake_case         =					copy.deepcopy(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					None
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)

                 self.assertEqual(len(result.feature_maps					)      ,					1					)
                 self.assertEqual(len(model.channels					)      ,					1					)

                 # Check backbone can be initialized with fresh weights
                 __snake_case         =					copy.deepcopy(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					False
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for backbone_class in self.all_model_classes:
                 __snake_case         =					backbone_class(__SCREAMING_SNAKE_CASE					)

                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''backbone_type'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''stage_names'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''num_features'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''out_indices'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''out_features'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''out_feature_channels'''					)					)
                 self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,					'''channels'''					)					)

                 self.assertIsInstance(backbone.backbone_type      ,					__SCREAMING_SNAKE_CASE					)
                 # Verify num_features has been initialized in the backbone init
                 self.assertIsNotNone(backbone.num_features					)
                 self.assertTrue(len(backbone.channels					) == len(backbone.out_indices					)					)
                 self.assertTrue(len(backbone.stage_names					) == len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.channels					) <= len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.out_feature_channels					) == len(backbone.stage_names					)					)




       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case         =					inputs_dict['''pixel_values'''].shape[0]

            for backbone_class in self.all_model_classes:
                 __snake_case         =					backbone_class(__SCREAMING_SNAKE_CASE					)
                 backbone.to(__SCREAMING_SNAKE_CASE					)
                 backbone.eval()

                 __snake_case         =					backbone(**__SCREAMING_SNAKE_CASE					)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps      ,					__SCREAMING_SNAKE_CASE					)
                 self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
                 for feature_map, n_channels in zip(outputs.feature_maps      ,					backbone.channels					):
                      self.assertTrue(feature_map.shape[:2]      ,					(batch_size, n_channels)					)
                 self.assertIsNone(outputs.hidden_states					)
                 self.assertIsNone(outputs.attentions					)

                 # Test output_hidden_states=True
                 __snake_case         =					backbone(**__SCREAMING_SNAKE_CASE      ,					output_hidden_states=__SCREAMING_SNAKE_CASE					)
                 self.assertIsNotNone(outputs.hidden_states					)
                 self.assertTrue(len(outputs.hidden_states					)      ,					len(backbone.stage_names					)					)
                 for hidden_state, n_channels in zip(outputs.hidden_states      ,					backbone.channels					):
                      self.assertTrue(hidden_state.shape[:2]      ,					(batch_size, n_channels)					)

                 # Test output_attentions=True
                 if self.has_attentions:
                      __snake_case         =					backbone(**__SCREAMING_SNAKE_CASE      ,					output_attentions=__SCREAMING_SNAKE_CASE					)
                      self.assertIsNotNone(outputs.attentions					)












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

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

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

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

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

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

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

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

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

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

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

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

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

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

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

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

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

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

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

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						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_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






