"""simple docstring"""


import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     YosoForMaskedLM,
     YosoForMultipleChoice,
     YosoForQuestionAnswering,
     YosoForSequenceClassification,
     YosoForTokenClassification,
     YosoModel,
 )
 from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST






class  a__     :


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=13,					_UpperCAmelCase=7,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase=99,					_UpperCAmelCase=32,					_UpperCAmelCase=5,					_UpperCAmelCase=4,					_UpperCAmelCase=37,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.1,					_UpperCAmelCase=512,					_UpperCAmelCase=16,					_UpperCAmelCase=2,					_UpperCAmelCase=0.02,					_UpperCAmelCase=3,					_UpperCAmelCase=4,					_UpperCAmelCase=None,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				seq_length
        lowercase__								=				is_training
        lowercase__								=				use_input_mask
        lowercase__								=				use_token_type_ids
        lowercase__								=				use_labels
        lowercase__								=				vocab_size
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				max_position_embeddings
        lowercase__								=				type_vocab_size
        lowercase__								=				type_sequence_label_size
        lowercase__								=				initializer_range
        lowercase__								=				num_labels
        lowercase__								=				num_choices
        lowercase__								=				scope


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

        lowercase__								=				None
        if self.use_input_mask:
             lowercase__								=				random_attention_mask([self.batch_size, self.seq_length]	)

        lowercase__								=				None
        if self.use_token_type_ids:
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size	)

        lowercase__								=				None
        lowercase__								=				None
        lowercase__								=				None
        if self.use_labels:
             lowercase__								=				ids_tensor([self.batch_size],					self.type_sequence_label_size	)
             lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					self.num_labels	)
             lowercase__								=				ids_tensor([self.batch_size],					self.num_choices	)

        lowercase__								=				self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return YosoConfig(
            vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					is_decoder=_UpperCAmelCase,					initializer_range=self.initializer_range,					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.get_config()
        lowercase__								=				300
        return config


   def 						snake_case__						(  self	):
        '''simple docstring'''



        (
            (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    
        )								=				self.prepare_config_and_inputs()

        lowercase__								=				True
        lowercase__								=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
        lowercase__								=				ids_tensor([self.batch_size, self.seq_length],					vocab_size=2	)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				YosoModel(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        lowercase__								=				model(_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				True
        lowercase__								=				YosoModel(_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					encoder_hidden_states=_UpperCAmelCase,					encoder_attention_mask=_UpperCAmelCase,					)
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					encoder_hidden_states=_UpperCAmelCase,					)
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase	)
        self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				YosoForMaskedLM(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				YosoForQuestionAnswering(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					start_positions=_UpperCAmelCase,					end_positions=_UpperCAmelCase,					)
        self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				YosoForSequenceClassification(_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				YosoForTokenClassification(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				model(_UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.num_choices
        lowercase__								=				YosoForMultipleChoice(config=_UpperCAmelCase	)
        model.to(_UpperCAmelCase	)
        model.eval()
        lowercase__								=				input_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				token_type_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				input_mask.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
        lowercase__								=				model(
            _UpperCAmelCase,					attention_mask=_UpperCAmelCase,					token_type_ids=_UpperCAmelCase,					labels=_UpperCAmelCase,					)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    
        )								=				config_and_inputs
        lowercase__								=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_torch
class  a__     ( _a       ,       _a       ,       unittest.TestCase						):
   snake_case_				=   (
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   snake_case_				=   False
   snake_case_				=   False
   snake_case_				=   False

   snake_case_				=   ()
   snake_case_				=   (
       {
           "feature-extraction": YosoModel,
           "fill-mask": YosoForMaskedLM,
           "question-answering": YosoForQuestionAnswering,
           "text-classification": YosoForSequenceClassification,
           "token-classification": YosoForTokenClassification,
           "zero-shot": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				YosoModelTester(self	)
        lowercase__								=				ConfigTester(self,					config_class=_UpperCAmelCase,					hidden_size=37	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             lowercase__								=				type
             self.model_tester.create_and_check_model(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__								=				YosoModel.from_pretrained(_UpperCAmelCase	)
             self.assertIsNotNone(_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return






@require_torch
class  a__     ( unittest.TestCase						):


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				YosoModel.from_pretrained("uw-madison/yoso-4096"	)
        lowercase__								=				torch.tensor([[0, 1, 2, 3, 4, 5]]	)

        with torch.no_grad():
             lowercase__								=				model(_UpperCAmelCase	)[0]

        lowercase__								=				torch.Size((1, 6, 768)	)
        self.assertEqual(output.shape,					_UpperCAmelCase	)

        lowercase__								=				torch.tensor(
            [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3],					_UpperCAmelCase,					atol=1E-4	)	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"	)
        lowercase__								=				torch.tensor([[0, 1, 2, 3, 4, 5]]	)

        with torch.no_grad():
             lowercase__								=				model(_UpperCAmelCase	)[0]

        lowercase__								=				5_0265

        lowercase__								=				torch.Size((1, 6, vocab_size)	)
        self.assertEqual(output.shape,					_UpperCAmelCase	)

        lowercase__								=				torch.tensor(
            [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3],					_UpperCAmelCase,					atol=1E-4	)	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"	)
        lowercase__								=				torch.arange(4096	).unsqueeze(0	)

        with torch.no_grad():
             lowercase__								=				model(_UpperCAmelCase	)[0]

        lowercase__								=				5_0265

        lowercase__								=				torch.Size((1, 4096, vocab_size)	)
        self.assertEqual(output.shape,					_UpperCAmelCase	)

        lowercase__								=				torch.tensor(
            [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]]	)

        self.assertTrue(torch.allclose(output[:, :3, :3],					_UpperCAmelCase,					atol=1E-4	)	)





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

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

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

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

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


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




