



'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

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

    # Encoder
    for layer_index in range(config.num_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # 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(lowerCAmelCase_	)}'

        # 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(lowerCAmelCase_	)
    print("T5X Model was sucessfully converted!"	)


if __name__ == "__main__":
      lowercase__     :  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.'
      )
      lowercase__     :  int    					=			parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types

class       lowerCamelCase_							(	unittest.TestCase					,    lowerCAmelCase_  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			load_tool("translation" )
       self.tool.setup()
       UpperCamelCase__      =			load_tool("translation"     ,						remote=__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       # There is a single output
       if len(self.tool.outputs ) == 1:
           UpperCamelCase__      =			[outputs]

       self.assertListEqual(output_types(__UpperCamelCase )     ,						self.tool.outputs )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )

       for output, output_type in zip(__UpperCamelCase     ,						self.tool.outputs ):
           UpperCamelCase__      =			AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(__UpperCamelCase     ,						__UpperCamelCase ) )







   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]

       UpperCamelCase__      =			[]

       for _input, input_type in zip(__UpperCamelCase     ,						self.tool.inputs ):
           if isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

       self.assertEqual(len(__UpperCamelCase )     ,						len(self.tool.outputs ) )




