"""simple docstring"""






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 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[Any]="attention"		) ->    Any:
      '''simple docstring'''


      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Optional[Any]=False		) ->    List[str]:
      '''simple docstring'''


      if split_mlp_wi:
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowercase														=	(wi_a, wi_a)
      else:
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi/kernel']

      lowercase														=	params[f'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Dict		) ->    Any:
      '''simple docstring'''


      return params[f'{prefix}/layers_{i}/{layer_name}/scale']


def 			UpperCAmelCase__       (				lowerCAmelCase__     :dict					,		*, lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	traverse_util.flatten_dict(variables["""target"""]		)
      lowercase														=	{'''/'''.join(__lowerCamelCase		): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowercase														=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
      print("""Split MLP:"""					,		__lowerCamelCase		)

      lowercase														=	collections.OrderedDict()

      # Shared embeddings.
      lowercase														=	old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__lowerCamelCase		):
            # Block i, layer 0 (Self Attention).
            lowercase														=	tax_layer_norm_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""encoder"""					,		"""pre_attention_layer_norm"""		)
            lowercase														=	tax_attention_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""encoder"""					,		"""attention"""		)
            lowercase														=	layer_norm
            lowercase														=	k.T
            lowercase														=	o.T
            lowercase														=	q.T
            lowercase														=	v.T

            # Block i, layer 1 (MLP).
            lowercase														=	tax_layer_norm_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""encoder"""					,		"""pre_mlp_layer_norm"""		)
            lowercase														=	tax_mlp_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""encoder"""					,		__lowerCamelCase		)
            lowercase														=	layer_norm
            if split_mlp_wi:
                  lowercase														=	wi[0].T
                  lowercase														=	wi[1].T
            else:
                  lowercase														=	wi.T
            lowercase														=	wo.T

      lowercase														=	old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      lowercase														=	old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__lowerCamelCase		):
                  # Block i, layer 0 (Self Attention).
                  lowercase														=	tax_layer_norm_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		"""pre_self_attention_layer_norm"""		)
                  lowercase														=	tax_attention_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		"""self_attention"""		)
                  lowercase														=	layer_norm
                  lowercase														=	k.T
                  lowercase														=	o.T
                  lowercase														=	q.T
                  lowercase														=	v.T

                  # Block i, layer 1 (Cross Attention).
                  lowercase														=	tax_layer_norm_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		"""pre_cross_attention_layer_norm"""		)
                  lowercase														=	tax_attention_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		"""encoder_decoder_attention"""		)
                  lowercase														=	layer_norm
                  lowercase														=	k.T
                  lowercase														=	o.T
                  lowercase														=	q.T
                  lowercase														=	v.T

                  # Block i, layer 2 (MLP).
                  lowercase														=	tax_layer_norm_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		"""pre_mlp_layer_norm"""		)
                  lowercase														=	tax_mlp_lookup(__lowerCamelCase					,		__lowerCamelCase					,		"""decoder"""					,		__lowerCamelCase		)
                  lowercase														=	layer_norm
                  if split_mlp_wi:
                        lowercase														=	wi[0].T
                        lowercase														=	wi[1].T
                  else:
                        lowercase														=	wi.T
                  lowercase														=	wo.T

            lowercase														=	old['''decoder/decoder_norm/scale''']
            lowercase														=	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:
                  lowercase														=	old['''decoder/logits_dense/kernel'''].T

      return new


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :bool		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	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:
            lowercase														=	state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowercase														=	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."""		)
                  lowercase														=	state_dict['''shared.weight''']

      return state_dict


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]		) ->    int:
      '''simple docstring'''


      lowercase														=	checkpoints.load_tax_checkpoint(__lowerCamelCase		)
      lowercase														=	convert_tax_to_pytorch(__lowerCamelCase					,		num_layers=config.num_layers					,		is_encoder_only=__lowerCamelCase		)
      lowercase														=	make_state_dict(__lowerCamelCase					,		__lowerCamelCase		)
      model.load_state_dict(__lowerCamelCase					,		strict=__lowerCamelCase		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :bool = False		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	TaConfig.from_json_file(__lowerCamelCase		)
      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:
            lowercase														=	TaEncoderModel(__lowerCamelCase		)
      else:
            lowercase														=	TaForConditionalGeneration(__lowerCamelCase		)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase		)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__lowerCamelCase		)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__lowerCamelCase		)
      print("""Done"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       List[str]					     =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
       )
       __lowerCAmelCase    :       int					     =parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )








"""simple docstring"""






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


_lowercase : Union[str, Any]      						=						["text", "image", "audio"]






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=[]

			for input_type in input_types:
						if input_type == "text":
									inputs.append('''Text input'''	)
						elif input_type == "image":
									inputs.append(
									    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
						elif input_type == "audio":
									inputs.append(torch.ones(3000	)	)
						elif isinstance(__lowerCamelCase							,		__lowerCamelCase	):
									inputs.append(create_inputs(__lowerCamelCase	)	)
						else:
									raise ValueError(f'''Invalid type requested: {input_type}'''	)

			return inputs






def      snake_case__   (		__lowerCamelCase				:    List	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[]

			for output in outputs:
						if isinstance(__lowerCamelCase							,		(str, AgentText)	):
									output_types.append('''text'''	)
						elif isinstance(__lowerCamelCase							,		(Image.Image, AgentImage)	):
									output_types.append('''image'''	)
						elif isinstance(__lowerCamelCase							,		(torch.Tensor, AgentAudio)	):
									output_types.append('''audio'''	)
						else:
									raise ValueError(f'''Invalid output: {output}'''	)

			return output_types

@is_tool_test
class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								self.assertTrue(hasattr(self.tool,  '''inputs'''     )     )
								self.assertTrue(hasattr(self.tool,  '''outputs'''     )     )

								lowerCamelCase__		:		Tuple			=self.tool.inputs
								for _input in inputs:
											if isinstance(_input,  lowerCamelCase     ):
														for __input in _input:
																	self.assertTrue(__input in authorized_types     )
											else:
														self.assertTrue(_input in authorized_types     )

								lowerCamelCase__		:		Optional[Any]			=self.tool.outputs
								for _output in outputs:
											self.assertTrue(_output in authorized_types     )



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		List[Any]			=self.tool(*lowerCamelCase     )

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

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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								self.assertTrue(hasattr(self.tool,  '''description'''     )     )
								self.assertTrue(hasattr(self.tool,  '''default_checkpoint'''     )     )
								self.assertTrue(self.tool.description.startswith('''This is a tool that'''     )     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					str:
								lowerCamelCase__		:		List[str]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		Optional[Any]			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Any			=[outputs]

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

								for output, output_type in zip(lowerCamelCase,  self.tool.outputs     ):
											lowerCamelCase__		:		List[Any]			=AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(lowerCamelCase,  lowerCamelCase     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=create_inputs(self.tool.inputs     )

								lowerCamelCase__		:		List[str]			=[]

								for _input, input_type in zip(lowerCamelCase,  self.tool.inputs     ):
											if isinstance(lowerCamelCase,  lowerCamelCase     ):
														_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
								lowerCamelCase__		:		Any			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Optional[int]			=[outputs]

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




