





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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_="attention"		)   ->     List[str]:
		lowerCamelCase   : Optional[Any]								=params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
		lowerCamelCase   : str								=params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
		lowerCamelCase   : List[str]								=params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
		lowerCamelCase   : List[Any]								=params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
		return k, o, q, v


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False		)   ->     str:
		if split_mlp_wi:
				lowerCamelCase   : Optional[int]								=params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
				lowerCamelCase   : Tuple								=params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
				lowerCamelCase   : str								=(wi_a, wi_a)
		else:
				lowerCamelCase   : Tuple								=params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

		lowerCamelCase   : Union[str, Any]								=params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
		return wi, wo


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Optional[int]:
		return params[F"{prefix}/layers_{i}/{layer_name}/scale"]


def 					A__	(       SCREAMING_SNAKE_CASE_							,       *, SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
		lowerCamelCase   : Tuple								=traverse_util.flatten_dict(variables['''target''']		)
		lowerCamelCase   : int								={'/'.join(SCREAMING_SNAKE_CASE_		): v for k, v in old.items()}

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

		lowerCamelCase   : Dict								=collections.OrderedDict()

		# Shared embeddings.
		lowerCamelCase   : Optional[Any]								=old['token_embedder/embedding']

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

				# Block i, layer 1 (MLP).
				lowerCamelCase   : Dict								=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''encoder'''							,       '''pre_mlp_layer_norm'''		)
				lowerCamelCase   : Optional[Any]								=tax_mlp_lookup(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''encoder'''							,       SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : Union[str, Any]								=layer_norm
				if split_mlp_wi:
						lowerCamelCase   : Dict								=wi[0].T
						lowerCamelCase   : Union[str, Any]								=wi[1].T
				else:
						lowerCamelCase   : int								=wi.T
				lowerCamelCase   : int								=wo.T

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

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

						# Block i, layer 1 (Cross Attention).
						lowerCamelCase   : str								=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''decoder'''							,       '''pre_cross_attention_layer_norm'''		)
						lowerCamelCase   : Tuple								=tax_attention_lookup(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''decoder'''							,       '''encoder_decoder_attention'''		)
						lowerCamelCase   : Tuple								=layer_norm
						lowerCamelCase   : List[Any]								=k.T
						lowerCamelCase   : Optional[int]								=o.T
						lowerCamelCase   : int								=q.T
						lowerCamelCase   : Optional[Any]								=v.T

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

				lowerCamelCase   : Dict								=old['decoder/decoder_norm/scale']
				lowerCamelCase   : List[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:
						lowerCamelCase   : Dict								=old['decoder/logits_dense/kernel'].T

		return new


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
		lowerCamelCase   : Tuple								=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:
				lowerCamelCase   : Tuple								=state_dict['shared.weight']

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

		return state_dict


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : str								=checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Dict								=convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_							,       num_layers=config.num_layers							,       is_encoder_only=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Union[str, Any]								=make_state_dict(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		model.load_state_dict(SCREAMING_SNAKE_CASE_							,       strict=SCREAMING_SNAKE_CASE_		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False		)   ->     List[str]:
		lowerCamelCase   : Optional[Any]								=TaConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		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:
				lowerCamelCase   : Any								=TaEncoderModel(SCREAMING_SNAKE_CASE_		)
		else:
				lowerCamelCase   : Optional[int]								=TaForConditionalGeneration(SCREAMING_SNAKE_CASE_		)

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

		# Save pytorch-model
		print(F"Save PyTorch model to {pytorch_dump_path}"		)
		model.save_pretrained(SCREAMING_SNAKE_CASE_		)

		# Verify that we can load the checkpoint.
		model.from_pretrained(SCREAMING_SNAKE_CASE_		)
		print('''Done'''		)


if __name__ == "__main__":
	snake_case_							=			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_							=			parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)








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


snake_case_							=			['''text''', '''image''', '''audio''']


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
		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((5_1_2, 5_1_2)		)		)
				elif input_type == "audio":
						inputs.append(torch.ones(3_0_0_0		)		)
				elif isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						inputs.append(create_inputs(SCREAMING_SNAKE_CASE_		)		)
				else:
						raise ValueError(F"Invalid type requested: {input_type}"		)

		return inputs


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Optional[int]:
		lowerCamelCase   : str								=[]

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

		return output_types



@is_tool_test
class        snake_case_		:







		def 			__lowercase   (		self   )				->   Tuple:
				self.assertTrue(hasattr(self.tool			,     '''inputs'''   )   )
				self.assertTrue(hasattr(self.tool			,     '''outputs'''   )   )

				lowerCamelCase   : List[Any]								=self.tool.inputs
				for _input in inputs:
						if isinstance(_input			,     __lowercase   ):
								for __input in _input:
										self.assertTrue(__input in authorized_types   )
						else:
								self.assertTrue(_input in authorized_types   )

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







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Dict								=create_inputs(self.tool.inputs   )
				lowerCamelCase   : Any								=self.tool(*__lowercase   )

				# There is a single output
				if len(self.tool.outputs   ) == 1:
						lowerCamelCase   : Union[str, Any]								=[outputs]

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







		def 			__lowercase   (		self   )				->   Any:
				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 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Dict								=create_inputs(self.tool.inputs   )
				lowerCamelCase   : Dict								=self.tool(*__lowercase   )

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

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

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







		def 			__lowercase   (		self   )				->   List[str]:
				lowerCamelCase   : Optional[Any]								=create_inputs(self.tool.inputs   )

				lowerCamelCase   : str								=[]

				for _input, input_type in zip(__lowercase			,     self.tool.inputs   ):
						if isinstance(__lowercase			,     __lowercase   ):
								_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   : Union[str, Any]								=self.tool(*__lowercase   )

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

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

