






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


SCREAMING_SNAKE_CASE__   :      int				=		["""text""", """image""", """audio"""]

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   []

				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(snake_case,	snake_case			):
												inputs.append(create_inputs(snake_case			)			)
								else:
												raise ValueError(f'''Invalid type requested: {input_type}'''			)

				return inputs

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   []

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

				return output_types

@is_tool_test
class     lowerCamelCase_					:







				def       A						(    self  ):
								"""simple docstring"""
								self.assertTrue(hasattr(self.tool							,       '''inputs'''  )  )
								self.assertTrue(hasattr(self.tool							,       '''outputs'''  )  )

								__magic_name__		:Union[str, Any]   =   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  )

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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   create_inputs(self.tool.inputs  )
								__magic_name__		:Union[str, Any]   =   self.tool(*__lowerCAmelCase  )

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

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







				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   create_inputs(self.tool.inputs  )
								__magic_name__		:Optional[Any]   =   self.tool(*__lowerCAmelCase  )

								if not isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
												__magic_name__		:str   =   [outputs]

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

								for output, output_type in zip(__lowerCAmelCase							,       self.tool.outputs  ):
												__magic_name__		:Tuple   =   AGENT_TYPE_MAPPING[output_type]
												self.assertTrue(isinstance(__lowerCAmelCase							,       __lowerCAmelCase  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   create_inputs(self.tool.inputs  )

								__magic_name__		:Tuple   =   []

								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
								__magic_name__		:Optional[int]   =   self.tool(*__lowerCAmelCase  )

								if not isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
												__magic_name__		:Any   =   [outputs]

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









'''simple docstring'''





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Dict:
						lowercase :							List[str]    		=R'''\w+[.]\d+'''
						lowercase :							List[str]    		=re.findall(__magic_name__   , __magic_name__						)
						for pat in pats:
												lowercase :							Optional[int]    		=key.replace(__magic_name__   , '''_'''.join(pat.split('''.'''						)						)						)
						return key






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict						)				->  str:
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''scale''',)
						if (
						    any('''norm''' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowercase :							str    		=pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowercase :							Dict    		=pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowercase :							Tuple    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowercase :							Tuple    		=pt_tensor.transpose(2   , 3   , 1   , 0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowercase :							str    		=pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight":
												lowercase :							Optional[Any]    		=pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowercase :							Dict    		=pt_tuple_key[:-1] + ('''weight''',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowercase :							Union[str, Any]    		=pt_tuple_key[:-1] + ('''bias''',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor







def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]=42						)				->  List[str]:
						# Step 1: Convert pytorch tensor to numpy
						lowercase :							Optional[Any]    		={k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowercase :							str    		=flax_model.init_weights(PRNGKey(__magic_name__						)						)

						lowercase :							Dict    		=flatten_dict(__magic_name__						)
						lowercase :							Dict    		={}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase :							Dict    		=rename_key(__magic_name__						)
												lowercase :							Optional[int]    		=tuple(renamed_pt_key.split('''.'''						)						)

												# Correctly rename weight parameters
												lowercase		,	lowercase :							Any    		=rename_key_and_reshape_tensor(__magic_name__   , __magic_name__   , __magic_name__						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowercase :							Tuple    		=jnp.asarray(__magic_name__						)

						return unflatten_dict(__magic_name__						)





