



'''simple docstring'''






from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_     =	logging.get_logger(__name__)

# TODO Update this
snake_case_     =	{
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """esm"""


   def __init__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=3_072    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=1_026    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-1_2    ,   _UpperCAmelCase="absolute"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(pad_token_id=_UpperCAmelCase    ,   mask_token_id=_UpperCAmelCase    ,   **_UpperCAmelCase					)

       A       :       Optional[int]							=						vocab_size
       A       :       str							=						hidden_size
       A       :       Tuple							=						num_hidden_layers
       A       :       Optional[Any]							=						num_attention_heads
       A       :       str							=						intermediate_size
       A       :       Optional[int]							=						hidden_dropout_prob
       A       :       int							=						attention_probs_dropout_prob
       A       :       Union[str, Any]							=						max_position_embeddings
       A       :       int							=						initializer_range
       A       :       int							=						layer_norm_eps
       A       :       List[str]							=						position_embedding_type
       A       :       List[Any]							=						use_cache
       A       :       Optional[int]							=						emb_layer_norm_before
       A       :       List[str]							=						token_dropout
       A       :       Optional[int]							=						is_folding_model
       if is_folding_model:
           if esmfold_config is None:
               logger.info('''No esmfold_config supplied for folding model, using default values.'''					)
               A       :       Dict							=						EsmFoldConfig()
           elif isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               A       :       Union[str, Any]							=						EsmFoldConfig(**_UpperCAmelCase					)
           A       :       List[Any]							=						esmfold_config
           if vocab_list is None:
               logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''					)
               A       :       Optional[int]							=						get_default_vocab_list()
           else:
               A       :       Any							=						vocab_list
       else:
           A       :       str							=						None
           A       :       Optional[Any]							=						None
       if self.esmfold_config is not None and getattr(self.esmfold_config    ,   '''use_esm_attn_map'''    ,   _UpperCAmelCase					):
           raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''					)


   def 					snake_case				( self					):
       A       :       List[str]							=						super().to_dict()
       if isinstance(self.esmfold_config    ,   _UpperCAmelCase					):
           A       :       int							=						self.esmfold_config.to_dict()
       return output







@dataclass
class   _lowercase						:
   _UpperCamelCase       		=   None
   _UpperCamelCase       		=   True
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   0

   _UpperCamelCase       		=   True
   _UpperCamelCase       		=   False

   _UpperCamelCase       		=   1_28
   _UpperCamelCase       		=   None


   def 					snake_case				( self					):
       if self.trunk is None:
           A       :       List[Any]							=						TrunkConfig()
       elif isinstance(self.trunk    ,   _UpperCAmelCase					):
           A       :       Any							=						TrunkConfig(**self.trunk					)


   def 					snake_case				( self					):
       A       :       List[str]							=						asdict(self					)
       A       :       str							=						self.trunk.to_dict()
       return output







@dataclass
class   _lowercase						:
   _UpperCamelCase       		=   48
   _UpperCamelCase       		=   10_24
   _UpperCamelCase       		=   1_28
   _UpperCamelCase       		=   32
   _UpperCamelCase       		=   32
   _UpperCamelCase       		=   32
   _UpperCamelCase       		=   0
   _UpperCamelCase       		=   0
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   4
   _UpperCamelCase       		=   1_28
   _UpperCamelCase       		=   None


   def 					snake_case				( self					):
       if self.structure_module is None:
           A       :       str							=						StructureModuleConfig()
       elif isinstance(self.structure_module    ,   _UpperCAmelCase					):
           A       :       List[Any]							=						StructureModuleConfig(**self.structure_module					)

       if self.max_recycles <= 0:
           raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.'''					)
       if self.sequence_state_dim % self.sequence_state_dim != 0:
           raise ValueError(
               '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
               f''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''					)
       if self.pairwise_state_dim % self.pairwise_state_dim != 0:
           raise ValueError(
               '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
               f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''					)

       A       :       Any							=						self.sequence_state_dim // self.sequence_head_width
       A       :       Union[str, Any]							=						self.pairwise_state_dim // self.pairwise_head_width

       if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
           raise ValueError(
               '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
               f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''					)
       if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
           raise ValueError(
               '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
               f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''					)
       if self.pairwise_state_dim % 2 != 0:
           raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''					)

       if self.dropout >= 0.4:
           raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.'''					)


   def 					snake_case				( self					):
       A       :       int							=						asdict(self					)
       A       :       Optional[Any]							=						self.structure_module.to_dict()
       return output







@dataclass
class   _lowercase						:
   _UpperCamelCase       		=   3_84
   _UpperCamelCase       		=   1_28
   _UpperCamelCase       		=   16
   _UpperCamelCase       		=   1_28
   _UpperCamelCase       		=   12
   _UpperCamelCase       		=   4
   _UpperCamelCase       		=   8
   _UpperCamelCase       		=   0.1
   _UpperCamelCase       		=   8
   _UpperCamelCase       		=   1
   _UpperCamelCase       		=   2
   _UpperCamelCase       		=   7
   _UpperCamelCase       		=   10
   _UpperCamelCase       		=   1e-8
   _UpperCamelCase       		=   1e5


   def 					snake_case				( self					):
       return asdict(self					)







def 							_lowerCamelCase(  )		->  Tuple:
    return (
        "<cls>",
        "<pad>",
        "<eos>",
        "<unk>",
        "L",
        "A",
        "G",
        "V",
        "S",
        "E",
        "R",
        "T",
        "I",
        "D",
        "P",
        "K",
        "Q",
        "N",
        "F",
        "Y",
        "M",
        "H",
        "W",
        "C",
        "X",
        "B",
        "U",
        "Z",
        "O",
        ".",
        "-",
        "<null_1>",
        "<mask>",
    )







'''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


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       int							=						[]

    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(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

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

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

       A       :       Any							=						self.tool.inputs
       for _input in inputs:
           if isinstance(_input    ,   _UpperCAmelCase					):
               for __input in _input:
                   self.assertTrue(__input in authorized_types					)
           else:
               self.assertTrue(_input in authorized_types					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

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

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


   def 					snake_case				( self					):
       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					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

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

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


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       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
       A       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

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