





from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCAmelCase	:	Optional[int]		     =       logging.get_logger(__name__)

# TODO Update this
UpperCAmelCase	:	Optional[Any]		     =       {
    """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 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[Any]						=			"""esm"""







      def __init__(    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=1_0_2_6		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__="absolute"		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=False		,      lowerCAmelCase__=False		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      **lowerCAmelCase__		,      )   ->						List[Any]:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,      mask_token_id=lowerCAmelCase__		,      **lowerCAmelCase__							)

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







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=super().to_dict()
        if isinstance(self.esmfold_config		,      lowerCAmelCase__							):
          a__	:					List[str]										=self.esmfold_config.to_dict()
        return output







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					str						=			None
      _lowercase				:					bool						=			True
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					bool						=			False
      _lowercase				:					float						=			0

      _lowercase				:					bool						=			True
      _lowercase				:					bool						=			False

      _lowercase				:					int						=			128
      _lowercase				:					"TrunkConfig"						=			None







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        if self.trunk is None:
          a__	:					List[Any]										=TrunkConfig()
        elif isinstance(self.trunk		,      lowerCAmelCase__							):
          a__	:					Dict										=TrunkConfig(**self.trunk							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=asdict(self							)
        a__	:					List[str]										=self.trunk.to_dict()
        return output







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					int						=			48
      _lowercase				:					int						=			1024
      _lowercase				:					int						=			128
      _lowercase				:					int						=			32
      _lowercase				:					int						=			32
      _lowercase				:					int						=			32
      _lowercase				:					float						=			0
      _lowercase				:					float						=			0
      _lowercase				:					bool						=			False
      _lowercase				:					int						=			4
      _lowercase				:					Optional[int]						=			128
      _lowercase				:					"StructureModuleConfig"						=			None







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        if self.structure_module is None:
          a__	:					List[Any]										=StructureModuleConfig()
        elif isinstance(self.structure_module		,      lowerCAmelCase__							):
          a__	:					Dict										=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__	:					int										=self.sequence_state_dim // self.sequence_head_width
        a__	:					Dict										=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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=asdict(self							)
        a__	:					List[str]										=self.structure_module.to_dict()
        return output







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					int						=			384
      _lowercase				:					int						=			128
      _lowercase				:					int						=			16
      _lowercase				:					int						=			128
      _lowercase				:					int						=			12
      _lowercase				:					int						=			4
      _lowercase				:					int						=			8
      _lowercase				:					float						=			0.1
      _lowercase				:					int						=			8
      _lowercase				:					int						=			1
      _lowercase				:					int						=			2
      _lowercase				:					int						=			7
      _lowercase				:					int						=			10
      _lowercase				:					float						=			1E-8
      _lowercase				:					float						=			1E5







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return asdict(self							)
def      _A       (		):





  """simple docstring"""



  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>",
  )











from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowercase: Union[str, Any]      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																raise ValueError(
																    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
																    " please use only one"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)

						if prompt is not None:
											if not isinstance(lowerCamelCase_		,       lowerCamelCase_		):
																raise ValueError(
																    F'''Received an invalid text input, got - {type(lowerCamelCase_		)} - but expected a single string. '''
																    "Note also that one single text can be provided for conditional image to text generation."		)

											a					     =   self.model.config.model_type

											if model_type == "git":
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(text=lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		).input_ids
																a					     =   [self.tokenizer.cls_token_id] + input_ids
																a					     =   torch.tensor(lowerCamelCase_		).unsqueeze(0		)
																model_inputs.update({"input_ids": input_ids}		)

											elif model_type == "pix2struct":
																a					     =   self.image_processor(images=lowerCamelCase_		,       header_text=lowerCamelCase_		,       return_tensors=self.framework		)

											elif model_type != "vision-encoder-decoder":
																# vision-encoder-decoder does not support conditional generation
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(lowerCamelCase_		,       return_tensors=self.framework		)
																model_inputs.update(lowerCamelCase_		)

											else:
																raise ValueError(F'''Model type {model_type} does not support conditional text generation'''		)

						else:
											a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
											a					     =   None

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs["input_ids"]		,       lowerCamelCase_		)
						    and all(x is None for x in model_inputs["input_ids"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   []
						for output_ids in model_outputs:
											a					     =   {
											    "generated_text": self.tokenizer.decode(
											        lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       )
											}
											records.append(lowerCamelCase_		)
						return records
