




'''simple docstring'''


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =				logging.get_logger(__name__)

UpperCamelCase     =				[
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

UpperCamelCase     =				{
    '''esm1b_t33_650M_UR50S''': esm_module.pretrained.esm1b_t33_650M_UR50S,
    '''esm1v_t33_650M_UR90S_1''': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    '''esm1v_t33_650M_UR90S_2''': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    '''esm1v_t33_650M_UR90S_3''': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    '''esm1v_t33_650M_UR90S_4''': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    '''esm1v_t33_650M_UR90S_5''': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    '''esm2_t48_15B_UR50D''': esm_module.pretrained.esm2_t48_15B_UR50D,
    '''esm2_t36_3B_UR50D''': esm_module.pretrained.esm2_t36_3B_UR50D,
    '''esm2_t33_650M_UR50D''': esm_module.pretrained.esm2_t33_650M_UR50D,
    '''esm2_t30_150M_UR50D''': esm_module.pretrained.esm2_t30_150M_UR50D,
    '''esm2_t12_35M_UR50D''': esm_module.pretrained.esm2_t12_35M_UR50D,
    '''esm2_t6_8M_UR50D''': esm_module.pretrained.esm2_t6_8M_UR50D,
    '''esmfold_v1''': esmfold_va,
}

UpperCamelCase     =				list('''ARNDCQEGHILKMFPSTWYV''')

UpperCamelCase     =				restypes + ['''X''']
UpperCamelCase     =				restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']
def 				SCREAMING_SNAKE_CASE(      ) ->   str:
     with TemporaryDirectory() as tempdir:
          A:		List[str]        =		'''\n'''.join(__lowercase  )
          A:		List[str]        =		Path(__lowercase  ) / '''vocab.txt'''
          vocab_file.write_text(__lowercase  )
          A:		List[Any]        =		EsmTokenizer(vocab_file=str(__lowercase  )  )
     A:		str        =		0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   Optional[int]:
     A:		Any        =		our_module.load_state_dict(original_module.state_dict()  )
     if status.missing_keys:
          raise ValueError(F"""Missing keys: {status.missing_keys}"""  )
     if status.unexpected_keys:
          raise ValueError(F"""Unexpected keys: {status.unexpected_keys}"""  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   Tuple:
     if model.startswith('''esmfold'''  ):
          A:		Any        =		MODEL_MAPPING[model]()
     else:
          A    ,						A:		List[Any]        =		MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith('''esmfold'''  ):
          A:		List[Any]        =		esm.esm.embed_dim
          A:		Optional[int]        =		esm.esm.num_layers
          A:		Union[str, Any]        =		esm.esm.attention_heads
          A:		Optional[Any]        =		4 * embed_dim
          A:		Optional[Any]        =		esm.esm.token_dropout
          A:		str        =		False  # This code path does not exist in ESM-2
          A:		Optional[Any]        =		'''rotary'''
          A:		Dict        =		True
          A:		int        =		EsmFoldConfig()
          for key, val in esm.cfg.items():
               if hasattr(__lowercase		,							__lowercase  ) and key != "trunk":
                    setattr(__lowercase		,							__lowercase		,							__lowercase  )
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk		,							__lowercase  ) and key != "structure_module":
                    setattr(esmfold_config.trunk		,							__lowercase		,							__lowercase  )
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module		,							__lowercase  ):
                    setattr(esmfold_config.trunk.structure_module		,							__lowercase		,							__lowercase  )
     elif hasattr(__lowercase		,							'''args'''  ):
          # Indicates an ESM-1b or ESM-1v model
          A:		Union[str, Any]        =		esm.args.embed_dim
          A:		Dict        =		esm.args.layers
          A:		int        =		esm.args.attention_heads
          A:		List[Any]        =		esm.args.ffn_embed_dim
          A:		Optional[int]        =		esm.args.token_dropout
          A:		str        =		True if esm.emb_layer_norm_before else False
          A:		str        =		'''absolute'''
          A:		List[Any]        =		False
          A:		Dict        =		None
     else:
          # Indicates an ESM-2 model
          A:		Any        =		esm.embed_dim
          A:		int        =		esm.num_layers
          A:		Tuple        =		esm.attention_heads
          A:		List[Any]        =		4 * embed_dim  # This is hardcoded in ESM-2
          A:		Tuple        =		esm.token_dropout
          A:		Any        =		False  # This code path does not exist in ESM-2
          A:		Optional[int]        =		'''rotary'''
          A:		int        =		False
          A:		Optional[int]        =		None

     if is_folding_model:
          A:		Any        =		esm.esm.alphabet
     A:		str        =		tuple(alphabet.all_toks  )
     A:		Union[str, Any]        =		alphabet.mask_idx
     A:		List[Any]        =		alphabet.padding_idx

     if is_folding_model:
          A:		Dict        =		esm.esm
     else:
          A:		Union[str, Any]        =		esm

     A:		List[str]        =		EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings		,							mask_token_id=__lowercase		,							hidden_size=__lowercase		,							num_hidden_layers=__lowercase		,							num_attention_heads=__lowercase		,							intermediate_size=__lowercase		,							max_position_embeddings=1_0_2_6		,							layer_norm_eps=1E-5		,							attention_probs_dropout_prob=0.0		,							hidden_dropout_prob=0.0		,							pad_token_id=__lowercase		,							emb_layer_norm_before=__lowercase		,							token_dropout=__lowercase		,							position_embedding_type=__lowercase		,							is_folding_model=__lowercase		,							esmfold_config=__lowercase		,							vocab_list=__lowercase		,							)
     if classification_head:
          A:		List[Any]        =		esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our ESM config:'''		,							__lowercase  )

     if model.startswith('''esmfold'''  ):
          A:		int        =		EsmForProteinFolding
     elif classification_head:
          A:		List[str]        =		EsmForSequenceClassification
     else:
          A:		Optional[Any]        =		EsmForMaskedLM
     A:		str        =		model_class(__lowercase  )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     A:		Optional[int]        =		original_esm_model.embed_tokens.weight
     if position_embedding_type == "absolute":
          A:		Dict        =		original_esm_model.embed_positions.weight

     if config.emb_layer_norm_before:
          A:		Optional[int]        =		original_esm_model.emb_layer_norm_before.weight
          A:		List[str]        =		original_esm_model.emb_layer_norm_before.bias

     A:		Tuple        =		original_esm_model.emb_layer_norm_after.weight
     A:		Dict        =		original_esm_model.emb_layer_norm_after.bias

     for i in range(config.num_hidden_layers  ):
          # Encoder: start of layer
          A:		EsmLayer        =		model.esm.encoder.layer[i]
          # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
          A:		Union[str, Any]        =		original_esm_model.layers[i]

          # self attention
          A:		EsmSelfAttention        =		layer.attention.self
          assert (
              esm_layer.self_attn.k_proj.weight.data.shape
              == esm_layer.self_attn.q_proj.weight.data.shape
              == esm_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)  )
          )

          A:		List[Any]        =		esm_layer.self_attn.q_proj.weight
          A:		int        =		esm_layer.self_attn.q_proj.bias
          A:		Union[str, Any]        =		esm_layer.self_attn.k_proj.weight
          A:		List[Any]        =		esm_layer.self_attn.k_proj.bias
          A:		Any        =		esm_layer.self_attn.v_proj.weight
          A:		Any        =		esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn		,							'''rot_emb'''		,							__lowercase  ) is not None:
               # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
               # During the training of ESM-2 the model was converted to float16 precision, which also converts
               # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
               # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
               # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
               # we make sure the new model copies the data from the old inv_freq.
               A:		Optional[int]        =		esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          A:		Any        =		esm_layer.self_attn_layer_norm.weight
          A:		int        =		esm_layer.self_attn_layer_norm.bias
          A:		int        =		esm_layer.final_layer_norm.weight
          A:		Dict        =		esm_layer.final_layer_norm.bias

          # self-attention output
          A:		EsmSelfOutput        =		layer.attention.output
          assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
          A:		Union[str, Any]        =		esm_layer.self_attn.out_proj.weight
          A:		Dict        =		esm_layer.self_attn.out_proj.bias

          # intermediate
          A:		EsmIntermediate        =		layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          A:		int        =		esm_layer.fca.weight
          A:		Dict        =		esm_layer.fca.bias

          # output
          A:		EsmOutput        =		layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          A:		str        =		esm_layer.fca.weight
          A:		str        =		esm_layer.fca.bias
          # end of layer

     if is_folding_model:
          A:		int        =		esm.esm_s_combine.data
          A:		Any        =		esm.afa_to_esm.data
          transfer_and_check_weights(esm.embedding		,							model.embedding  )
          transfer_and_check_weights(esm.esm_s_mlp		,							model.esm_s_mlp  )
          transfer_and_check_weights(esm.trunk		,							model.trunk  )
          transfer_and_check_weights(esm.distogram_head		,							model.distogram_head  )
          transfer_and_check_weights(esm.ptm_head		,							model.ptm_head  )
          transfer_and_check_weights(esm.lm_head		,							model.lm_head  )
          transfer_and_check_weights(esm.lddt_head		,							model.lddt_head  )

     elif classification_head:
          A:		Dict        =		esm.esm.classification_heads['''mnli'''].dense.weight
          A:		Tuple        =		esm.classification_heads['''mnli'''].dense.bias
          A:		Optional[int]        =		esm.classification_heads['''mnli'''].out_proj.weight
          A:		int        =		esm.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          A:		int        =		esm.lm_head.dense.weight
          A:		Any        =		esm.lm_head.dense.bias
          A:		List[Any]        =		esm.lm_head.layer_norm.weight
          A:		str        =		esm.lm_head.layer_norm.bias
          A:		Dict        =		esm.lm_head.weight
          A:		Tuple        =		esm.lm_head.bias

     # Contact prediction head
     transfer_and_check_weights(esm.contact_head		,							model.esm.contact_head  )

     # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
     if is_folding_model:
          # Folding models aren't trained on masked inputs and don't like mask tokens.
          A:		str        =		SAMPLE_DATA[:2]
     else:
          A:		Union[str, Any]        =		SAMPLE_DATA

     if is_folding_model:
          A:		Union[str, Any]        =		get_esmfold_tokenizer()
          A:		List[str]        =		hf_tokenizer(
              [row[1] for row in sample_data]		,							return_tensors='''pt'''		,							padding=__lowercase		,							add_special_tokens=__lowercase  )
          A    ,						A    ,						A    ,						A    ,						A:		int        =		esmfold_encode_sequences([row[1] for row in sample_data]  )
          A:		Optional[Any]        =		torch.all(hf_tokens['''input_ids'''] == esmfold_aas  ) and torch.all(
              hf_tokens['''attention_mask'''] == esmfold_mask  )
     else:
          # Let's check that we get the same results.
          A:		Union[str, Any]        =		alphabet.get_batch_converter()
          A    ,						A    ,						A:		str        =		batch_converter(__lowercase  )
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               A:		List[Any]        =		'''\n'''.join(alphabet.all_toks  )
               A:		Optional[int]        =		Path(__lowercase  ) / '''vocab.txt'''
               vocab_file.write_text(__lowercase  )
               A:		Any        =		EsmTokenizer(vocab_file=str(__lowercase  )  )

          A:		List[Any]        =		hf_tokenizer([row[1] for row in sample_data]		,							return_tensors='''pt'''		,							padding=__lowercase  )
          A:		str        =		torch.all(hf_tokens['''input_ids'''] == batch_tokens  )

     print('''Do both models tokenizers output the same tokens?'''		,							'''🔥''' if success else '''💩'''  )
     if not success:
          raise Exception('''Tokenization does not match!'''  )

     with torch.no_grad():
          if is_folding_model:
               # Let's test the model in parts
               # ESMFold always converts the ESM stem to float16, which requires float16 ops
               # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
               # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
               # original and the converted model on the GPU at the same time.
               A:		Union[str, Any]        =		esm.cuda().infer([row[1] for row in sample_data]  )
               A:		Tuple        =		model.cuda()(
                   input_ids=hf_tokens['''input_ids'''].cuda()		,							attention_mask=hf_tokens['''attention_mask'''].cuda()  )
          else:
               A:		Any        =		model(**__lowercase		,							output_hidden_states=__lowercase  )
               A:		List[Any]        =		our_output['''logits''']
               if classification_head:
                    A:		Dict        =		esm.model.classification_heads['''mnli'''](esm.extract_features(__lowercase  )  )
               else:
                    A:		Optional[Any]        =		esm(hf_tokens['''input_ids''']		,							repr_layers=list(range(9_9_9  )  )  )
                    A:		Union[str, Any]        =		their_output['''logits''']

          if is_folding_model:
               A:		List[str]        =		torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']  )  ).item()
               A:		Optional[Any]        =		torch.allclose(our_output['''positions''']		,							their_output['''positions''']		,							atol=1E-5  )
          else:
               A:		Dict        =		torch.max(torch.abs(our_output - their_output  )  ).item()
               A:		List[str]        =		torch.allclose(__lowercase		,							__lowercase		,							atol=1E-5  )

          print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-5
          print('''Do both models output the same tensors?'''		,							'''🔥''' if success else '''💩'''  )

          if not success:
               raise Exception('''Something went wRoNg'''  )

          if not is_folding_model:
               # Let's check contact prediction too
               A:		List[str]        =		model.predict_contacts(hf_tokens['''input_ids''']		,							hf_tokens['''attention_mask''']  )
               A:		List[str]        =		esm.predict_contacts(hf_tokens['''input_ids''']  )
               A:		Optional[Any]        =		torch.max(torch.abs(our_output - their_output  )  ).item()
               A:		Tuple        =		torch.allclose(__lowercase		,							__lowercase		,							atol=1E-5  )

               print('''Contact prediction testing:'''  )
               print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-5
               print('''Do both models output the same tensors?'''		,							'''🔥''' if success else '''💩'''  )

               if not success:
                    raise Exception('''Something went wRoNg'''  )

          pathlib.Path(__lowercase  ).mkdir(parents=__lowercase		,							exist_ok=__lowercase  )
          print(F"""Saving model to {pytorch_dump_folder_path}"""  )
          model.save_pretrained(__lowercase  )

          del esm  # Free up some memory before continuing

     print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""  )
     hf_tokenizer.save_pretrained(__lowercase  )

     if push_to_repo:
          model.push_to_hub(repo_id=__lowercase		,							use_auth_token=__lowercase  )
          hf_tokenizer.push_to_hub(repo_id=__lowercase		,							use_auth_token=__lowercase  )


if __name__ == "__main__":
     UpperCamelCase     =				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_dump_folder_path''', type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
     )
     parser.add_argument('''--model''', default=None, type=str, required=True, help='''Name of model to convert.''')
     parser.add_argument('''--push_to_repo''', type=str, help='''Repo to upload to (including username!).''')
     parser.add_argument('''--auth_token''', type=str, help='''HuggingFace auth token.''')
     UpperCamelCase     =				parser.parse_args()
     convert_esm_checkpoint_to_pytorch(
         args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
     )












'''simple docstring'''


from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


UpperCamelCase     =				logging.get_logger(__name__)






class 	lowerCAmelCase_  (					nn.Module       ):







  '''simple docstring'''

  UpperCamelCase_ :			HybridCLIPConfig
  UpperCamelCase_ :			jnp.dtype        =							jnp.floataa



  def 				_snake_case    (   self							:       List[str]  ) ->		int:







       '''simple docstring'''



       A:		List[Any]        =		self.config.text_config
       A:		Any        =		self.config.vision_config

       A:		Union[str, Any]        =		self.config.projection_dim
       A:		List[str]        =		text_config.hidden_size
       A:		List[str]        =		vision_config.hidden_size

       A:		List[Any]        =		FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
       A:		int        =		FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__				,     SCREAMING_SNAKE_CASE_  ).module_class

       A:		str        =		text_module(SCREAMING_SNAKE_CASE_				,     dtype=self.dtype  )
       A:		Union[str, Any]        =		vision_module(SCREAMING_SNAKE_CASE_				,     dtype=self.dtype  )

       A:		Any        =		nn.Dense(
           self.projection_dim				,     dtype=self.dtype				,     kernel_init=jax.nn.initializers.normal(0.02  )				,     use_bias=SCREAMING_SNAKE_CASE_				,     )
       A:		Tuple        =		nn.Dense(
           self.projection_dim				,     dtype=self.dtype				,     kernel_init=jax.nn.initializers.normal(0.02  )				,     use_bias=SCREAMING_SNAKE_CASE_				,     )
       A:		Dict        =		self.param('''logit_scale'''				,     jax.nn.initializers.ones				,     []  )




  def __call__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict=None				,     SCREAMING_SNAKE_CASE_							:       List[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Dict=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       bool = True				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		return_dict if return_dict is not None else self.config.return_dict

       A:		List[str]        =		self.vision_model(
           pixel_values=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_				,     output_attentions=SCREAMING_SNAKE_CASE_				,     output_hidden_states=SCREAMING_SNAKE_CASE_				,     return_dict=SCREAMING_SNAKE_CASE_				,     )

       A:		Tuple        =		self.text_model(
           input_ids=SCREAMING_SNAKE_CASE_				,     attention_mask=SCREAMING_SNAKE_CASE_				,     token_type_ids=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_				,     output_attentions=SCREAMING_SNAKE_CASE_				,     output_hidden_states=SCREAMING_SNAKE_CASE_				,     return_dict=SCREAMING_SNAKE_CASE_				,     )

       A:		str        =		vision_outputs[1]
       A:		Any        =		self.visual_projection(SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		text_outputs[1]
       A:		str        =		self.text_projection(SCREAMING_SNAKE_CASE_  )

       # normalized features
       A:		Tuple        =		image_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE_				,     axis=-1				,     keepdims=SCREAMING_SNAKE_CASE_  )
       A:		Tuple        =		text_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE_				,     axis=-1				,     keepdims=SCREAMING_SNAKE_CASE_  )

       # cosine similarity as logits
       A:		Optional[int]        =		jnp.exp(self.logit_scale  )
       A:		Union[str, Any]        =		jnp.matmul(SCREAMING_SNAKE_CASE_				,     image_embeds.T  ) * logit_scale
       A:		Tuple        =		logits_per_text.T

       if not return_dict:
            return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

       return FlaxCLIPOutput(
           logits_per_image=SCREAMING_SNAKE_CASE_				,     logits_per_text=SCREAMING_SNAKE_CASE_				,     text_embeds=SCREAMING_SNAKE_CASE_				,     image_embeds=SCREAMING_SNAKE_CASE_				,     text_model_output=SCREAMING_SNAKE_CASE_				,     vision_model_output=SCREAMING_SNAKE_CASE_				,     )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  UpperCamelCase_ :			Union[str, Any]        =							HybridCLIPConfig
  UpperCamelCase_ :			List[str]        =							FlaxHybridCLIPModule



  def __init__(   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       HybridCLIPConfig				,     SCREAMING_SNAKE_CASE_							:       Optional[Tuple] = None				,     SCREAMING_SNAKE_CASE_							:       int = 0				,     SCREAMING_SNAKE_CASE_							:       jnp.dtype = jnp.floataa				,     **SCREAMING_SNAKE_CASE_							:       str				,     ) ->		int:







       '''simple docstring'''



       if input_shape is None:
            A:		List[Any]        =		((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

       A:		int        =		self.module_class(config=SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       super().__init__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     input_shape=SCREAMING_SNAKE_CASE_				,     seed=SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       FrozenDict = None  ) ->		FrozenDict:







       '''simple docstring'''



       A:		List[Any]        =		jnp.zeros(input_shape[0]				,     dtype='''i4'''  )
       A:		List[str]        =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE_  ).shape[-1]  )				,     input_shape[0]  )
       A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )
       A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       A:		Any        =		jax.random.normal(SCREAMING_SNAKE_CASE_				,     input_shape[1]  )

       A    ,						A:		int        =		jax.random.split(SCREAMING_SNAKE_CASE_  )
       A:		Dict        =		{'''params''': params_rng, '''dropout''': dropout_rng}

       return self.module.init(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )["params"]



  def __call__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       int=None				,     SCREAMING_SNAKE_CASE_							:       Any=None				,     SCREAMING_SNAKE_CASE_							:       List[str]=None				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       bool = False				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     ) ->		str:







       '''simple docstring'''



       A:		List[str]        =		output_attentions if output_attentions is not None else self.config.output_attentions
       A:		int        =		(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       A:		Dict        =		return_dict if return_dict is not None else self.config.return_dict

       if position_ids is None:
            A:		Any        =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE_  ).shape[-1]  )				,     input_ids.shape  )

       if token_type_ids is None:
            A:		Tuple        =		jnp.zeros_like(SCREAMING_SNAKE_CASE_  )

       if attention_mask is None:
            A:		Any        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       # Handle any PRNG if needed
       A:		Union[str, Any]        =		{}
       if dropout_rng is not None:
            A:		Any        =		dropout_rng

       return self.module.apply(
           {'''params''': params or self.params}				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype=jnp.floataa  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     not train				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     rngs=SCREAMING_SNAKE_CASE_				,     )



  def 				_snake_case    (   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       str=None				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=None				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=False				,     ) ->		Optional[Any]:







       '''simple docstring'''



       if position_ids is None:
            A:		Any        =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE_  ).shape[-1]  )				,     input_ids.shape  )

       if token_type_ids is None:
            A:		List[str]        =		jnp.zeros_like(SCREAMING_SNAKE_CASE_  )

       if attention_mask is None:
            A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       # Handle any PRNG if needed
       A:		Any        =		{}
       if dropout_rng is not None:
            A:		Tuple        =		dropout_rng

       def _get_features(SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Tuple  ):
            A:		Tuple        =		module.text_model(
                input_ids=SCREAMING_SNAKE_CASE_				,     attention_mask=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     token_type_ids=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_				,     )
            A:		str        =		text_outputs[1]
            A:		int        =		module.text_projection(SCREAMING_SNAKE_CASE_  )
            return text_features

       return self.module.apply(
           {'''params''': params or self.params}				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype='''i4'''  )				,     not train				,     method=_get_features				,     rngs=SCREAMING_SNAKE_CASE_				,     )



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       List[str]=False  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		Union[str, Any]        =		{}
       if dropout_rng is not None:
            A:		Any        =		dropout_rng

       def _get_features(SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Any  ):
            A:		List[str]        =		module.vision_model(pixel_values=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_  )
            A:		Union[str, Any]        =		vision_outputs[1]  # pooled_output
            A:		Any        =		module.visual_projection(SCREAMING_SNAKE_CASE_  )
            return image_features

       return self.module.apply(
           {'''params''': params or self.params}				,     jnp.array(SCREAMING_SNAKE_CASE_				,     dtype=jnp.floataa  )				,     not train				,     method=_get_features				,     rngs=SCREAMING_SNAKE_CASE_				,     )




  @classmethod
  def 				_snake_case    (   cls							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       str = None				,     SCREAMING_SNAKE_CASE_							:       str = None				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       List[str]				,     ) ->		FlaxPreTrainedModel:







       '''simple docstring'''



       A:		List[Any]        =		{
           argument[len('''text_'''  ) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''  )
       }

       A:		str        =		{
           argument[len('''vision_'''  ) :]: value for argument, value in kwargs.items() if argument.startswith('''vision_'''  )
       }

       # remove text, vision kwargs from kwargs
       for key in kwargs_text.keys():
            del kwargs["text_" + key]
       for key in kwargs_vision.keys():
            del kwargs["vision_" + key]

       # Load and initialize the text and vision model
       A:		Union[str, Any]        =		kwargs_text.pop('''model'''				,     SCREAMING_SNAKE_CASE_  )
       if text_model is None:
            assert (
                text_model_name_or_path is not None
            ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
            from transformers import FlaxAutoModel

            if "config" not in kwargs_text:
                 from transformers import AutoConfig

                 A:		List[str]        =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
                 A:		Optional[int]        =		text_config

            A:		Optional[int]        =		FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		kwargs_vision.pop('''model'''				,     SCREAMING_SNAKE_CASE_  )
       if vision_model is None:
            assert (
                vision_model_name_or_path is not None
            ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
            from transformers import FlaxAutoModel

            if "config" not in kwargs_vision:
                 from transformers import AutoConfig

                 A:		Any        =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
                 A:		Dict        =		vision_config

            A:		Union[str, Any]        =		FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       # instantiate config with corresponding kwargs
       A:		Dict        =		kwargs.pop('''dtype'''				,     jnp.floataa  )
       A:		Any        =		HybridCLIPConfig.from_text_vision_configs(text_model.config				,     vision_model.config				,     **SCREAMING_SNAKE_CASE_  )

       # init model
       A:		List[str]        =		cls(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       if vision_config.model_type == "clip":
            A:		List[Any]        =		vision_model.params['''vision_model''']
            A:		int        =		vision_model.params['''visual_projection''']['''kernel''']
       else:
            A:		Tuple        =		vision_model.params

       A:		Tuple        =		text_model.params

       return model






