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					(  ):
       with TemporaryDirectory() as tempdir:
              _UpperCamelCase  :							Union[str, Any]	    =	'\n'.join(lowercase_					)
              _UpperCamelCase  :							List[str]	    =	Path(lowercase_					) / 'vocab.txt'
              vocab_file.write_text(lowercase_					)
              _UpperCamelCase  :							List[Any]	    =	EsmTokenizer(vocab_file=str(lowercase_					)					)
       _UpperCamelCase  :							Any	    =	0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Dict					):
       _UpperCamelCase  :							str	    =	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_:						str		,    lowercase_:						str		,    lowercase_:						bool		,    lowercase_:						str		,    lowercase_:						str					):
       if model.startswith('esmfold'					):
              _UpperCamelCase  :							Dict	    =	MODEL_MAPPING[model]()
       else:
              _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

       if model.startswith('esmfold'					):
              _UpperCamelCase  :							Optional[int]	    =	esm.esm.embed_dim
              _UpperCamelCase  :							int	    =	esm.esm.num_layers
              _UpperCamelCase  :							List[str]	    =	esm.esm.attention_heads
              _UpperCamelCase  :							Any	    =	4 * embed_dim
              _UpperCamelCase  :							Union[str, Any]	    =	esm.esm.token_dropout
              _UpperCamelCase  :							List[str]	    =	False  # This code path does not exist in ESM-2
              _UpperCamelCase  :							Optional[Any]	    =	'rotary'
              _UpperCamelCase  :							Optional[int]	    =	True
              _UpperCamelCase  :							Optional[Any]	    =	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
              _UpperCamelCase  :							List[str]	    =	esm.args.embed_dim
              _UpperCamelCase  :							Union[str, Any]	    =	esm.args.layers
              _UpperCamelCase  :							List[Any]	    =	esm.args.attention_heads
              _UpperCamelCase  :							str	    =	esm.args.ffn_embed_dim
              _UpperCamelCase  :							int	    =	esm.args.token_dropout
              _UpperCamelCase  :							int	    =	True if esm.emb_layer_norm_before else False
              _UpperCamelCase  :							Dict	    =	'absolute'
              _UpperCamelCase  :							List[str]	    =	False
              _UpperCamelCase  :							Optional[int]	    =	None
       else:
              # Indicates an ESM-2 model
              _UpperCamelCase  :							Optional[Any]	    =	esm.embed_dim
              _UpperCamelCase  :							str	    =	esm.num_layers
              _UpperCamelCase  :							List[Any]	    =	esm.attention_heads
              _UpperCamelCase  :							Optional[int]	    =	4 * embed_dim  # This is hardcoded in ESM-2
              _UpperCamelCase  :							Any	    =	esm.token_dropout
              _UpperCamelCase  :							Union[str, Any]	    =	False  # This code path does not exist in ESM-2
              _UpperCamelCase  :							List[str]	    =	'rotary'
              _UpperCamelCase  :							Tuple	    =	False
              _UpperCamelCase  :							Optional[int]	    =	None

       if is_folding_model:
              _UpperCamelCase  :							Optional[Any]	    =	esm.esm.alphabet
       _UpperCamelCase  :							int	    =	tuple(alphabet.all_toks					)
       _UpperCamelCase  :							int	    =	alphabet.mask_idx
       _UpperCamelCase  :							int	    =	alphabet.padding_idx

       if is_folding_model:
              _UpperCamelCase  :							Union[str, Any]	    =	esm.esm
       else:
              _UpperCamelCase  :							Optional[int]	    =	esm

       _UpperCamelCase  :							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=1026		,    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:
              _UpperCamelCase  :							Tuple	    =	esm.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our ESM config:'		,    lowercase_					)

       if model.startswith('esmfold'					):
              _UpperCamelCase  :							Union[str, Any]	    =	EsmForProteinFolding
       elif classification_head:
              _UpperCamelCase  :							Optional[Any]	    =	EsmForSequenceClassification
       else:
              _UpperCamelCase  :							int	    =	EsmForMaskedLM
       _UpperCamelCase  :							Dict	    =	model_class(lowercase_					)
       model.eval()

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

       if config.emb_layer_norm_before:
              _UpperCamelCase  :							List[str]	    =	original_esm_model.emb_layer_norm_before.weight
              _UpperCamelCase  :							List[str]	    =	original_esm_model.emb_layer_norm_before.bias

       _UpperCamelCase  :							str	    =	original_esm_model.emb_layer_norm_after.weight
       _UpperCamelCase  :							List[str]	    =	original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              _UpperCamelCase  :							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)					)
              )

              _UpperCamelCase  :							List[Any]	    =	esm_layer.self_attn.q_proj.weight
              _UpperCamelCase  :							List[Any]	    =	esm_layer.self_attn.q_proj.bias
              _UpperCamelCase  :							int	    =	esm_layer.self_attn.k_proj.weight
              _UpperCamelCase  :							Optional[int]	    =	esm_layer.self_attn.k_proj.bias
              _UpperCamelCase  :							Union[str, Any]	    =	esm_layer.self_attn.v_proj.weight
              _UpperCamelCase  :							Tuple	    =	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.
                     _UpperCamelCase  :							Dict	    =	esm_layer.self_attn.rot_emb.inv_freq

              # LayerNorm changes for pre-activation
              _UpperCamelCase  :							Union[str, Any]	    =	esm_layer.self_attn_layer_norm.weight
              _UpperCamelCase  :							Optional[int]	    =	esm_layer.self_attn_layer_norm.bias
              _UpperCamelCase  :							Tuple	    =	esm_layer.final_layer_norm.weight
              _UpperCamelCase  :							Dict	    =	esm_layer.final_layer_norm.bias

              # self-attention output
              _UpperCamelCase  :							EsmSelfOutput	    =	layer.attention.output
              assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
              _UpperCamelCase  :							List[Any]	    =	esm_layer.self_attn.out_proj.weight
              _UpperCamelCase  :							Optional[Any]	    =	esm_layer.self_attn.out_proj.bias

              # intermediate
              _UpperCamelCase  :							EsmIntermediate	    =	layer.intermediate
              assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
              _UpperCamelCase  :							Optional[Any]	    =	esm_layer.fca.weight
              _UpperCamelCase  :							str	    =	esm_layer.fca.bias

              # output
              _UpperCamelCase  :							EsmOutput	    =	layer.output
              assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
              _UpperCamelCase  :							List[Any]	    =	esm_layer.fca.weight
              _UpperCamelCase  :							int	    =	esm_layer.fca.bias
              # end of layer

       if is_folding_model:
              _UpperCamelCase  :							List[Any]	    =	esm.esm_s_combine.data
              _UpperCamelCase  :							Union[str, 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:
              _UpperCamelCase  :							Optional[int]	    =	esm.esm.classification_heads['mnli'].dense.weight
              _UpperCamelCase  :							str	    =	esm.classification_heads['mnli'].dense.bias
              _UpperCamelCase  :							Optional[int]	    =	esm.classification_heads['mnli'].out_proj.weight
              _UpperCamelCase  :							Tuple	    =	esm.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _UpperCamelCase  :							Union[str, Any]	    =	esm.lm_head.dense.weight
              _UpperCamelCase  :							Any	    =	esm.lm_head.dense.bias
              _UpperCamelCase  :							Any	    =	esm.lm_head.layer_norm.weight
              _UpperCamelCase  :							Dict	    =	esm.lm_head.layer_norm.bias
              _UpperCamelCase  :							Any	    =	esm.lm_head.weight
              _UpperCamelCase  :							Optional[int]	    =	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.
              _UpperCamelCase  :							int	    =	SAMPLE_DATA[:2]
       else:
              _UpperCamelCase  :							List[Any]	    =	SAMPLE_DATA

       if is_folding_model:
              _UpperCamelCase  :							Optional[int]	    =	get_esmfold_tokenizer()
              _UpperCamelCase  :							Any	    =	hf_tokenizer(
                  [row[1] for row in sample_data]		,    return_tensors='pt'		,    padding=lowercase_		,    add_special_tokens=lowercase_					)
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	esmfold_encode_sequences([row[1] for row in sample_data]					)
              _UpperCamelCase  :							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.
              _UpperCamelCase  :							List[Any]	    =	alphabet.get_batch_converter()
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	batch_converter(lowercase_					)
              # Prepare tokenizer and make sure it matches
              with TemporaryDirectory() as tempdir:
                     _UpperCamelCase  :							List[str]	    =	'\n'.join(alphabet.all_toks					)
                     _UpperCamelCase  :							Optional[int]	    =	Path(lowercase_					) / 'vocab.txt'
                     vocab_file.write_text(lowercase_					)
                     _UpperCamelCase  :							Tuple	    =	EsmTokenizer(vocab_file=str(lowercase_					)					)

              _UpperCamelCase  :							Any	    =	hf_tokenizer([row[1] for row in sample_data]		,    return_tensors='pt'		,    padding=lowercase_					)
              _UpperCamelCase  :							List[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.
                     _UpperCamelCase  :							Tuple	    =	esm.cuda().infer([row[1] for row in sample_data]					)
                     _UpperCamelCase  :							List[Any]	    =	model.cuda()(
                         input_ids=hf_tokens['input_ids'].cuda()		,    attention_mask=hf_tokens['attention_mask'].cuda()					)
              else:
                     _UpperCamelCase  :							Tuple	    =	model(**lowercase_		,    output_hidden_states=lowercase_					)
                     _UpperCamelCase  :							str	    =	our_output['logits']
                     if classification_head:
                            _UpperCamelCase  :							Optional[int]	    =	esm.model.classification_heads['mnli'](esm.extract_features(lowercase_					)					)
                     else:
                            _UpperCamelCase  :							Any	    =	esm(hf_tokens['input_ids']		,    repr_layers=list(range(999					)					)					)
                            _UpperCamelCase  :							int	    =	their_output['logits']

              if is_folding_model:
                     _UpperCamelCase  :							Union[str, Any]	    =	torch.max(torch.abs(our_output['positions'] - their_output['positions']					)					).item()
                     _UpperCamelCase  :							List[Any]	    =	torch.allclose(our_output['positions']		,    their_output['positions']		,    atol=1e-5					)
              else:
                     _UpperCamelCase  :							Any	    =	torch.max(torch.abs(our_output - their_output					)					).item()
                     _UpperCamelCase  :							List[Any]	    =	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
                     _UpperCamelCase  :							Union[str, Any]	    =	model.predict_contacts(hf_tokens['input_ids']		,    hf_tokens['attention_mask']					)
                     _UpperCamelCase  :							List[str]	    =	esm.predict_contacts(hf_tokens['input_ids']					)
                     _UpperCamelCase  :							Union[str, Any]	    =	torch.max(torch.abs(our_output - their_output					)					).item()
                     _UpperCamelCase  :							str	    =	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
 )








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__A       =False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,)
@require_vision
@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Any						=      PixaStructImageProcessor()
					__UpperCAmelCase       :	Any						=      TaTokenizer.from_pretrained('''t5-small'''					)

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(a_			,			a_					)

					processor.save_pretrained(self.tmpdirname					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).tokenizer




			def 					snake_case__	(  self				:					int			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).image_processor




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      [np.random.randint(2_55			,			size=(3, 30, 4_00)			,			dtype=np.uinta					)]

					__UpperCAmelCase       :	Any						=      [Image.fromarray(np.moveaxis(a_			,			0			,			-1					)					) for x in image_inputs]

					return image_inputs




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	Any						=      self.get_image_processor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	Dict						=      PixaStructProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.image_processor.to_json_string()			,			image_processor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.image_processor			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	List[Any]						=      image_processor(a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	Union[str, Any]						=      processor(images=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''

					__UpperCAmelCase       :	Any						=      processor(text=a_					)

					__UpperCAmelCase       :	Dict						=      tokenizer(a_			,			return_token_type_ids=a_			,			add_special_tokens=a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	List[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_					)

					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					# test if it raises when no input is passed
					with pytest.raises(a_					):
							processor()




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	int						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	int						=      processor(text=a_			,			images=a_					)

					__UpperCAmelCase       :	Tuple						=      [5_12, 10_24, 20_48, 40_96]
					__UpperCAmelCase       :	Tuple						=      [7_70, 7_70, 7_70, 7_70]
					# with text
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)

					# without text input
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Any						=      processor(images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	List[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      self.get_image_processor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Dict						=      '''lower newer'''
					__UpperCAmelCase       :	List[str]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Optional[Any]						=      processor(text=a_			,			images=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					__UpperCAmelCase       :	Optional[int]						=      processor(text=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(list(inputs.keys()					)			,			['''input_ids''', '''attention_mask''']					)





