


'''simple docstring'''






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor




class 						a__							(				a__    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.config_class(**self.inputs_dict  )
														self.parent.assertTrue(hasattr(snake_case__   ,       '''hidden_sizes'''  )  )
														self.parent.assertTrue(hasattr(snake_case__   ,       '''num_attention_heads'''  )  )
														self.parent.assertTrue(hasattr(snake_case__   ,       '''num_encoder_blocks'''  )  )




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=64   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=[2, 2, 2, 2]   ,       lowerCamelCase_=[8, 4, 2, 1]   ,       lowerCamelCase_=[16, 32, 64, 1_28]   ,       lowerCamelCase_=[1, 4, 8, 16]   ,       lowerCamelCase_=[1, 2, 4, 8]   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=None   ,       )			->				List[str]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      num_encoder_blocks
														lowerCAmelCase__			  =      sr_ratios
														lowerCAmelCase__			  =      depths
														lowerCAmelCase__			  =      hidden_sizes
														lowerCAmelCase__			  =      downsampling_rates
														lowerCAmelCase__			  =      num_attention_heads
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      attention_probs_dropout_prob
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      num_labels
														lowerCAmelCase__			  =      scope







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels  )

														lowerCAmelCase__			  =      self.get_config()
														return config, pixel_values, labels







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														return SegformerConfig(
														    image_size=self.image_size   ,       num_channels=self.num_channels   ,       num_encoder_blocks=self.num_encoder_blocks   ,       depths=self.depths   ,       hidden_sizes=self.hidden_sizes   ,       num_attention_heads=self.num_attention_heads   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       initializer_range=self.initializer_range   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      SegformerModel(config=snake_case__  )
														model.to(snake_case__  )
														model.eval()
														lowerCAmelCase__			  =      model(snake_case__  )
														lowerCAmelCase__			  =      self.image_size // (self.downsampling_rates[-1] * 2)
														self.parent.assertEqual(
														    result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Any:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      SegformerForSemanticSegmentation(snake_case__  )
														model.to(snake_case__  )
														model.eval()
														lowerCAmelCase__			  =      model(snake_case__  )
														self.parent.assertEqual(
														    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)  )
														lowerCAmelCase__			  =      model(snake_case__   ,       labels=snake_case__  )
														self.parent.assertEqual(
														    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)  )
														self.parent.assertGreater(result.loss   ,       0.0  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Any:
														lowerCAmelCase__			  =      1
														lowerCAmelCase__			  =      SegformerForSemanticSegmentation(config=snake_case__  )
														model.to(snake_case__  )
														model.eval()
														lowerCAmelCase__			  =      torch.randint(0   ,       1   ,       (self.batch_size, self.image_size, self.image_size)  ).to(snake_case__  )
														lowerCAmelCase__			  =      model(snake_case__   ,       labels=snake_case__  )
														self.parent.assertGreater(result.loss   ,       0.0  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__			  =      config_and_inputs
														lowerCAmelCase__			  =      {"pixel_values": pixel_values}
														return config, inputs_dict




@require_torch
class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  int      		= (
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__     :  Any      		= (
							    {
							        "feature-extraction": SegformerModel,
							        "image-classification": SegformerForImageClassification,
							        "image-segmentation": SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase__     :  int      		= True
							lowercase__     :  List[Any]      		= False
							lowercase__     :  str      		= False
							lowercase__     :  List[Any]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      SegformerModelTester(self  )
														lowerCAmelCase__			  =      SegformerConfigTester(self   ,       config_class=snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														self.config_tester.run_common_tests()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_segmentation(*snake_case__  )







							@unittest.skip('''SegFormer does not use inputs_embeds'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														pass







							@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					lowerCAmelCase__			  =      inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCAmelCase__			  =      [*signature.parameters.keys()]

																					lowerCAmelCase__			  =      ["pixel_values"]
																					self.assertListEqual(arg_names[:1]   ,       snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__			  =      True

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      True
																					lowerCAmelCase__			  =      False
																					lowerCAmelCase__			  =      True
																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					model.to(snake_case__  )
																					model.eval()
																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**self._prepare_for_class(snake_case__   ,       snake_case__  )  )
																					lowerCAmelCase__			  =      outputs.attentions

																					lowerCAmelCase__			  =      sum(self.model_tester.depths  )
																					self.assertEqual(len(snake_case__  )   ,       snake_case__  )

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					lowerCAmelCase__			  =      True
																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					model.to(snake_case__  )
																					model.eval()
																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**self._prepare_for_class(snake_case__   ,       snake_case__  )  )
																					lowerCAmelCase__			  =      outputs.attentions

																					self.assertEqual(len(snake_case__  )   ,       snake_case__  )

																					# verify the first attentions (first block, first layer)
																					lowerCAmelCase__			  =      (self.model_tester.image_size // 4) ** 2
																					lowerCAmelCase__			  =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																					self.assertListEqual(
																					    list(attentions[0].shape[-3:]  )   ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,       )

																					# verify the last attentions (last block, last layer)
																					lowerCAmelCase__			  =      (self.model_tester.image_size // 32) ** 2
																					lowerCAmelCase__			  =      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																					self.assertListEqual(
																					    list(attentions[-1].shape[-3:]  )   ,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,       )
																					lowerCAmelCase__			  =      len(snake_case__  )

																					# Check attention is always last and order is fine
																					lowerCAmelCase__			  =      True
																					lowerCAmelCase__			  =      True
																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					model.to(snake_case__  )
																					model.eval()
																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**self._prepare_for_class(snake_case__   ,       snake_case__  )  )

																					self.assertEqual(out_len + 1   ,       len(snake_case__  )  )

																					lowerCAmelCase__			  =      outputs.attentions

																					self.assertEqual(len(snake_case__  )   ,       snake_case__  )
																					# verify the first attentions (first block, first layer)
																					lowerCAmelCase__			  =      (self.model_tester.image_size // 4) ** 2
																					lowerCAmelCase__			  =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																					self.assertListEqual(
																					    list(self_attentions[0].shape[-3:]  )   ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														def check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					model.to(snake_case__  )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**self._prepare_for_class(snake_case__   ,       snake_case__  )  )

																					lowerCAmelCase__			  =      outputs.hidden_states

																					lowerCAmelCase__			  =      self.model_tester.num_encoder_blocks
																					self.assertEqual(len(snake_case__  )   ,       snake_case__  )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]  )   ,       [
																					        self.model_tester.hidden_sizes[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]   ,       )

														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      True
																					check_hidden_states_output(snake_case__   ,       snake_case__   ,       snake_case__  )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCAmelCase__			  =      True

																					check_hidden_states_output(snake_case__   ,       snake_case__   ,       snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														if not self.model_tester.is_training:
																					return

														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__			  =      True

														for model_class in self.all_model_classes:
																					if model_class in get_values(snake_case__  ):
																												continue

																					lowerCAmelCase__			  =      model_class(snake_case__  )
																					model.to(snake_case__  )
																					model.train()
																					lowerCAmelCase__			  =      self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__  )
																					lowerCAmelCase__			  =      model(**snake_case__  ).loss
																					loss.backward()







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														pass







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      SegformerModel.from_pretrained(snake_case__  )
																					self.assertIsNotNone(snake_case__  )


def    _snake_case  (	)		->       str:
							lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
							return image






@require_torch
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      SegformerImageProcessor(
														    image_scale=(5_12, 5_12)   ,       keep_ratio=snake_case__   ,       align=snake_case__   ,       do_random_crop=snake_case__  )
														lowerCAmelCase__			  =      SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''  ).to(
														    snake_case__  )

														lowerCAmelCase__			  =      prepare_img()
														lowerCAmelCase__			  =      image_processor(images=snake_case__   ,       return_tensors='''pt'''  )
														lowerCAmelCase__			  =      encoded_inputs.pixel_values.to(snake_case__  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(snake_case__  )

														lowerCAmelCase__			  =      torch.Size((1, model.config.num_labels, 1_28, 1_28)  )
														self.assertEqual(outputs.logits.shape   ,       snake_case__  )

														lowerCAmelCase__			  =      torch.tensor(
														    [
														        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
														        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
														        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
														    ]  ).to(snake_case__  )
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,       snake_case__   ,       atol=1e-4  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      SegformerImageProcessor(
														    image_scale=(5_12, 5_12)   ,       keep_ratio=snake_case__   ,       align=snake_case__   ,       do_random_crop=snake_case__  )
														lowerCAmelCase__			  =      SegformerForSemanticSegmentation.from_pretrained(
														    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''  ).to(snake_case__  )

														lowerCAmelCase__			  =      prepare_img()
														lowerCAmelCase__			  =      image_processor(images=snake_case__   ,       return_tensors='''pt'''  )
														lowerCAmelCase__			  =      encoded_inputs.pixel_values.to(snake_case__  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(snake_case__  )

														lowerCAmelCase__			  =      torch.Size((1, model.config.num_labels, 1_28, 1_28)  )
														self.assertEqual(outputs.logits.shape   ,       snake_case__  )

														lowerCAmelCase__			  =      torch.tensor(
														    [
														        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
														        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
														        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
														    ]  ).to(snake_case__  )
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,       snake_case__   ,       atol=1e-1  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      SegformerImageProcessor(
														    image_scale=(5_12, 5_12)   ,       keep_ratio=snake_case__   ,       align=snake_case__   ,       do_random_crop=snake_case__  )
														lowerCAmelCase__			  =      SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''  ).to(
														    snake_case__  )

														lowerCAmelCase__			  =      prepare_img()
														lowerCAmelCase__			  =      image_processor(images=snake_case__   ,       return_tensors='''pt'''  )
														lowerCAmelCase__			  =      encoded_inputs.pixel_values.to(snake_case__  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(snake_case__  )

														lowerCAmelCase__			  =      outputs.logits.detach().cpu()

														lowerCAmelCase__			  =      image_processor.post_process_semantic_segmentation(outputs=snake_case__   ,       target_sizes=[(5_00, 3_00)]  )
														lowerCAmelCase__			  =      torch.Size((5_00, 3_00)  )
														self.assertEqual(segmentation[0].shape   ,       snake_case__  )

														lowerCAmelCase__			  =      image_processor.post_process_semantic_segmentation(outputs=snake_case__  )
														lowerCAmelCase__			  =      torch.Size((1_28, 1_28)  )
														self.assertEqual(segmentation[0].shape   ,       snake_case__  )







"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCAmelCase	:	Optional[int]				=      2




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			*,  # begin keyword-only arguments
       snake_case__      :	List[str]="<s>"							,			snake_case__      :	str="<pad>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Any="<unk>"							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if idx < len(self.symbols							):
             return self.symbols[idx]
        return self.unk_word
   def __len__(		self      :	int							):


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        if isinstance(snake_case__							,			snake_case__							):
             try:
                  with open(snake_case__							,			"r"							,			encoding="utf-8"							) as fd:
                       self.add_from_file(snake_case__							)
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(snake_case__							)							)
             return

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

        for line in lines[indices_start_line:]:
             try:
                  UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rstrip().rsplit(" "							,			1							)
                  if field == "#fairseq:overwrite":
                       UpperCAmelCase__						:    Union[str, Any]							=							True
                       UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rsplit(" "							,			1							)
                  else:
                       UpperCAmelCase__						:    Tuple							=							False
                  UpperCAmelCase__						:    Any							=							int(snake_case__							)
                  UpperCAmelCase__						:    List[Any]							=							line
                  if word in self and not overwrite:
                       raise RuntimeError(
                           "Duplicate word found when loading Dictionary: '{}'. "
                           "Duplicate words can overwrite earlier ones by adding the "
                           "#fairseq:overwrite flag at the end of the corresponding row "
                           "in the dictionary file. If using the Camembert model, please "
                           "download an updated copy of the model file.".format(snake_case__							)							)
                  self.add_symbol(snake_case__							,			n=snake_case__							,			overwrite=snake_case__							)
             except ValueError:
                  raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							dict((re.sub(r"@@$"			,					""			,					snake_case				), v) if k.endswith("@@"				) else (re.sub(r"$"			,					"</w>"			,					snake_case				), v) for k, v in d.items()				)
     UpperCAmelCase__						:    Optional[int]							=							"<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          UpperCAmelCase__						:    List[str]							=							d[k]  # restore
     return da


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if not os.path.exists(snake_case				):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
     os.makedirs(snake_case			,					exist_ok=snake_case				)
     print(f'Writing results to {pytorch_dump_folder_path}'				)

     # handle various types of models

     UpperCAmelCase__						:    Dict							=							os.path.join(snake_case			,					"checkpoint.pt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {checkpoint_file} does not exist!'				)
     UpperCAmelCase__						:    Optional[int]							=							torch.load(snake_case			,					map_location="cpu"				)

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

     # dicts
     UpperCAmelCase__						:    Optional[int]							=							os.path.join(snake_case			,					"dict.txt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {dict_file} does not exist!'				)
     UpperCAmelCase__						:    Dict							=							Dictionary.load(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							rewrite_dict_keys(src_dict.indices				)
     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["vocab_file"]				)
     print(f'Generating {src_vocab_file} of {src_vocab_size} records'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # merges_file (bpecodes)
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"bpecodes"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

     UpperCAmelCase__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    Any							=							{
         "activation_dropout": args["activation_dropout"],
         "architectures": ["BioGptForCausalLM"],
         "attention_probs_dropout_prob": args["attention_dropout"],
         "bos_token_id": 0,
         "eos_token_id": 2,
         "hidden_act": args["activation_fn"],
         "hidden_dropout_prob": args["dropout"],
         "hidden_size": args["decoder_embed_dim"],
         "initializer_range": 0.02,
         "intermediate_size": args["decoder_ffn_embed_dim"],
         "layer_norm_eps": 1E-1_2,
         "layerdrop": args["decoder_layerdrop"],
         "max_position_embeddings": args["max_target_positions"],
         "model_type": "biogpt",
         "num_attention_heads": args["decoder_attention_heads"],
         "num_hidden_layers": args["decoder_layers"],
         "pad_token_id": 1,
         "scale_embedding": not args["no_scale_embedding"],
         "tie_word_embeddings": args["share_decoder_input_output_embed"],
         "vocab_size": src_vocab_size,
     }

     # good hparam defaults to start with

     print(f'Generating {biogpt_model_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    str							=							{
         "bos_token": "<s>",
         "eos_token": "</s>",
         "model_max_length": 1024,
         "pad_token": "<pad>",
         "special_tokens_map_file": None,
         "tokenizer_class": "BioGptTokenizer",
         "unk_token": "<unk>",
     }

     print(f'Generating {biogpt_tokenizer_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # model
     UpperCAmelCase__						:    Union[str, Any]							=							chkpt["model"]

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

     # check that it loads ok
     model_new.load_state_dict(snake_case				)

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(snake_case			,					snake_case				)
     print(f'Generating {pytorch_weights_dump_path}'				)
     torch.save(snake_case			,					snake_case				)

     print("Conversion is done!"				)


if __name__ == "__main__":
 _lowerCAmelCase	:	List[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--biogpt_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help=(
         """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
         """ bpecodes, etc."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


