
"""simple docstring"""




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   _A     :			List[str]  						=			"""0.12"""  # assumed parallelism: 8


if is_torch_available():
   import torch


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			int	,					__snake_case							:			List[Any]=None    )					->							List[Any]:
       if rng is None:
              lowercase       :							Union[str, Any]									=				random.Random()

       lowercase       :							List[Any]									=				1
       for dim in shape:
              total_dims *= dim

       lowercase       :							List[Any]									=				[]
       for _ in range(__snake_case    ):
              values.append(rng.randint(0	,					vocab_size - 1    )    )

       lowercase       :							Union[str, Any]									=				np.array(__snake_case	,					dtype=jnp.intaa    ).reshape(__snake_case    )

       return output


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			Optional[Any]=None    )					->							Optional[int]:
       lowercase       :							str									=				ids_tensor(__snake_case	,					vocab_size=2	,					rng=__snake_case    )
       # make sure that at least one token is attended to for each batch
       lowercase       :							Union[str, Any]									=				1
       return attn_mask

@require_flax
class 						a__		:
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							()
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Optional[Any]									=				self.model_tester.prepare_config_and_inputs_for_common()

          # cut to half length & take max batch_size 3
          lowercase       :							Optional[Any]									=				2
          lowercase       :							Dict									=				inputs["input_ids"].shape[-1] // 2
          lowercase       :							Optional[int]									=				inputs["input_ids"][:max_batch_size, :sequence_length]

          lowercase       :							Optional[int]									=				jnp.ones_like(_a							)
          lowercase       :							int									=				attention_mask[:max_batch_size, :sequence_length]

          # generate max 5 tokens
          lowercase       :							Optional[int]									=				input_ids.shape[-1] + 5
          if config.eos_token_id is not None and config.pad_token_id is None:
                 # hack to allow generate for models such as GPT2 as is done in `generate()`
                 lowercase       :							Any									=				config.eos_token_id
          return config, input_ids, attention_mask, max_length
   @is_pt_flax_cross_test
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				self._get_input_ids_and_config()
          lowercase       :							List[str]									=				False
          lowercase       :							Any									=				max_length
          lowercase       :							Any									=				0

          for model_class in self.all_generative_model_classes:
                 lowercase       :							int									=				model_class(_a							)

                 lowercase       :							Optional[Any]									=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 lowercase       :							List[Any]									=				getattr(_a      ,       _a							)
                 lowercase       :							Any									=				pt_model_class(_a							).eval()
                 lowercase       :							Union[str, Any]									=				load_flax_weights_in_pytorch_model(_a      ,       flax_model.params							)

                 lowercase       :							Optional[Any]									=				flax_model.generate(_a							).sequences
                 lowercase       :							Union[str, Any]									=				pt_model.generate(torch.tensor(_a      ,       dtype=torch.long							)							)

                 if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                        lowercase       :							List[str]									=				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

                 self.assertListEqual(pt_generation_outputs.numpy().tolist()      ,       flax_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							str									=				self._get_input_ids_and_config()
          lowercase       :							Union[str, Any]									=				False
          lowercase       :							List[str]									=				max_length

          for model_class in self.all_generative_model_classes:
                 lowercase       :							int									=				model_class(_a							)

                 lowercase       :							List[str]									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							str									=				jit(model.generate							)
                 lowercase       :							List[Any]									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Optional[int]									=				self._get_input_ids_and_config()
          lowercase       :							int									=				True
          lowercase       :							int									=				max_length

          for model_class in self.all_generative_model_classes:
                 lowercase       :							int									=				model_class(_a							)

                 lowercase       :							List[str]									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							str									=				jit(model.generate							)
                 lowercase       :							Any									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Optional[int]									=				self._get_input_ids_and_config()
          lowercase       :							Optional[int]									=				False
          lowercase       :							Tuple									=				max_length
          lowercase       :							Optional[int]									=				2

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Dict									=				model_class(_a							)

                 lowercase       :							Tuple									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							Union[str, Any]									=				jit(model.generate							)
                 lowercase       :							Dict									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				self._get_input_ids_and_config()
          lowercase       :							int									=				False
          lowercase       :							Optional[int]									=				max_length
          lowercase       :							Dict									=				2
          lowercase       :							List[str]									=				2

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Optional[int]									=				model_class(_a							)

                 lowercase       :							Dict									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[0]      ,       input_ids.shape[0] * config.num_return_sequences							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Tuple									=				self._get_input_ids_and_config()
          lowercase       :							List[str]									=				True
          lowercase       :							int									=				max_length
          lowercase       :							Union[str, Any]									=				0.8
          lowercase       :							List[str]									=				10
          lowercase       :							str									=				0.3
          lowercase       :							Optional[int]									=				1
          lowercase       :							int									=				8
          lowercase       :							List[str]									=				9

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Union[str, Any]									=				model_class(_a							)

                 lowercase       :							Any									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							List[str]									=				jit(model.generate							)
                 lowercase       :							int									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Optional[int]									=				self._get_input_ids_and_config()
          lowercase       :							Optional[int]									=				max_length
          lowercase       :							Optional[Any]									=				1
          lowercase       :							Union[str, Any]									=				8
          lowercase       :							Union[str, Any]									=				9

          for model_class in self.all_generative_model_classes:
                 lowercase       :							int									=				model_class(_a							)

                 lowercase       :							Any									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							str									=				jit(model.generate							)
                 lowercase       :							Optional[Any]									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							List[str]									=				self._get_input_ids_and_config()
          lowercase       :							Tuple									=				max_length
          lowercase       :							Any									=				2
          lowercase       :							Optional[int]									=				1
          lowercase       :							int									=				8
          lowercase       :							str									=				9

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Dict									=				model_class(_a							)

                 lowercase       :							str									=				model.generate(_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							Optional[int]									=				jit(model.generate							)
                 lowercase       :							int									=				jit_generate(_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							List[str]									=				self._get_input_ids_and_config()

          # pad attention mask on the left
          lowercase       :							Optional[int]									=				attention_mask.at[(0, 0)].set(0							)

          lowercase       :							Union[str, Any]									=				False
          lowercase       :							Optional[int]									=				max_length

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Dict									=				model_class(_a							)

                 lowercase       :							Tuple									=				model.generate(_a      ,       attention_mask=_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							int									=				jit(model.generate							)
                 lowercase       :							str									=				jit_generate(_a      ,       attention_mask=_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				self._get_input_ids_and_config()

          # pad attention mask on the left
          lowercase       :							List[Any]									=				attention_mask.at[(0, 0)].set(0							)

          lowercase       :							Union[str, Any]									=				True
          lowercase       :							Tuple									=				max_length

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Optional[Any]									=				model_class(_a							)

                 lowercase       :							List[Any]									=				model.generate(_a      ,       attention_mask=_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							Optional[Any]									=				jit(model.generate							)
                 lowercase       :							List[Any]									=				jit_generate(_a      ,       attention_mask=_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Tuple									=				self._get_input_ids_and_config()

          # pad attention mask on the left
          lowercase       :							Union[str, Any]									=				attention_mask.at[(0, 0)].set(0							)

          lowercase       :							Optional[Any]									=				2
          lowercase       :							Tuple									=				max_length

          for model_class in self.all_generative_model_classes:
                 lowercase       :							Dict									=				model_class(_a							)

                 lowercase       :							int									=				model.generate(_a      ,       attention_mask=_a							).sequences
                 self.assertEqual(generation_outputs.shape[-1]      ,       _a							)

                 lowercase       :							List[str]									=				jit(model.generate							)
                 lowercase       :							List[str]									=				jit_generate(_a      ,       attention_mask=_a							).sequences

                 self.assertListEqual(generation_outputs.tolist()      ,       jit_generation_outputs.tolist()							)


@require_flax
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"							)
          lowercase       :							Optional[int]									=				FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"							)

          lowercase       :							Optional[Any]									=				"Hello world"
          lowercase       :							Optional[int]									=				tokenizer(_a      ,       return_tensors="np"							).input_ids

          # typos are quickly detected (the correct argument is `do_sample`)
          with self.assertRaisesRegex(_a      ,       "do_samples"							):
                 model.generate(_a      ,       do_samples=_a							)

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(_a      ,       "foo"							):
                 lowercase       :							int									=				{"foo": "bar"}
                 model.generate(_a      ,       **_a							)




"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


