




import os
from typing import List, Optional, Union

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


snake_case_ : Dict    		=       logging.get_logger(__name__)

snake_case_ : Any    		=       {"vocab_file": "vocab.txt"}

snake_case_ : List[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",
    },
}

snake_case_ : Union[str, Any]    		=       {
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}


def    A						(__A   :    Optional[int]       )   ->      Dict:







     """simple docstring"""


     with open(__A   ,				'''r'''       ) as f:
          UpperCAmelCase_          =							f.read().splitlines()
          return [l.strip() for l in lines]







class __snake_case			(			a		):
   UpperCAmelCase__  :    List[Any]          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    Union[str, Any]          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    Optional[int]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__  :    Any          =  ['''input_ids''', '''attention_mask''']







   def __init__(      self		:						Any					,						_snake_case		:						int					,						_snake_case		:						Tuple="<unk>"					,						_snake_case		:						List[str]="<cls>"					,						_snake_case		:						str="<pad>"					,						_snake_case		:						str="<mask>"					,						_snake_case		:						Optional[Any]="<eos>"					,						**_snake_case		:						Dict					,						):

        """simple docstring"""


        super().__init__(**_snake_case)
        UpperCAmelCase_          =							load_vocab_file(_snake_case)
        UpperCAmelCase_          =							dict(enumerate(self.all_tokens))
        UpperCAmelCase_          =							{tok: ind for ind, tok in enumerate(self.all_tokens)}
        UpperCAmelCase_          =							unk_token
        UpperCAmelCase_          =							cls_token
        UpperCAmelCase_          =							pad_token
        UpperCAmelCase_          =							mask_token
        UpperCAmelCase_          =							eos_token
        UpperCAmelCase_          =							self.all_tokens
        self._create_trie(self.unique_no_split_tokens)







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						int):

        """simple docstring"""


        return self._id_to_token.get(_snake_case					,						self.unk_token)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						str):

        """simple docstring"""


        return self._token_to_id.get(_snake_case					,						self._token_to_id.get(self.unk_token))







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						List[Any]					,						**_snake_case		:						int):

        """simple docstring"""


        return text.split()







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Optional[Any]=False):

        """simple docstring"""


        return len(self._id_to_token)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        return {token: i for i, token in enumerate(self.all_tokens)}







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						str):

        """simple docstring"""


        return self._token_to_id.get(_snake_case					,						self._token_to_id.get(self.unk_token))







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int):

        """simple docstring"""


        return self._id_to_token.get(_snake_case					,						self.unk_token)







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						List[int]					,						_snake_case		:						Optional[List[int]] = None):

        """simple docstring"""


        UpperCAmelCase_          =							[self.cls_token_id]
        UpperCAmelCase_          =							[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 lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						List					,						_snake_case		:						Optional[List] = None					,						_snake_case		:						bool = False):

        """simple docstring"""


        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]
        UpperCAmelCase_          =							[1] + ([0] * len(_snake_case)) + [1]
        if token_ids_a is not None:
             mask += [0] * len(_snake_case) + [1]
        return mask







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						List[str]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							os.path.join(_snake_case					,						(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''')
        with open(_snake_case					,						'''w''') as f:
             f.write('''\n'''.join(self.all_tokens))
        return (vocab_file,)







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        return self.get_vocab_size(with_added_tokens=_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[List[str], List[AddedToken]]					,						_snake_case		:						bool = False):

        """simple docstring"""


        return super()._add_tokens(_snake_case					,						special_tokens=_snake_case)







"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class  _A						:

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=1		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=3		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=[1, 2]		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=6		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       class_embed_type="""timestep"""		,       mid_block_scale_factor=1.4_1_4		,       time_embedding_act_fn="""gelu"""		,       time_embedding_dim=32		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""prompt"""]
            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            if "image" in inputs:
                  lowercase														=	inputs["""image"""]
            else:
                  lowercase														=	None

            if "mask_image" in inputs:
                  lowercase														=	inputs["""mask_image"""]
            else:
                  lowercase														=	None

            if "original_image" in inputs:
                  lowercase														=	inputs["""original_image"""]
            else:
                  lowercase														=	None

            lowercase							,							lowercase														=	pipe.encode_prompt(__lowerCAmelCase						)

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(__lowerCAmelCase		,       __lowerCAmelCase						) is None		,       f'`{optional_component}` did not stay set to None after loading.'		,       )

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)



