


'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class   snake_case					(					UpperCAmelCase__    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						PriorTransformer
  _lowerCamelCase						=						"hidden_states"






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						4
   lowerCamelCase_  				=						8
   lowerCamelCase_  				=						7

   lowerCamelCase_  				=						floats_tensor((batch_size, embedding_dim)  ).to(lowerCamelCase__  )

   lowerCamelCase_  				=						floats_tensor((batch_size, embedding_dim)  ).to(lowerCamelCase__  )
   lowerCamelCase_  				=						floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase__  )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }






  def 						snake_case	(					self    ,						UpperCamelCase=0  ):







   """simple docstring"""
   torch.manual_seed(lowerCamelCase__  )
   lowerCamelCase_  				=						4
   lowerCamelCase_  				=						8
   lowerCamelCase_  				=						7

   lowerCamelCase_  				=						torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase__  )

   lowerCamelCase_  				=						torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase__  )
   lowerCamelCase_  				=						torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase__  )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return (4, 8)






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return (4, 8)






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   lowerCamelCase_  				=						self.dummy_input
   return init_dict, inputs_dict






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"    ,						output_loading_info=lowerCamelCase__  )
   self.assertIsNotNone(lowerCamelCase__  )
   self.assertEqual(len(loading_info["missing_keys"]  )    ,						0  )

   model.to(lowerCamelCase__  )
   lowerCamelCase_  				=						model(**self.dummy_input  )[0]

   assert hidden_states is not None, "Make sure output is not None"






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_init_args_and_inputs_for_common()

   lowerCamelCase_  				=						self.model_class(**lowerCamelCase__  )
   lowerCamelCase_  				=						inspect.signature(model.forward  )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCamelCase_  				=						[*signature.parameters.keys()]

   lowerCamelCase_  				=						["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]    ,						lowerCamelCase__  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
   lowerCamelCase_  				=						model.to(lowerCamelCase__  )

   if hasattr(lowerCamelCase__    ,						"set_default_attn_processor"  ):
    model.set_default_attn_processor()

   lowerCamelCase_  				=						self.get_dummy_seed_input()

   with torch.no_grad():
    lowerCamelCase_  				=						model(**lowerCamelCase__  )[0]

   lowerCamelCase_  				=						output[0, :5].flatten().cpu()
   print(lowerCamelCase__  )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   lowerCamelCase_  				=						torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
   self.assertTrue(torch_all_close(lowerCamelCase__    ,						lowerCamelCase__    ,						rtol=1e-2  )  )



@slow
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=768    ,						UpperCamelCase=77    ,						UpperCamelCase=0  ):







   """simple docstring"""
   torch.manual_seed(lowerCamelCase__  )
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						embedding_dim
   lowerCamelCase_  				=						num_embeddings

   lowerCamelCase_  				=						torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase__  )

   lowerCamelCase_  				=						torch.randn((batch_size, embedding_dim)  ).to(lowerCamelCase__  )
   lowerCamelCase_  				=						torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(lowerCamelCase__  )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }






  def 						snake_case	(					self  ):







   """simple docstring"""
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







  @parameterized.expand(
      [
          # fmt: off
          [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
          [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
          # fmt: on
      ]  )
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"    ,						subfolder="prior"  )
   model.to(lowerCamelCase__  )
   lowerCamelCase_  				=						self.get_dummy_seed_input(seed=lowerCamelCase__  )

   with torch.no_grad():
    lowerCamelCase_  				=						model(**lowerCamelCase__  )[0]

   assert list(sample.shape  ) == [1, 768]

   lowerCamelCase_  				=						sample[0, :8].flatten().cpu()
   print(lowerCamelCase__  )
   lowerCamelCase_  				=						torch.tensor(lowerCamelCase__  )

   assert torch_all_close(lowerCamelCase__    ,						lowerCamelCase__    ,						atol=1e-3  )







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Optional[Any]      			=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

   lowerCamelCase_  				=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
   if pre_tok_state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
    lowerCamelCase_  				=						getattr(UpperCamelCase    ,						pre_tok_state.pop("type"  )  )
    lowerCamelCase_  				=						add_prefix_space
    lowerCamelCase_  				=						pre_tok_class(**UpperCamelCase  )

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



