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	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		PriorTransformer
      a__				=		"""hidden_states"""



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     4
        a__:					Any      							=     8
        a__:					Optional[Any]      							=     7

        a__:					Tuple      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)

        a__:					Optional[int]      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)
        a__:					List[str]      							=     floats_tensor((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      def   lowerCamelCase_       (						self   ,      lowercase=0)     ->      str:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Optional[Any]      							=     4
        a__:					Optional[Any]      							=     8
        a__:					Union[str, Any]      							=     7

        a__:					Optional[Any]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					Tuple      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return (4, 8)



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return (4, 8)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     {
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        a__:					Union[str, Any]      							=     self.dummy_input
        return init_dict, inputs_dict



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     PriorTransformer.from_pretrained(
            'hf-internal-testing/prior-dummy'   ,      output_loading_info=lowercase)
        self.assertIsNotNone(lowercase)
        self.assertEqual(len(loading_info['missing_keys'])   ,      0)

        model.to(lowercase)
        a__:					Any      							=     model(**self.dummy_input)[0]

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



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					Tuple      							=     self.prepare_init_args_and_inputs_for_common()

        a__:					Any      							=     self.model_class(**lowercase)
        a__:					str      							=     inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a__:					Tuple      							=     [*signature.parameters.keys()]

        a__:					List[Any]      							=     ['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					str      							=     PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
        a__:					str      							=     model.to(lowercase)

        if hasattr(lowercase   ,      'set_default_attn_processor'):
          model.set_default_attn_processor()

        a__:					Dict      							=     self.get_dummy_seed_input()

        with torch.no_grad():
          a__:					str      							=     model(**lowercase)[0]

        a__:					str      							=     output[0, :5].flatten().cpu()
        print(lowercase)

        # 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.
        a__:					Any      							=     torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
        self.assertTrue(torch_all_close(lowercase   ,      lowercase   ,      rtol=1e-2))







@slow
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase=1   ,      lowercase=7_68   ,      lowercase=77   ,      lowercase=0)     ->      int:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Union[str, Any]      							=     batch_size
        a__:					List[str]      							=     embedding_dim
        a__:					str      							=     num_embeddings

        a__:					Tuple      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					str      							=     torch.randn((batch_size, num_embeddings, embedding_dim)).to(lowercase)

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



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
              [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
              # fmt: on
          ])
      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Tuple      							=     PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,      subfolder='prior')
        model.to(lowercase)
        a__:					Optional[Any]      							=     self.get_dummy_seed_input(seed=lowercase)

        with torch.no_grad():
          a__:					Optional[int]      							=     model(**lowercase)[0]

        assert list(sample.shape) == [1, 7_68]

        a__:					List[str]      							=     sample[0, :8].flatten().cpu()
        print(lowercase)
        a__:					Union[str, Any]      							=     torch.tensor(lowercase)

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



"""simple docstring"""
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		DebertaTokenizer
      a__				=		True
      a__				=		DebertaTokenizerFast



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        a__:					List[str]      							=     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        a__:					List[str]      							=     dict(zip(lowercase   ,      range(len(lowercase))))
        a__:					Any      							=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:					Optional[Any]      							=     {'unk_token': '[UNK]'}

        a__:					Any      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file'])
        a__:					Dict      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file'])
        with open(self.vocab_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write(json.dumps(lowercase) + '\n')
        with open(self.merges_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     'lower newer'
        a__:					int      							=     'lower newer'
        return input_text, output_text



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.get_tokenizer()
        a__:					List[Any]      							=     'lower newer'
        a__:					Optional[int]      							=     ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        a__:					Optional[Any]      							=     tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase   ,      lowercase)

        a__:					int      							=     tokens + [tokenizer.unk_token]
        a__:					Optional[int]      							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					int      							=     self.get_tokenizer()
        a__:					Any      							=     tokenizer('Hello'   ,      'World')
        a__:					Optional[Any]      							=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids']   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.tokenizer_class.from_pretrained('microsoft/deberta-base')

        a__:					Union[str, Any]      							=     tokenizer.encode('sequence builders'   ,      add_special_tokens=lowercase)
        a__:					Optional[Any]      							=     tokenizer.encode('multi-sequence build'   ,      add_special_tokens=lowercase)

        a__:					List[str]      							=     tokenizer.encode(
            'sequence builders'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)
        a__:					Any      							=     tokenizer.encode(
            'sequence builders'   ,      'multi-sequence build'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)

        a__:					Optional[int]      							=     tokenizer.build_inputs_with_special_tokens(lowercase)
        a__:					Union[str, Any]      							=     tokenizer.build_inputs_with_special_tokens(lowercase   ,      lowercase)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.tokenizer_class]
        if self.test_rust_tokenizer:
          tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
          a__:					int      							=     tokenizer_class.from_pretrained('microsoft/deberta-base')

          a__:					List[str]      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          a__:					Dict      							=     tokenizer(lowercase   ,      padding=lowercase)
          a__:					Union[str, Any]      							=     [tokenizer.decode(lowercase   ,      skip_special_tokens=lowercase) for seq in encoding['input_ids']]

          # fmt: off
          a__:					Any      							=     {
              'input_ids': [
                  [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
              ],
              'token_type_ids': [
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
              ],
              'attention_mask': [
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
              ]
          }
          # fmt: on

          a__:					str      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          self.assertDictEqual(encoding.data   ,      lowercase)

          for expected, decoded in zip(lowercase   ,      lowercase):
            self.assertEqual(lowercase   ,      lowercase)


