




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			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  PriorTransformer
   UpperCAmelCase__  :    Tuple          =  '''hidden_states'''







   @property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							4
        UpperCAmelCase_          =							8
        UpperCAmelCase_          =							7

        UpperCAmelCase_          =							floats_tensor((batch_size, embedding_dim)).to(_snake_case)

        UpperCAmelCase_          =							floats_tensor((batch_size, embedding_dim)).to(_snake_case)
        UpperCAmelCase_          =							floats_tensor((batch_size, num_embeddings, embedding_dim)).to(_snake_case)

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







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						List[str]=0):

        """simple docstring"""


        torch.manual_seed(_snake_case)
        UpperCAmelCase_          =							4
        UpperCAmelCase_          =							8
        UpperCAmelCase_          =							7

        UpperCAmelCase_          =							torch.randn((batch_size, embedding_dim)).to(_snake_case)

        UpperCAmelCase_          =							torch.randn((batch_size, embedding_dim)).to(_snake_case)
        UpperCAmelCase_          =							torch.randn((batch_size, num_embeddings, embedding_dim)).to(_snake_case)

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







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

        """simple docstring"""


        return (4, 8)







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        return (4, 8)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 4,
            '''num_layers''': 2,
            '''embedding_dim''': 8,
            '''num_embeddings''': 7,
            '''additional_embeddings''': 4,
        }
        UpperCAmelCase_          =							self.dummy_input
        return init_dict, inputs_dict







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							PriorTransformer.from_pretrained(
            '''hf-internal-testing/prior-dummy'''					,						output_loading_info=_snake_case)
        self.assertIsNotNone(_snake_case)
        self.assertEqual(len(loading_info['''missing_keys'''])					,						0)

        model.to(_snake_case)
        UpperCAmelCase_          =							model(**self.dummy_input)[0]

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







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.prepare_init_args_and_inputs_for_common()

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

        UpperCAmelCase_          =							['''hidden_states''', '''timestep''']
        self.assertListEqual(arg_names[:2]					,						_snake_case)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy''')
        UpperCAmelCase_          =							model.to(_snake_case)

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

        UpperCAmelCase_          =							self.get_dummy_seed_input()

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)[0]

        UpperCAmelCase_          =							output[0, :5].flatten().cpu()
        print(_snake_case)

        # 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.
        UpperCAmelCase_          =							torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9])
        self.assertTrue(torch_all_close(_snake_case					,						_snake_case					,						rtol=1e-2))







@slow
class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Optional[Any]=1					,						_snake_case		:						Any=768					,						_snake_case		:						Optional[Any]=77					,						_snake_case		:						Optional[int]=0):

        """simple docstring"""


        torch.manual_seed(_snake_case)
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							embedding_dim
        UpperCAmelCase_          =							num_embeddings

        UpperCAmelCase_          =							torch.randn((batch_size, embedding_dim)).to(_snake_case)

        UpperCAmelCase_          =							torch.randn((batch_size, embedding_dim)).to(_snake_case)
        UpperCAmelCase_          =							torch.randn((batch_size, num_embeddings, embedding_dim)).to(_snake_case)

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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


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







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
           [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
           # fmt: on
       ])
   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''					,						subfolder='''prior''')
        model.to(_snake_case)
        UpperCAmelCase_          =							self.get_dummy_seed_input(seed=_snake_case)

        with torch.no_grad():
             UpperCAmelCase_          =							model(**_snake_case)[0]

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

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

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






