





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_						(					lowercase_					):
      __magic_name__:			List[Any]       =       '''xlm'''
      __magic_name__:			List[Any]       =       {
          '''hidden_size''': '''emb_dim''',
          '''num_attention_heads''': '''n_heads''',
          '''num_hidden_layers''': '''n_layers''',
          '''n_words''': '''vocab_size''',  # For backward compatibility
      }






      def __init__(      self      :      List[Any]		,   _A      :      Union[str, Any]=30145		,   _A      :      List[str]=2048		,   _A      :      Union[str, Any]=12		,   _A      :      Any=16		,   _A      :      str=0.1		,   _A      :      Any=0.1		,   _A      :      Optional[Any]=True		,   _A      :      Optional[Any]=False		,   _A      :      Any=False		,   _A      :      Any=False		,   _A      :      str=1		,   _A      :      int=True		,   _A      :      List[str]=512		,   _A      :      Optional[Any]=2048**-0.5		,   _A      :      Tuple=1E-12		,   _A      :      Optional[int]=0.0_2		,   _A      :      Optional[Any]=0		,   _A      :      List[str]=1		,   _A      :      Union[str, Any]=2		,   _A      :      Optional[Any]=3		,   _A      :      List[str]=5		,   _A      :      List[str]=True		,   _A      :      List[Any]="first"		,   _A      :      Optional[Any]=True		,   _A      :      List[str]=None		,   _A      :      Dict=True		,   _A      :      Any=0.1		,   _A      :      int=5		,   _A      :      List[Any]=5		,   _A      :      Union[str, Any]=0		,   _A      :      int=0		,   _A      :      str=2		,   _A      :      List[str]=0		,   **_A      :      Optional[int]		,   )							->    Tuple:







       """simple docstring"""


       snake_case_		:			Dict											=	vocab_size
       snake_case_		:			Any											=	emb_dim
       snake_case_		:			Tuple											=	n_layers
       snake_case_		:			Optional[int]											=	n_heads
       snake_case_		:			str											=	dropout
       snake_case_		:			Tuple											=	attention_dropout
       snake_case_		:			Dict											=	gelu_activation
       snake_case_		:			List[str]											=	sinusoidal_embeddings
       snake_case_		:			Dict											=	causal
       snake_case_		:			Optional[Any]											=	asm
       snake_case_		:			str											=	n_langs
       snake_case_		:			Dict											=	use_lang_emb
       snake_case_		:			List[Any]											=	layer_norm_eps
       snake_case_		:			Optional[Any]											=	bos_index
       snake_case_		:			Any											=	eos_index
       snake_case_		:			Optional[Any]											=	pad_index
       snake_case_		:			Optional[int]											=	unk_index
       snake_case_		:			List[Any]											=	mask_index
       snake_case_		:			int											=	is_encoder
       snake_case_		:			List[Any]											=	max_position_embeddings
       snake_case_		:			Tuple											=	embed_init_std
       snake_case_		:			Optional[int]											=	init_std
       snake_case_		:			Optional[Any]											=	summary_type
       snake_case_		:			Dict											=	summary_use_proj
       snake_case_		:			Optional[Any]											=	summary_activation
       snake_case_		:			Union[str, Any]											=	summary_proj_to_labels
       snake_case_		:			Optional[int]											=	summary_first_dropout
       snake_case_		:			Optional[Any]											=	start_n_top
       snake_case_		:			int											=	end_n_top
       snake_case_		:			Any											=	mask_token_id
       snake_case_		:			Optional[Any]											=	lang_id

       if "n_words" in kwargs:
        snake_case_		:			Tuple											=	kwargs['n_words']

       super().__init__(pad_token_id=_A		,   bos_token_id=_A		,   **_A	)
class SCREAMING_SNAKE_CASE_						(					lowercase_					):






      @property
      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Optional[Any]:







       """simple docstring"""


       if self.task == "multiple-choice":
        snake_case_		:			int											=	{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
        snake_case_		:			str											=	{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]	)








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 SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






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







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


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

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

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

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






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







							"""simple docstring"""


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

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

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# 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.
							snake_case_		:			str											=	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(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""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 UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


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

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

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

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)

