





from typing import TYPE_CHECKING

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


A       :   str					= {
    """configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
    """feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
    """processing_whisper""": ["""WhisperProcessor"""],
    """tokenization_whisper""": ["""WhisperTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Any					= ["""WhisperTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Union[str, Any]					= [
					    """WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """WhisperForConditionalGeneration""",
					    """WhisperModel""",
					    """WhisperPreTrainedModel""",
					    """WhisperForAudioClassification""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   int					= [
					    """TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFWhisperForConditionalGeneration""",
					    """TFWhisperModel""",
					    """TFWhisperPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[Any]					= [
					    """FlaxWhisperForConditionalGeneration""",
					    """FlaxWhisperModel""",
					    """FlaxWhisperPreTrainedModel""",
					    """FlaxWhisperForAudioClassification""",
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					A       :   Dict					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PriorTransformer
	_A													=				"hidden_states"

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      List[Any]      =		4
			lowercase__					:      Optional[int]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Optional[Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      Any      =		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:						int,					lowerCamelCase_:						Dict=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Tuple      =		4
			lowercase__					:      List[Any]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Union[str, Any]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		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:						str			):
			return (4, 8)

	@property
	def    snake_case__(						self:						List[str]			):
			return (4, 8)

	def    snake_case__(						self:						Dict			):
			lowercase__					:      int      =		{
			    'num_attention_heads': 2,
			    'attention_head_dim': 4,
			    'num_layers': 2,
			    'embedding_dim': 8,
			    'num_embeddings': 7,
			    'additional_embeddings': 4,
			}
			lowercase__					:      Optional[Any]      =		self.dummy_input
			return init_dict, inputs_dict

	def    snake_case__(						self:						int			):
			lowercase__   ,				lowercase__					:      Dict      =		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_			)
			lowercase__					:      Tuple      =		model(**self.dummy_input			)[0]

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

	def    snake_case__(						self:						str			):
			lowercase__   ,				lowercase__					:      List[Any]      =		self.prepare_init_args_and_inputs_for_common()

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

			lowercase__					:      List[str]      =		['hidden_states', 'timestep']
			self.assertListEqual(arg_names[:2],					lowerCamelCase_			)





	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
			lowercase__					:      Optional[int]      =		model.to(lowerCamelCase_			)

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

			lowercase__					:      List[str]      =		self.get_dummy_seed_input()

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

			lowercase__					:      int      =		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.
			lowercase__					:      List[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(lowerCamelCase_,					lowerCamelCase_,					rtol=1E-2			)			)


@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Union[str, Any]=1,					lowerCamelCase_:						Tuple=768,					lowerCamelCase_:						Dict=77,					lowerCamelCase_:						Union[str, Any]=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Dict      =		batch_size
			lowercase__					:      Dict      =		embedding_dim
			lowercase__					:      Dict      =		num_embeddings

			lowercase__					:      int      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      str      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		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:						str			):
			# clean up the VRAM after each test
			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    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						str			):
			lowercase__					:      List[Any]      =		PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',					subfolder='prior'			)
			model.to(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.get_dummy_seed_input(seed=lowerCamelCase_			)

			with torch.no_grad():
					lowercase__					:      List[str]      =		model(**lowerCamelCase_			)[0]

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

			lowercase__					:      Union[str, Any]      =		sample[0, :8].flatten().cpu()
			print(lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		torch.tensor(lowerCamelCase_			)

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


