import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Any						=      DDIMPipeline
    a__	:						Dict						=      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    a__	:						str						=      PipelineTesterMixin.required_optional_params - {
        """num_images_per_prompt""",
        """latents""",
        """callback""",
        """callback_steps""",
    }
    a__	:						int						=      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    a__	:						List[Any]						=      False







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        torch.manual_seed(0)
        __UpperCamelCase     :Optional[int]			  =		UNetaDModel(
            block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
        __UpperCamelCase     :Union[str, Any]			  =		DDIMScheduler()
        __UpperCamelCase     :str			  =		{'''unet''': unet, '''scheduler''': scheduler}
        return components







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=0)       ->	Optional[Any]:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Optional[Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :Dict			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase     :List[str]			  =		{
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :str			  =		'''cpu'''

        __UpperCamelCase     :Tuple			  =		self.get_dummy_components()
        __UpperCamelCase     :Optional[Any]			  =		self.pipeline_class(**__lowercase)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :Any			  =		self.get_dummy_inputs(__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		pipe(**__lowercase).images
        __UpperCamelCase     :Union[str, Any]			  =		image[0, -3:, -3:, -1]

        self.assertEqual(image.shape , (1, 32, 32, 3))
        __UpperCamelCase     :Optional[int]			  =		np.array(
            [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4])
        __UpperCamelCase     :str			  =		np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(__lowercase , 1E-3)







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        super().test_save_load_local(expected_max_difference=3E-3)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        super().test_save_load_optional_components(expected_max_difference=3E-3)







    def 			UpperCamelCase__   (	self)       ->	Any:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3)


@slow
@require_torch_gpu
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :List[str]			  =		'''google/ddpm-cifar10-32'''

        __UpperCamelCase     :Union[str, Any]			  =		UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase     :str			  =		DDIMScheduler()

        __UpperCamelCase     :Union[str, Any]			  =		DDIMPipeline(unet=__lowercase , scheduler=__lowercase)
        ddim.to(__lowercase)
        ddim.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :List[Any]			  =		torch.manual_seed(0)
        __UpperCamelCase     :Any			  =		ddim(generator=__lowercase , eta=0.0 , output_type='''numpy''').images

        __UpperCamelCase     :int			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase     :int			  =		np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		'''google/ddpm-ema-bedroom-256'''

        __UpperCamelCase     :List[str]			  =		UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase     :Dict			  =		DDIMScheduler.from_pretrained(__lowercase)

        __UpperCamelCase     :Optional[Any]			  =		DDIMPipeline(unet=__lowercase , scheduler=__lowercase)
        ddpm.to(__lowercase)
        ddpm.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :Dict			  =		torch.manual_seed(0)
        __UpperCamelCase     :str			  =		ddpm(generator=__lowercase , output_type='''numpy''').images

        __UpperCamelCase     :Dict			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        __UpperCamelCase     :Tuple			  =		np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      """wav2vec2"""







    def __init__(	self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=0 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="sum" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , __lowercase=None , **__lowercase , )       ->	int:
        super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
        __UpperCamelCase     :Any			  =		hidden_size
        __UpperCamelCase     :int			  =		feat_extract_norm
        __UpperCamelCase     :Tuple			  =		feat_extract_activation
        __UpperCamelCase     :Union[str, Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :int			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		conv_bias
        __UpperCamelCase     :Optional[int]			  =		num_conv_pos_embeddings
        __UpperCamelCase     :Dict			  =		num_conv_pos_embedding_groups
        __UpperCamelCase     :Any			  =		len(self.conv_dim)
        __UpperCamelCase     :List[str]			  =		num_hidden_layers
        __UpperCamelCase     :int			  =		intermediate_size
        __UpperCamelCase     :str			  =		hidden_act
        __UpperCamelCase     :Any			  =		num_attention_heads
        __UpperCamelCase     :int			  =		hidden_dropout
        __UpperCamelCase     :Tuple			  =		attention_dropout
        __UpperCamelCase     :List[str]			  =		activation_dropout
        __UpperCamelCase     :Optional[Any]			  =		feat_proj_dropout
        __UpperCamelCase     :Any			  =		final_dropout
        __UpperCamelCase     :Any			  =		layerdrop
        __UpperCamelCase     :str			  =		layer_norm_eps
        __UpperCamelCase     :Optional[Any]			  =		initializer_range
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :str			  =		do_stable_layer_norm
        __UpperCamelCase     :Union[str, Any]			  =		use_weighted_layer_sum

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase     :List[Any]			  =		apply_spec_augment
        __UpperCamelCase     :Tuple			  =		mask_time_prob
        __UpperCamelCase     :int			  =		mask_time_length
        __UpperCamelCase     :Dict			  =		mask_time_min_masks
        __UpperCamelCase     :str			  =		mask_feature_prob
        __UpperCamelCase     :List[str]			  =		mask_feature_length
        __UpperCamelCase     :Union[str, Any]			  =		mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase     :Optional[Any]			  =		num_codevectors_per_group
        __UpperCamelCase     :List[Any]			  =		num_codevector_groups
        __UpperCamelCase     :Tuple			  =		contrastive_logits_temperature
        __UpperCamelCase     :Optional[int]			  =		feat_quantizer_dropout
        __UpperCamelCase     :Optional[int]			  =		num_negatives
        __UpperCamelCase     :List[Any]			  =		codevector_dim
        __UpperCamelCase     :str			  =		proj_codevector_dim
        __UpperCamelCase     :List[str]			  =		diversity_loss_weight

        # ctc loss
        __UpperCamelCase     :Tuple			  =		ctc_loss_reduction
        __UpperCamelCase     :Tuple			  =		ctc_zero_infinity

        # adapter
        __UpperCamelCase     :List[str]			  =		add_adapter
        __UpperCamelCase     :Tuple			  =		adapter_kernel_size
        __UpperCamelCase     :str			  =		adapter_stride
        __UpperCamelCase     :Tuple			  =		num_adapter_layers
        __UpperCamelCase     :Tuple			  =		output_hidden_size or hidden_size
        __UpperCamelCase     :Optional[Any]			  =		adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[Any]			  =		classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[int]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :str			  =		xvector_output_dim







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return functools.reduce(operator.mul , self.conv_stride , 1)





