

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase		:   Optional[int]										=       """scheduler_config.json"""







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			1
				__SCREAMING_SNAKE_CASE					: Any       			=			2
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			3
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			4
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			5







@dataclass
class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: jnp.ndarray







class  lowercase				:
				__SCREAMING_SNAKE_CASE					: Dict       			=			SCHEDULER_CONFIG_NAME
				__SCREAMING_SNAKE_CASE					: Tuple       			=			['''dtype''']
				__SCREAMING_SNAKE_CASE					: Any       			=			[]
				__SCREAMING_SNAKE_CASE					: int       			=			True




				@classmethod
				def     a							(					cls      ,   snake_case = None      ,   snake_case = None      ,   snake_case=False      ,   **snake_case      ,   ):
											snake_case_				,    snake_case_							=    cls.load_config(
											    pretrained_model_name_or_path=snake_case      ,   subfolder=snake_case      ,   return_unused_kwargs=snake_case      ,   **snake_case      ,   )
											snake_case_				,    snake_case_							=    cls.from_config(snake_case      ,   return_unused_kwargs=snake_case      ,   **snake_case       )

											if hasattr(snake_case      ,   'create_state'       ) and getattr(snake_case      ,   'has_state'      ,   snake_case       ):
																		snake_case_							=    scheduler.create_state()

											if return_unused_kwargs:
																		return scheduler, state, unused_kwargs

											return scheduler, state




				def     a							(					self      ,   snake_case      ,   snake_case = False      ,   **snake_case       ):
											self.save_config(save_directory=snake_case      ,   push_to_hub=snake_case      ,   **snake_case       )




				@property
				def     a							(					self       ):
											return self._get_compatibles()




				@classmethod
				def     a							(					cls       ):
											snake_case_							=    list(set([cls.__name__] + cls._compatibles       )       )
											snake_case_							=    importlib.import_module(__name__.split('.'       )[0]       )
											snake_case_							=    [
											    getattr(snake_case      ,   snake_case       ) for c in compatible_classes_str if hasattr(snake_case      ,   snake_case       )
											]
											return compatible_classes





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							assert len(UpperCamelCase__      ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase__      ) - x.ndim)      )   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=0.9_99   ,							UpperCamelCase__=jnp.floataa      ):







							'''simple docstring'''






							def alpha_bar(UpperCamelCase__      ):
														return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							snake_case_							=    []
							for i in range(UpperCamelCase__      ):
														snake_case_							=    i / num_diffusion_timesteps
														snake_case_							=    (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(UpperCamelCase__      ) / alpha_bar(UpperCamelCase__      )   ,							UpperCamelCase__      )      )
							return jnp.array(UpperCamelCase__   ,							dtype=UpperCamelCase__      )







@flax.struct.dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: jnp.ndarray
				__SCREAMING_SNAKE_CASE					: jnp.ndarray
				__SCREAMING_SNAKE_CASE					: jnp.ndarray




				@classmethod
				def     a							(					cls      ,   snake_case       ):
											snake_case_							=    scheduler.config

											if config.trained_betas is not None:
																		snake_case_							=    jnp.asarray(config.trained_betas      ,   dtype=scheduler.dtype       )
											elif config.beta_schedule == "linear":
																		snake_case_							=    jnp.linspace(config.beta_start      ,   config.beta_end      ,   config.num_train_timesteps      ,   dtype=scheduler.dtype       )
											elif config.beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		snake_case_							=    (
																		    jnp.linspace(
																		        config.beta_start**0.5      ,   config.beta_end**0.5      ,   config.num_train_timesteps      ,   dtype=scheduler.dtype       )
																		    ** 2
																		)
											elif config.beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		snake_case_							=    betas_for_alpha_bar(config.num_train_timesteps      ,   dtype=scheduler.dtype       )
											else:
																		raise NotImplementedError(
																		    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''       )

											snake_case_							=    1.0 - betas

											snake_case_							=    jnp.cumprod(snake_case      ,   axis=0       )

											return cls(
											    alphas=snake_case      ,   betas=snake_case      ,   alphas_cumprod=snake_case      ,   )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    state.alphas_cumprod

							snake_case_							=    alphas_cumprod[timesteps] ** 0.5
							snake_case_							=    sqrt_alpha_prod.flatten()
							snake_case_							=    broadcast_to_shape_from_left(UpperCamelCase__   ,							original_samples.shape      )

							snake_case_							=    (1 - alphas_cumprod[timesteps]) ** 0.5
							snake_case_							=    sqrt_one_minus_alpha_prod.flatten()
							snake_case_							=    broadcast_to_shape_from_left(UpperCamelCase__   ,							original_samples.shape      )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    get_sqrt_alpha_prod(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    get_sqrt_alpha_prod(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity







from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


_UpperCAmelCase		:   Optional[Any]										=       logging.get_logger(__name__)

# General docstring
_UpperCAmelCase		:   Dict										=       """ResNetConfig"""

# Base docstring
_UpperCAmelCase		:   Optional[int]										=       """microsoft/resnet-50"""
_UpperCAmelCase		:   Optional[Any]										=       [1, 2048, 7, 7]

# Image classification docstring
_UpperCAmelCase		:   Tuple										=       """microsoft/resnet-50"""
_UpperCAmelCase		:   int										=       """tiger cat"""

_UpperCAmelCase		:   Optional[Any]										=       [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case = 3      ,   snake_case = 1      ,   snake_case = "relu"       ):
											super().__init__()
											snake_case_							=    nn.Convad(
											    snake_case      ,   snake_case      ,   kernel_size=snake_case      ,   stride=snake_case      ,   padding=kernel_size // 2      ,   bias=snake_case       )
											snake_case_							=    nn.BatchNormad(snake_case       )
											snake_case_							=    ACTaFN[activation] if activation is not None else nn.Identity()




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.convolution(snake_case       )
											snake_case_							=    self.normalization(snake_case       )
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case       ):
											super().__init__()
											snake_case_							=    ResNetConvLayer(
											    config.num_channels      ,   config.embedding_size      ,   kernel_size=7      ,   stride=2      ,   activation=config.hidden_act       )
											snake_case_							=    nn.MaxPoolad(kernel_size=3      ,   stride=2      ,   padding=1       )
											snake_case_							=    config.num_channels




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    pixel_values.shape[1]
											if num_channels != self.num_channels:
																		raise ValueError(
																		    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'       )
											snake_case_							=    self.embedder(snake_case       )
											snake_case_							=    self.pooler(snake_case       )
											return embedding







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case = 2       ):
											super().__init__()
											snake_case_							=    nn.Convad(snake_case      ,   snake_case      ,   kernel_size=1      ,   stride=snake_case      ,   bias=snake_case       )
											snake_case_							=    nn.BatchNormad(snake_case       )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.convolution(snake_case       )
											snake_case_							=    self.normalization(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case = 1      ,   snake_case = "relu"       ):
											super().__init__()
											snake_case_							=    in_channels != out_channels or stride != 1
											snake_case_							=    (
											    ResNetShortCut(snake_case      ,   snake_case      ,   stride=snake_case       ) if should_apply_shortcut else nn.Identity()
											)
											snake_case_							=    nn.Sequential(
											    ResNetConvLayer(snake_case      ,   snake_case      ,   stride=snake_case       )      ,   ResNetConvLayer(snake_case      ,   snake_case      ,   activation=snake_case       )      ,   )
											snake_case_							=    ACTaFN[activation]




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    hidden_state
											snake_case_							=    self.layer(snake_case       )
											snake_case_							=    self.shortcut(snake_case       )
											hidden_state += residual
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case = 1      ,   snake_case = "relu"      ,   snake_case = 4       ):
											super().__init__()
											snake_case_							=    in_channels != out_channels or stride != 1
											snake_case_							=    out_channels // reduction
											snake_case_							=    (
											    ResNetShortCut(snake_case      ,   snake_case      ,   stride=snake_case       ) if should_apply_shortcut else nn.Identity()
											)
											snake_case_							=    nn.Sequential(
											    ResNetConvLayer(snake_case      ,   snake_case      ,   kernel_size=1       )      ,   ResNetConvLayer(snake_case      ,   snake_case      ,   stride=snake_case       )      ,   ResNetConvLayer(snake_case      ,   snake_case      ,   kernel_size=1      ,   activation=snake_case       )      ,   )
											snake_case_							=    ACTaFN[activation]




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    hidden_state
											snake_case_							=    self.layer(snake_case       )
											snake_case_							=    self.shortcut(snake_case       )
											hidden_state += residual
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case = 2      ,   snake_case = 2      ,   ):
											super().__init__()

											snake_case_							=    ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

											snake_case_							=    nn.Sequential(
											    # downsampling is done in the first layer with stride of 2
											    layer(snake_case      ,   snake_case      ,   stride=snake_case      ,   activation=config.hidden_act       )      ,   *[layer(snake_case      ,   snake_case      ,   activation=config.hidden_act       ) for _ in range(depth - 1       )]      ,   )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    input
											for layer in self.layers:
																		snake_case_							=    layer(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case       ):
											super().__init__()
											snake_case_							=    nn.ModuleList([]       )
											# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    ResNetStage(
											        snake_case      ,   config.embedding_size      ,   config.hidden_sizes[0]      ,   stride=2 if config.downsample_in_first_stage else 1      ,   depth=config.depths[0]      ,   )       )
											snake_case_							=    zip(config.hidden_sizes      ,   config.hidden_sizes[1:]       )
											for (in_channels, out_channels), depth in zip(snake_case      ,   config.depths[1:]       ):
																		self.stages.append(ResNetStage(snake_case      ,   snake_case      ,   snake_case      ,   depth=snake_case       )       )




				def     a							(					self      ,   snake_case      ,   snake_case = False      ,   snake_case = True       ):
											snake_case_							=    () if output_hidden_states else None

											for stage_module in self.stages:
																		if output_hidden_states:
																									snake_case_							=    hidden_states + (hidden_state,)

																		snake_case_							=    stage_module(snake_case       )

											if output_hidden_states:
																		snake_case_							=    hidden_states + (hidden_state,)

											if not return_dict:
																		return tuple(v for v in [hidden_state, hidden_states] if v is not None       )

											return BaseModelOutputWithNoAttention(
											    last_hidden_state=snake_case      ,   hidden_states=snake_case      ,   )







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			ResNetConfig
				__SCREAMING_SNAKE_CASE					: Any       			=			'''resnet'''
				__SCREAMING_SNAKE_CASE					: int       			=			'''pixel_values'''
				__SCREAMING_SNAKE_CASE					: Tuple       			=			True




				def     a							(					self      ,   snake_case       ):
											if isinstance(snake_case      ,   nn.Convad       ):
																		nn.init.kaiming_normal_(module.weight      ,   mode='fan_out'      ,   nonlinearity='relu'       )
											elif isinstance(snake_case      ,   (nn.BatchNormad, nn.GroupNorm)       ):
																		nn.init.constant_(module.weight      ,   1       )
																		nn.init.constant_(module.bias      ,   0       )




				def     a							(					self      ,   snake_case      ,   snake_case=False       ):
											if isinstance(snake_case      ,   snake_case       ):
																		snake_case_							=    value



_UpperCAmelCase		:   Tuple										=       R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

_UpperCAmelCase		:   Optional[int]										=       R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''	,  lowercase_	,  )
class  lowercase				(       lowercase_			):




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											snake_case_							=    config
											snake_case_							=    ResNetEmbeddings(snake_case       )
											snake_case_							=    ResNetEncoder(snake_case       )
											snake_case_							=    nn.AdaptiveAvgPoolad((1, 1)       )
											# Initialize weights and apply final processing
											self.post_init()




				@add_start_docstrings_to_model_forward(snake_case       )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC      ,   output_type=snake_case      ,   config_class=_CONFIG_FOR_DOC      ,   modality='vision'      ,   expected_output=_EXPECTED_OUTPUT_SHAPE      ,   )
				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None       ):
											snake_case_							=    (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict

											snake_case_							=    self.embedder(snake_case       )

											snake_case_							=    self.encoder(
											    snake_case      ,   output_hidden_states=snake_case      ,   return_dict=snake_case       )

											snake_case_							=    encoder_outputs[0]

											snake_case_							=    self.pooler(snake_case       )

											if not return_dict:
																		return (last_hidden_state, pooled_output) + encoder_outputs[1:]

											return BaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=snake_case      ,   pooler_output=snake_case      ,   hidden_states=encoder_outputs.hidden_states      ,   )







@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''	,  lowercase_	,  )
class  lowercase				(       lowercase_			):




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											snake_case_							=    config.num_labels
											snake_case_							=    ResNetModel(snake_case       )
											# classification head
											snake_case_							=    nn.Sequential(
											    nn.Flatten()      ,   nn.Linear(config.hidden_sizes[-1]      ,   config.num_labels       ) if config.num_labels > 0 else nn.Identity()      ,   )
											# initialize weights and apply final processing
											self.post_init()




				@add_start_docstrings_to_model_forward(snake_case       )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,   output_type=snake_case      ,   config_class=_CONFIG_FOR_DOC      ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,   )
				def     a							(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   ):
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict

											snake_case_							=    self.resnet(snake_case      ,   output_hidden_states=snake_case      ,   return_dict=snake_case       )

											snake_case_							=    outputs.pooler_output if return_dict else outputs[1]

											snake_case_							=    self.classifier(snake_case       )

											snake_case_							=    None

											if labels is not None:
																		if self.config.problem_type is None:
																									if self.num_labels == 1:
																																snake_case_							=    'regression'
																									elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																snake_case_							=    'single_label_classification'
																									else:
																																snake_case_							=    'multi_label_classification'
																		if self.config.problem_type == "regression":
																									snake_case_							=    MSELoss()
																									if self.num_labels == 1:
																																snake_case_							=    loss_fct(logits.squeeze()      ,   labels.squeeze()       )
																									else:
																																snake_case_							=    loss_fct(snake_case      ,   snake_case       )
																		elif self.config.problem_type == "single_label_classification":
																									snake_case_							=    CrossEntropyLoss()
																									snake_case_							=    loss_fct(logits.view(-1      ,   self.num_labels       )      ,   labels.view(-1       )       )
																		elif self.config.problem_type == "multi_label_classification":
																									snake_case_							=    BCEWithLogitsLoss()
																									snake_case_							=    loss_fct(snake_case      ,   snake_case       )

											if not return_dict:
																		snake_case_							=    (logits,) + outputs[2:]
																		return (loss,) + output if loss is not None else output

											return ImageClassifierOutputWithNoAttention(loss=snake_case      ,   logits=snake_case      ,   hidden_states=outputs.hidden_states       )







@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''	,  lowercase_	,  )
class  lowercase				(       lowercase_	,  lowercase_			):




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											super()._init_backbone(snake_case       )

											snake_case_							=    [config.embedding_size] + config.hidden_sizes
											snake_case_							=    ResNetEmbeddings(snake_case       )
											snake_case_							=    ResNetEncoder(snake_case       )

											# initialize weights and apply final processing
											self.post_init()




				@add_start_docstrings_to_model_forward(snake_case       )
				@replace_return_docstrings(output_type=snake_case      ,   config_class=_CONFIG_FOR_DOC       )
				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None       ):
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict
											snake_case_							=    (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)

											snake_case_							=    self.embedder(snake_case       )

											snake_case_							=    self.encoder(snake_case      ,   output_hidden_states=snake_case      ,   return_dict=snake_case       )

											snake_case_							=    outputs.hidden_states

											snake_case_							=    ()
											for idx, stage in enumerate(self.stage_names       ):
																		if stage in self.out_features:
																									feature_maps += (hidden_states[idx],)

											if not return_dict:
																		snake_case_							=    (feature_maps,)
																		if output_hidden_states:
																									output += (outputs.hidden_states,)
																		return output

											return BackboneOutput(
											    feature_maps=snake_case      ,   hidden_states=outputs.hidden_states if output_hidden_states else None      ,   attentions=snake_case      ,   )




