



from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


# General docstring
SCREAMING_SNAKE_CASE__   			=   '''MobileNetV1Config'''

# Base docstring
SCREAMING_SNAKE_CASE__   			=   '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE__   			=   [1, 1_0_2_4, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE__   			=   '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE__   			=   '''tabby, tabby cat'''


SCREAMING_SNAKE_CASE__   			=   [
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None       )    ->   List[str]:
			A__	  =     {}

			if isinstance(__UpperCamelCase			,						__UpperCamelCase       ):
						A__	  =     model.mobilenet_va
			else:
						A__	  =     model

			A__	  =     'MobilenetV1/Conv2d_0/'
			A__	  =     backbone.conv_stem.convolution.weight
			A__	  =     backbone.conv_stem.normalization.bias
			A__	  =     backbone.conv_stem.normalization.weight
			A__	  =     backbone.conv_stem.normalization.running_mean
			A__	  =     backbone.conv_stem.normalization.running_var

			for i in range(13       ):
						A__	  =     i + 1
						A__	  =     i * 2

						A__	  =     backbone.layer[pt_index]
						A__	  =     f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
						A__	  =     pointer.convolution.weight
						A__	  =     pointer.normalization.bias
						A__	  =     pointer.normalization.weight
						A__	  =     pointer.normalization.running_mean
						A__	  =     pointer.normalization.running_var

						A__	  =     backbone.layer[pt_index + 1]
						A__	  =     f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
						A__	  =     pointer.convolution.weight
						A__	  =     pointer.normalization.bias
						A__	  =     pointer.normalization.weight
						A__	  =     pointer.normalization.running_mean
						A__	  =     pointer.normalization.running_var

			if isinstance(__UpperCamelCase			,						__UpperCamelCase       ):
						A__	  =     'MobilenetV1/Logits/Conv2d_1c_1x1/'
						A__	  =     model.classifier.weight
						A__	  =     model.classifier.bias

			return tf_to_pt_map


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			try:
						import numpy as np
						import tensorflow as tf
			except ImportError:
						logger.error(
						    'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
						    'https://www.tensorflow.org/install/ for installation instructions.'       )
						raise

			# Load weights from TF model
			A__	  =     tf.train.list_variables(__UpperCamelCase       )
			A__	  =     {}
			for name, shape in init_vars:
						logger.info(f'''Loading TF weight {name} with shape {shape}'''       )
						A__	  =     tf.train.load_variable(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     array

			# Build TF to PyTorch weights loading map
			A__	  =     _build_tf_to_pytorch_map(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			for name, pointer in tf_to_pt_map.items():
						logger.info(f'''Importing {name}'''       )
						if name not in tf_weights:
									logger.info(f'''{name} not in tf pre-trained weights, skipping'''       )
									continue

						A__	  =     tf_weights[name]

						if "depthwise_weights" in name:
									logger.info('Transposing depthwise'       )
									A__	  =     np.transpose(__UpperCamelCase			,						(2, 3, 0, 1)       )
						elif "weights" in name:
									logger.info('Transposing'       )
									if len(pointer.shape       ) == 2:  # copying into linear layer
												A__	  =     array.squeeze().transpose()
									else:
												A__	  =     np.transpose(__UpperCamelCase			,						(3, 2, 0, 1)       )

						if pointer.shape != array.shape:
									raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched'''       )

						logger.info(f'''Initialize PyTorch weight {name} {array.shape}'''       )
						A__	  =     torch.from_numpy(__UpperCamelCase       )

						tf_weights.pop(__UpperCamelCase			,						__UpperCamelCase       )
						tf_weights.pop(name + '/RMSProp'			,						__UpperCamelCase       )
						tf_weights.pop(name + '/RMSProp_1'			,						__UpperCamelCase       )
						tf_weights.pop(name + '/ExponentialMovingAverage'			,						__UpperCamelCase       )

			logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys()       )}'''       )
			return model


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   torch.Tensor:
			A__  ,			A__	  =     features.shape[-2:]
			A__  ,			A__	  =     conv_layer.stride
			A__  ,			A__	  =     conv_layer.kernel_size

			if in_height % stride_height == 0:
						A__	  =     max(kernel_height - stride_height			,						0       )
			else:
						A__	  =     max(kernel_height - (in_height % stride_height)			,						0       )

			if in_width % stride_width == 0:
						A__	  =     max(kernel_width - stride_width			,						0       )
			else:
						A__	  =     max(kernel_width - (in_width % stride_width)			,						0       )

			A__	  =     pad_along_width // 2
			A__	  =     pad_along_width - pad_left
			A__	  =     pad_along_height // 2
			A__	  =     pad_along_height - pad_top

			A__	  =     (pad_left, pad_right, pad_top, pad_bottom)
			return nn.functional.pad(__UpperCamelCase			,						__UpperCamelCase			,						'constant'			,						0.0       )


class   __lowerCAmelCase  (		nn.Module   ):
					"""simple docstring"""






					def __init__(  self       :							List[str]					,       _snake_case       :							MobileNetVaConfig					,       _snake_case       :							int					,       _snake_case       :							int					,       _snake_case       :							int					,       _snake_case       :							Optional[int] = 1					,       _snake_case       :							Optional[int] = 1					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[bool] = True					,       _snake_case       :							Optional[bool or str] = True					,       ):






								"""simple docstring"""


								super().__init__()
								A__	  =     config

								if in_channels % groups != 0:
											raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.'''       )
								if out_channels % groups != 0:
											raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.'''       )

								A__	  =     0 if config.tf_padding else int((kernel_size - 1) / 2       )

								A__	  =     nn.Convad(
								    in_channels=_snake_case					,       out_channels=_snake_case					,       kernel_size=_snake_case					,       stride=_snake_case					,       padding=_snake_case					,       groups=_snake_case					,       bias=_snake_case					,       padding_mode='zeros'					,       )

								if use_normalization:
											A__	  =     nn.BatchNormad(
											    num_features=_snake_case					,       eps=config.layer_norm_eps					,       momentum=0.9997					,       affine=_snake_case					,       track_running_stats=_snake_case					,       )
								else:
											A__	  =     None

								if use_activation:
											if isinstance(_snake_case					,       _snake_case       ):
														A__	  =     ACTaFN[use_activation]
											elif isinstance(config.hidden_act					,       _snake_case       ):
														A__	  =     ACTaFN[config.hidden_act]
											else:
														A__	  =     config.hidden_act
								else:
											A__	  =     None



					def      _a				(  self       :							Dict					,       _snake_case       :							torch.Tensor       ):






								"""simple docstring"""


								if self.config.tf_padding:
											A__	  =     apply_tf_padding(_snake_case					,       self.convolution       )
								A__	  =     self.convolution(_snake_case       )
								if self.normalization is not None:
											A__	  =     self.normalization(_snake_case       )
								if self.activation is not None:
											A__	  =     self.activation(_snake_case       )
								return features







class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     MobileNetVaConfig
					A__					:		List[Any]      							=     load_tf_weights_in_mobilenet_va
					A__					:		Tuple      							=     "mobilenet_v1"
					A__					:		List[Any]      							=     "pixel_values"
					A__					:		int      							=     False



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Union[nn.Linear, nn.Convad]       ):






								"""simple docstring"""


								if isinstance(_snake_case					,       (nn.Linear, nn.Convad)       ):
											module.weight.data.normal_(mean=0.0					,       std=self.config.initializer_range       )
											if module.bias is not None:
														module.bias.data.zero_()
								elif isinstance(_snake_case					,       nn.BatchNormad       ):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0       )







SCREAMING_SNAKE_CASE__   			=   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 ([`MobileNetV1Config`]): 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.
'''

SCREAMING_SNAKE_CASE__   			=   r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__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 MobileNetV1 model outputting raw hidden-states without any specific head on top."				,  UpperCAmelCase_				,  )
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Optional[int]					,       _snake_case       :							MobileNetVaConfig					,       _snake_case       :							bool = True       ):






								"""simple docstring"""


								super().__init__(_snake_case       )
								A__	  =     config

								A__	  =     32
								A__	  =     max(int(depth * config.depth_multiplier       )					,       config.min_depth       )

								A__	  =     MobileNetVaConvLayer(
								    _snake_case					,       in_channels=config.num_channels					,       out_channels=_snake_case					,       kernel_size=3					,       stride=2					,       )

								A__	  =     [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

								A__	  =     nn.ModuleList()
								for i in range(13       ):
											A__	  =     out_channels

											if strides[i] == 2 or i == 0:
														depth *= 2
														A__	  =     max(int(depth * config.depth_multiplier       )					,       config.min_depth       )

											self.layer.append(
											    MobileNetVaConvLayer(
											        _snake_case					,       in_channels=_snake_case					,       out_channels=_snake_case					,       kernel_size=3					,       stride=strides[i]					,       groups=_snake_case					,       )       )

											self.layer.append(
											    MobileNetVaConvLayer(
											        _snake_case					,       in_channels=_snake_case					,       out_channels=_snake_case					,       kernel_size=1					,       )       )

								A__	  =     nn.AdaptiveAvgPoolad((1, 1)       ) if add_pooling_layer else None

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



					def      _a				(  self       :							List[str]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								raise NotImplementedError



					@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       :							Optional[Any]					,       _snake_case       :							Optional[torch.Tensor] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[bool] = None					,       ):






								"""simple docstring"""


								A__	  =     (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								A__	  =     return_dict if return_dict is not None else self.config.use_return_dict

								if pixel_values is None:
											raise ValueError('You have to specify pixel_values'       )

								A__	  =     self.conv_stem(_snake_case       )

								A__	  =     () if output_hidden_states else None

								for i, layer_module in enumerate(self.layer       ):
											A__	  =     layer_module(_snake_case       )

											if output_hidden_states:
														A__	  =     all_hidden_states + (hidden_states,)

								A__	  =     hidden_states

								if self.pooler is not None:
											A__	  =     torch.flatten(self.pooler(_snake_case       )					,       start_dim=1       )
								else:
											A__	  =     None

								if not return_dict:
											return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None       )

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case					,       pooler_output=_snake_case					,       hidden_states=_snake_case					,       )







@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "				,  UpperCAmelCase_				,  )
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Optional[Any]					,       _snake_case       :							MobileNetVaConfig       ):






								"""simple docstring"""


								super().__init__(_snake_case       )

								A__	  =     config.num_labels
								A__	  =     MobileNetVaModel(_snake_case       )

								A__	  =     self.mobilenet_va.layer[-1].convolution.out_channels

								# Classifier head
								A__	  =     nn.Dropout(config.classifier_dropout_prob					,       inplace=_snake_case       )
								A__	  =     nn.Linear(_snake_case					,       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       :							Optional[Any]					,       _snake_case       :							Optional[torch.Tensor] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[torch.Tensor] = None					,       _snake_case       :							Optional[bool] = None					,       ):






								"""simple docstring"""


								A__	  =     return_dict if return_dict is not None else self.config.use_return_dict

								A__	  =     self.mobilenet_va(_snake_case					,       output_hidden_states=_snake_case					,       return_dict=_snake_case       )

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

								A__	  =     self.classifier(self.dropout(_snake_case       )       )

								A__	  =     None
								if labels is not None:
											if self.config.problem_type is None:
														if self.num_labels == 1:
																	A__	  =     'regression'
														elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	A__	  =     'single_label_classification'
														else:
																	A__	  =     'multi_label_classification'

											if self.config.problem_type == "regression":
														A__	  =     MSELoss()
														if self.num_labels == 1:
																	A__	  =     loss_fct(logits.squeeze()					,       labels.squeeze()       )
														else:
																	A__	  =     loss_fct(_snake_case					,       _snake_case       )
											elif self.config.problem_type == "single_label_classification":
														A__	  =     CrossEntropyLoss()
														A__	  =     loss_fct(logits.view(-1					,       self.num_labels       )					,       labels.view(-1       )       )
											elif self.config.problem_type == "multi_label_classification":
														A__	  =     BCEWithLogitsLoss()
														A__	  =     loss_fct(_snake_case					,       _snake_case       )

								if not return_dict:
											A__	  =     (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					,       )









import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    'down_block_types': down_block_types,
			    'block_out_channels': block_out_channels,
			    'up_block_types': up_block_types,
			    'layers_per_block': 1,
			    'use_timestep_embedding': True,
			    'out_block_type': 'OutConv1DBlock',
			    'norm_num_groups': 8,
			    'downsample_each_block': False,
			    'in_channels': 14,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'sample_size': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''       )
			with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    'in_channels': 14,
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': (),
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': (32, 64, 128, 256),
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'sample_size': 65_536,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'norm_num_groups': 8,
			    'act_fn': 'mish',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

			A__	  =     dict(zip(state_dict.keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'       )
			with open('hub/hopper-medium-v2/value_function/config.json'			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




