"""simple docstring"""





from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "				,							__SCREAMING_SNAKE_CASE				,							)
class   UpperCAmelCase_			(					__SCREAMING_SNAKE_CASE):
      lowerCamelCase__	:					int										=						RobertaConfig
      lowerCamelCase__	:					int										=						"roberta"


      def __init__(     self	,    a							) ->      Optional[Any]:
             super().__init__(__snake_case							)

             lowercase__    :	Optional[int]     					=			RobertaEmbeddings(__snake_case							)
             self.init_weights()





@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "				,							__SCREAMING_SNAKE_CASE				,							)
class   UpperCAmelCase_			(					__SCREAMING_SNAKE_CASE):
      lowerCamelCase__	:					Any										=						RobertaConfig
      lowerCamelCase__	:					int										=						"roberta"


      def __init__(     self	,    a							) ->      Optional[Any]:
             super().__init__(__snake_case							)
             lowercase__    :	Any     					=			config.num_labels
             lowercase__    :	Union[str, Any]     					=			config.num_hidden_layers

             lowercase__    :	Optional[int]     					=			DeeRobertaModel(__snake_case							)
             lowercase__    :	Dict     					=			nn.Dropout(config.hidden_dropout_prob							)
             lowercase__    :	Optional[Any]     					=			nn.Linear(config.hidden_size	,    self.config.num_labels							)


      @add_start_docstrings_to_model_forward(__snake_case							)
      def 	_UpperCAmelCase (     self	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    a=None	,    a=-1	,    a=False	,    ) ->      Tuple:
             lowercase__    :	List[str]     					=			self.num_layers
             try:
                    lowercase__    :	Tuple     					=			self.roberta(
                        __snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    position_ids=__snake_case	,    head_mask=__snake_case	,    inputs_embeds=__snake_case	,    )

                    lowercase__    :	int     					=			outputs[1]

                    lowercase__    :	str     					=			self.dropout(__snake_case							)
                    lowercase__    :	List[Any]     					=			self.classifier(__snake_case							)
                    lowercase__    :	Optional[int]     					=			(logits,) + outputs[2:]  # add hidden states and attention if they are here
             except HighwayException as e:
                    lowercase__    :	Union[str, Any]     					=			e.message
                    lowercase__    :	List[Any]     					=			e.exit_layer
                    lowercase__    :	Union[str, Any]     					=			outputs[0]

             if not self.training:
                    lowercase__    :	List[str]     					=			entropy(__snake_case							)
                    lowercase__    :	Union[str, Any]     					=			[]
                    lowercase__    :	Dict     					=			[]
             if labels is not None:
                    if self.num_labels == 1:
                           #  We are doing regression
                           lowercase__    :	List[str]     					=			MSELoss()
                           lowercase__    :	Tuple     					=			loss_fct(logits.view(-1							)	,    labels.view(-1							)							)
                    else:
                           lowercase__    :	List[str]     					=			CrossEntropyLoss()
                           lowercase__    :	Dict     					=			loss_fct(logits.view(-1	,    self.num_labels							)	,    labels.view(-1							)							)

                    # work with highway exits
                    lowercase__    :	List[Any]     					=			[]
                    for highway_exit in outputs[-1]:
                           lowercase__    :	Optional[int]     					=			highway_exit[0]
                           if not self.training:
                                  highway_logits_all.append(__snake_case							)
                                  highway_entropy.append(highway_exit[2]							)
                           if self.num_labels == 1:
                                  #  We are doing regression
                                  lowercase__    :	Dict     					=			MSELoss()
                                  lowercase__    :	int     					=			loss_fct(highway_logits.view(-1							)	,    labels.view(-1							)							)
                           else:
                                  lowercase__    :	int     					=			CrossEntropyLoss()
                                  lowercase__    :	List[Any]     					=			loss_fct(highway_logits.view(-1	,    self.num_labels							)	,    labels.view(-1							)							)
                           highway_losses.append(__snake_case							)

                    if train_highway:
                           lowercase__    :	Dict     					=			(sum(highway_losses[:-1]							),) + outputs
                           # exclude the final highway, of course
                    else:
                           lowercase__    :	Union[str, Any]     					=			(loss,) + outputs
             if not self.training:
                    lowercase__    :	List[str]     					=			outputs + ((original_entropy, highway_entropy), exit_layer)
                    if output_layer >= 0:
                           lowercase__    :	Union[str, Any]     					=			(
                               (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                           )  # use the highway of the last layer

             return outputs  # (loss), logits, (hidden_states), (attentions), entropy







"""simple docstring"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

