

"""simple docstring"""




import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE_     :			str	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			{
    '''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
    '''Salesforce/blip-vqa-capfit-large''': (
        '''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-base''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-large''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
    ),
    '''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-flikr''': (
        '''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
    ),
}


class 		_A       (			__a					):
      __a								=	'blip_text_model'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=30524    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=3072    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=1e-12    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=30522    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=102    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__    ,						bos_token_id=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__    ,						sep_token_id=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)

         lowerCamelCase__						=			vocab_size
         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			encoder_hidden_size
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			projection_dim
         lowerCamelCase__						=			hidden_dropout_prob
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			max_position_embeddings
         lowerCamelCase__						=			layer_norm_eps
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			attention_probs_dropout_prob
         lowerCamelCase__						=			is_decoder
         lowerCamelCase__						=			use_cache





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__					,       lowerCamelCase__						=			cls.get_config_dict(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         # get the text config dict if we are loading from BlipConfig
         if config_dict.get("model_type"		) == "blip":
            lowerCamelCase__						=			config_dict["text_config"]

         if "model_type" in config_dict and hasattr(cls    ,						"model_type"		) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

         return cls.from_dict(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)






class 		_A       (			__a					):
      __a								=	'blip_vision_model'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=3072    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=384    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=1e-10    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         super().__init__(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			projection_dim
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			patch_size
         lowerCamelCase__						=			image_size
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			attention_dropout
         lowerCamelCase__						=			layer_norm_eps
         lowerCamelCase__						=			hidden_act





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__					,       lowerCamelCase__						=			cls.get_config_dict(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         # get the vision config dict if we are loading from BlipConfig
         if config_dict.get("model_type"		) == "blip":
            lowerCamelCase__						=			config_dict["vision_config"]

         if "model_type" in config_dict and hasattr(cls    ,						"model_type"		) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

         return cls.from_dict(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)






class 		_A       (			__a					):
      __a								=	'blip'
      __a								=	True





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__=2.65_92    ,						SCREAMING_SNAKE_CASE__=256    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					str:
         super().__init__(**SCREAMING_SNAKE_CASE__		)

         if text_config is None:
            lowerCamelCase__						=			{}
            logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."		)

         if vision_config is None:
            lowerCamelCase__						=			{}
            logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."		)

         lowerCamelCase__						=			BlipTextConfig(**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			BlipVisionConfig(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			self.vision_config.hidden_size

         lowerCamelCase__						=			projection_dim
         lowerCamelCase__						=			logit_scale_init_value
         lowerCamelCase__						=			1.0
         lowerCamelCase__						=			0.02
         lowerCamelCase__						=			image_text_hidden_size





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict:
         return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.text_config.to_dict()
         lowerCamelCase__						=			self.vision_config.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output







"""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 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

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

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




