





import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""input_ids""", """attention_mask"""]
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : List[Any]="</s>"     ,	UpperCAmelCase    : Optional[int]="<unk>"     ,	UpperCAmelCase    : Tuple="<pad>"     ,	UpperCAmelCase    : Dict=125     ,	UpperCAmelCase    : Tuple=None     ,	**UpperCAmelCase    : Any     ,	)     ->      None:
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
         lowerCamelCase__       : Dict   					=						[F"""<extra_id_{i}>""" for i in range(UpperCAmelCase      )]
     elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra_id special tokens
         lowerCamelCase__       : List[str]   					=						len(set(filter(lambda UpperCAmelCase      : bool('extra_id' in str(UpperCAmelCase      )      )     ,	UpperCAmelCase      )      )      )
         if extra_tokens != extra_ids:
             raise ValueError(
                 F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                 ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                 ' extra_ids tokens'      )

     lowerCamelCase__       : Tuple   					=						AddedToken(UpperCAmelCase     ,	lstrip=UpperCAmelCase     ,	rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else pad_token
     lowerCamelCase__       : Optional[int]   					=						AddedToken(UpperCAmelCase     ,	lstrip=UpperCAmelCase     ,	rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else eos_token
     lowerCamelCase__       : Union[str, Any]   					=						AddedToken(UpperCAmelCase     ,	lstrip=UpperCAmelCase     ,	rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else unk_token

     super().__init__(
         eos_token=UpperCAmelCase     ,	unk_token=UpperCAmelCase     ,	pad_token=UpperCAmelCase     ,	extra_ids=UpperCAmelCase     ,	additional_special_tokens=UpperCAmelCase     ,	**UpperCAmelCase     ,	)

     lowerCamelCase__       : Any   					=						extra_ids

     lowerCamelCase__       : int   					=						2**8  # utf is 8 bits

     # define special tokens dict
     lowerCamelCase__       : Dict[int, str]   					=						{
         self.pad_token: 0,
         self.eos_token: 1,
         self.unk_token: 2,
     }
     lowerCamelCase__       : str   					=						len(self.special_tokens_encoder      )
     lowerCamelCase__       : int   					=						len(UpperCAmelCase      )
     for i, token in enumerate(UpperCAmelCase      ):
         lowerCamelCase__       : List[str]   					=						self.vocab_size + i - n
     lowerCamelCase__       : Dict[str, int]   					=						{v: k for k, v in self.special_tokens_encoder.items()}
 @property
 def 	A_      ( self    : Tuple      )     ->      List[str]:
     return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : Optional[List[int]] = None     ,	UpperCAmelCase    : bool = False      )     ->      List[int]:
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase     ,	token_ids_a=UpperCAmelCase     ,	already_has_special_tokens=UpperCAmelCase      )

     # normal case: some special tokens
     if token_ids_a is None:
         return ([0] * len(UpperCAmelCase      )) + [1]
     return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : List[int]      )     ->      List[int]:
     if len(UpperCAmelCase      ) > 0 and token_ids[-1] == self.eos_token_id:
         warnings.warn(
             F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
             ' eos tokens being added.'      )
         return token_ids
     else:
         return token_ids + [self.eos_token_id]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : Optional[List[int]] = None      )     ->      List[int]:
     lowerCamelCase__       : str   					=						[self.eos_token_id]

     if token_ids_a is None:
         return len(token_ids_a + eos      ) * [0]
     return len(token_ids_a + eos + token_ids_a + eos      ) * [0]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : Optional[List[int]] = None      )     ->      List[int]:
     lowerCamelCase__       : str   					=						self._add_eos_if_not_present(UpperCAmelCase      )
     if token_ids_a is None:
         return token_ids_a
     else:
         lowerCamelCase__       : List[str]   					=						self._add_eos_if_not_present(UpperCAmelCase      )
         return token_ids_a + token_ids_a
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      List[str]:
     lowerCamelCase__       : Optional[int]   					=						[chr(UpperCAmelCase      ) for i in text.encode('utf-8'      )]
     return tokens
 def 	A_      ( self    : str     ,	UpperCAmelCase    : str      )     ->      int:
     if token in self.special_tokens_encoder:
         lowerCamelCase__       : Optional[int]   					=						self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
         lowerCamelCase__       : List[str]   					=						self.added_tokens_encoder[token]
     elif len(UpperCAmelCase      ) != 1:
         lowerCamelCase__       : int   					=						self.unk_token_id
     else:
         lowerCamelCase__       : List[str]   					=						ord(UpperCAmelCase      ) + self._num_special_tokens
     return token_id
 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : int      )     ->      Tuple:
     if index in self.special_tokens_decoder:
         lowerCamelCase__       : List[Any]   					=						self.special_tokens_decoder[index]
     else:
         lowerCamelCase__       : List[Any]   					=						chr(index - self._num_special_tokens      )
     return token
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : str      )     ->      int:
     lowerCamelCase__       : List[str]   					=						B''
     for token in tokens:
         if token in self.special_tokens_decoder:
             lowerCamelCase__       : Union[str, Any]   					=						self.special_tokens_decoder[token].encode('utf-8'      )
         elif token in self.added_tokens_decoder:
             lowerCamelCase__       : str   					=						self.special_tokens_decoder[token].encode('utf-8'      )
         elif token in self.special_tokens_encoder:
             lowerCamelCase__       : Tuple   					=						token.encode('utf-8'      )
         elif token in self.added_tokens_encoder:
             lowerCamelCase__       : Any   					=						token.encode('utf-8'      )
         else:
             lowerCamelCase__       : Any   					=						bytes([ord(UpperCAmelCase      )]      )
         bstring += tok_string
     lowerCamelCase__       : Dict   					=						bstring.decode('utf-8'     ,	errors='ignore'      )
     return string







 def 	A_      ( self    : str     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[str] = None      )     ->      Tuple[str]:
     return ()












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   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : Any      )     ->      int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[int]:
     lowerCamelCase__					,				lowerCamelCase__       : str   					=						FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-canny'     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__					,				lowerCamelCase__       : int   					=						FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'     ,	controlnet=UpperCAmelCase     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__       : int   					=						controlnet_params

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

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

     lowerCamelCase__       : List[str]   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : Optional[int]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : List[Any]   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						shard(UpperCAmelCase      )

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

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

     lowerCamelCase__       : Tuple   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : Tuple   					=						jnp.array(
         [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







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

     lowerCamelCase__       : Any   					=						'Chef in the kitchen'
     lowerCamelCase__       : str   					=						jax.device_count()
     lowerCamelCase__       : Union[str, Any]   					=						pipe.prepare_text_inputs([prompts] * num_samples      )

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

     lowerCamelCase__       : Tuple   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : List[str]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : Dict   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						pipe(
         prompt_ids=UpperCAmelCase     ,	image=UpperCAmelCase     ,	params=UpperCAmelCase     ,	prng_seed=UpperCAmelCase     ,	num_inference_steps=50     ,	jit=UpperCAmelCase     ,	).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

     lowerCamelCase__       : Optional[int]   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : int   					=						jnp.array(
         [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2





