
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

__A				={
    '''yjernite/retribert-base-uncased''': 5_1_2,
}


__A				={
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = RetriBertTokenizer
	lowerCAmelCase__         = ['input_ids', 'attention_mask']



	def __init__(    self						,  lowercase=None						,  lowercase=None						,  lowercase=True						,  lowercase="[UNK]"						,  lowercase="[SEP]"						,  lowercase="[PAD]"						,  lowercase="[CLS]"						,  lowercase="[MASK]"						,  lowercase=True						,  lowercase=None						,  **lowercase						,  )				->       List[Any]:
					super().__init__(
					    lowercase						,  tokenizer_file=lowercase						,  do_lower_case=lowercase						,  unk_token=lowercase						,  sep_token=lowercase						,  pad_token=lowercase						,  cls_token=lowercase						,  mask_token=lowercase						,  tokenize_chinese_chars=lowercase						,  strip_accents=lowercase						,  **lowercase						,  )

					lowerCamelCase_						=			json.loads(self.backend_tokenizer.normalizer.__getstate__() )
					if (
					    normalizer_state.get("lowercase"						,  lowercase ) != do_lower_case
					    or normalizer_state.get("strip_accents"						,  lowercase ) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"						,  lowercase ) != tokenize_chinese_chars
					):
									lowerCamelCase_						=			getattr(lowercase						,  normalizer_state.pop("type" ) )
									lowerCamelCase_						=			do_lower_case
									lowerCamelCase_						=			strip_accents
									lowerCamelCase_						=			tokenize_chinese_chars
									lowerCamelCase_						=			normalizer_class(**lowercase )

					lowerCamelCase_						=			do_lower_case



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=None )				->       int:
					lowerCamelCase_						=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

					return output



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]
					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					lowerCamelCase_						=			self._tokenizer.model.save(lowercase						,  name=lowercase )
					return tuple(lowercase )







"""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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





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






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :List[str]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-canny"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :Union[str, Any]						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :Union[str, Any]						=			controlnet_params

											snake_case_ :Union[str, Any]						=			"""bird"""
											snake_case_ :List[Any]						=			jax.device_count()
											snake_case_ :List[Any]						=			pipe.prepare_text_inputs([prompts] * num_samples						)

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

											snake_case_ :Any						=			jax.random.PRNGKey(0						)
											snake_case_ :List[str]						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :List[Any]						=			replicate(snake_case						)
											snake_case_ :List[str]						=			shard(snake_case						)
											snake_case_ :str						=			shard(snake_case						)

											snake_case_ :Dict						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :Union[str, Any]						=			images[0, 253:256, 253:256, -1]

											snake_case_ :str						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Dict						=			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    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_,       snake_case_ :List[Any]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :int						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :str						=			controlnet_params

											snake_case_ :Optional[int]						=			"""Chef in the kitchen"""
											snake_case_ :Union[str, Any]						=			jax.device_count()
											snake_case_ :Any						=			pipe.prepare_text_inputs([prompts] * num_samples						)

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

											snake_case_ :str						=			jax.random.PRNGKey(0						)
											snake_case_ :str						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :Tuple						=			replicate(snake_case						)
											snake_case_ :str						=			shard(snake_case						)
											snake_case_ :int						=			shard(snake_case						)

											snake_case_ :List[str]						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

											snake_case_ :Dict						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Optional[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
