'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	:       Any						       =	logging.get_logger(__name__)

__lowerCAmelCase	:       Union[str, Any]						       =	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase	:       Optional[int]						       =	{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase	:       Optional[Any]						       =	{
    "gpt-neox-20b": 2_048,
}






class 						A   (   UpperCAmelCase   ):
							a_        =      VOCAB_FILES_NAMES
							a_        =      PRETRAINED_VOCAB_FILES_MAP
							a_        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a_        =      ['''input_ids''', '''attention_mask''']





							def __init__(      self				:							List[str]     ,		__a				:							str=None     ,		__a				:							List[str]=None     ,		__a				:							Optional[int]=None     ,		__a				:							Tuple="<|endoftext|>"     ,		__a				:							List[str]="<|endoftext|>"     ,		__a				:							Dict="<|endoftext|>"     ,		__a				:							Tuple=False     ,		**__a				:							Optional[int]     ,		)							->	Dict:
								super().__init__(
								    __a     ,		__a     ,		tokenizer_file=__a     ,		unk_token=__a     ,		bos_token=__a     ,		eos_token=__a     ,		add_prefix_space=__a     ,		**__a     ,		)

								__UpperCAmelCase							=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
								if pre_tok_state.get('''add_prefix_space'''     ,		__a	) != add_prefix_space:
									__UpperCAmelCase							=			getattr(__a     ,		pre_tok_state.pop('''type'''	)	)
									__UpperCAmelCase							=			add_prefix_space
									__UpperCAmelCase							=			pre_tok_class(**__a	)

								__UpperCAmelCase							=			add_prefix_space





							def       snake_case__						(      self				:							List[str]     ,		__a				:							str     ,		__a				:							Optional[str] = None	)							->	Tuple[str]:
								__UpperCAmelCase							=			self._tokenizer.model.save(__a     ,		name=__a	)
								return tuple(__a	)






							def       snake_case__						(      self				:							Tuple     ,		__a				:							"Conversation"	)							->	List[int]:
								__UpperCAmelCase							=			[]
								for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a     ,		add_special_tokens=__a	) + [self.eos_token_id]	)

								if len(__a	) > self.model_max_length:
									__UpperCAmelCase							=			input_ids[-self.model_max_length :]
								return input_ids

'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp






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





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							List[str]     ,		__a				:							Optional[Any]	)							->	List[Any]:
								return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__a	) for s in shape]	)}.npy"""





							def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Tuple=0     ,		__a				:							List[Any]=(4, 4, 6_4, 6_4)     ,		__a				:							Optional[Any]=False	)							->	Tuple:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return image





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]=False     ,		__a				:							Optional[Any]="CompVis/stable-diffusion-v1-4"	)							->	Any:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			'''bf16''' if fpaa else None

								__UpperCAmelCase				,			__UpperCAmelCase							=			FlaxUNetaDConditionModel.from_pretrained(
								    __a     ,		subfolder='''unet'''     ,		dtype=__a     ,		revision=__a	)
								return model, params





							def       snake_case__						(      self				:							str     ,		__a				:							int=0     ,		__a				:							Tuple=(4, 7_7, 7_6_8)     ,		__a				:							Optional[int]=False	)							->	Union[str, Any]:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return hidden_states





							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
							        [1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
							        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
							        [3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Tuple     ,		__a				:							Tuple     ,		__a				:							str     ,		__a				:							Optional[Any]	)							->	Any:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
							        [1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
							        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
							        [3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		shape=(4, 4, 9_6, 9_6)     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		shape=(4, 7_7, 1_0_2_4)     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)
