import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
_SCREAMING_SNAKE_CASE:						Dict    	=       logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE:						List[str]    	=       int(10_000)  # Hardcoded max length to avoid infinite loop

_SCREAMING_SNAKE_CASE:						List[str]    	=       {
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_SCREAMING_SNAKE_CASE:						str    	=       '''In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>'''






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			np.random.seed(args.seed  )
			torch.manual_seed(args.seed  )
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			if args.temperature > 0.7:
						logger.info("CTRL typically works better with lower temperatures (and lower top_k)."  )

			_snake_case              =      tokenizer.encode(lowerCamelCase_							,	add_special_tokens=lowerCamelCase_  )
			if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()  ):
						logger.info("WARNING! You are not starting your generation from a control code so you won't get good results"  )
			return prompt_text






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      hasattr(model.config							,	"use_lang_emb"  ) and model.config.use_lang_emb
			if hasattr(model.config							,	"lang2id"  ) and use_lang_emb:
						_snake_case              =      model.config.langaid.keys()
						if args.xlm_language in available_languages:
									_snake_case              =      args.xlm_language
						else:
									_snake_case              =      None
									while language not in available_languages:
												_snake_case              =      input("Using XLM. Select language in " + str(list(lowerCamelCase_  )  ) + " >>> "  )

						_snake_case              =      model.config.langaid[language]
						# kwargs["language"] = tokenizer.lang2id[language]

			# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
			# XLM masked-language modeling (MLM) models need masked token
			# is_xlm_mlm = "mlm" in args.model_name_or_path
			# if is_xlm_mlm:
			#     kwargs["mask_token_id"] = tokenizer.mask_token_id

			return prompt_text






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			_snake_case              =      prefix + prompt_text
			return prompt_text






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			_snake_case              =      prefix + prompt_text
			return prompt_text


_SCREAMING_SNAKE_CASE:						Any    	=       {
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			if length < 0 and max_sequence_length > 0:
						_snake_case              =      max_sequence_length
			elif 0 < max_sequence_length < length:
						_snake_case              =      max_sequence_length  # No generation bigger than model size
			elif length < 0:
						_snake_case              =      MAX_LENGTH  # avoid infinite loop
			return length






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      None
			if hasattr(lowerCamelCase_							,	"hidden_size"  ):
						_snake_case              =      model_config.hidden_size
			elif hasattr(lowerCamelCase_							,	"n_embed"  ):
						_snake_case              =      model_config.n_embed
			elif hasattr(lowerCamelCase_							,	"n_embd"  ):
						_snake_case              =      model_config.n_embd

			_snake_case              =      None
			if hasattr(lowerCamelCase_							,	"num_attention_heads"  ):
						_snake_case              =      model_config.num_attention_heads
			elif hasattr(lowerCamelCase_							,	"n_head"  ):
						_snake_case              =      model_config.n_head

			if embedding_size is None or num_head is None or num_head == 0:
						raise ValueError("Check the model config"  )

			_snake_case              =      int(embedding_size / num_head  )
			if hasattr(lowerCamelCase_							,	"n_layer"  ):
						_snake_case              =      model_config.n_layer
			elif hasattr(lowerCamelCase_							,	"num_hidden_layers"  ):
						_snake_case              =      model_config.num_hidden_layers
			else:
						raise ValueError("Number of hidden layers couldn't be determined from the model config"  )

			return num_layer, num_head, num_embedding_size_per_head






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      sparse_model_config(model.config  )
			if model.config.model_type == "bloom":
						_snake_case              =      tuple(
						    (
						        torch.empty(int(num_attention_heads * batch_size  )							,	lowerCamelCase_							,	lowerCamelCase_  )
						        .to(model.dtype  )
						        .to(model.device  ),
						        torch.empty(int(num_attention_heads * batch_size  )							,	lowerCamelCase_							,	lowerCamelCase_  )
						        .to(model.dtype  )
						        .to(model.device  ),
						    )
						    for _ in range(lowerCamelCase_  )  )
			else:
						_snake_case              =      tuple(
						    (
						        torch.empty(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )
						        .to(model.dtype  )
						        .to(model.device  ),
						        torch.empty(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )
						        .to(model.dtype  )
						        .to(model.device  ),
						    )
						    for _ in range(lowerCamelCase_  )  )
			return past_key_values






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      len(lowerCamelCase_  )
			_snake_case              =      tokenizer.batch_encode_plus(lowerCamelCase_							,	return_tensors="pt"  )
			_snake_case              =      dummy_input.to(model.device  )
			if model.config.use_cache:
						_snake_case              =      generate_past_key_values(lowerCamelCase_							,	lowerCamelCase_							,	1  )
			_snake_case              =      torch.cat(
			    [
			        torch.zeros(dummy_input["attention_mask"].shape[0]							,	1  )
			        .to(dummy_input["attention_mask"].dtype  )
			        .to(model.device  ),
			        dummy_input["attention_mask"],
			    ]							,	-1							,	)
			return dummy_input

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      ('''_optimized''', '''_default''')



		def __init__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      optimized
					_snake_case              =      default



		def __call__(			self    :				List[Any]  ,					*__SCREAMING_SNAKE_CASE    :				str  ,					**__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					if kwargs["past_key_values"] is None and self._default.config.use_cache:
								_snake_case              =      generate_past_key_values(self._default  ,					kwargs["input_ids"].shape[0]  ,					0   )
					kwargs.pop("position_ids"  ,					__SCREAMING_SNAKE_CASE   )
					for k in list(kwargs.keys()   ):
								if kwargs[k] is None or isinstance(kwargs[k]  ,					__SCREAMING_SNAKE_CASE   ):
											kwargs.pop(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._optimized(**__SCREAMING_SNAKE_CASE   )
					_snake_case              =      outputs[0]
					_snake_case              =      outputs[1]
					_snake_case              =      CausalLMOutputWithPast(
					    loss=__SCREAMING_SNAKE_CASE  ,					logits=__SCREAMING_SNAKE_CASE  ,					past_key_values=__SCREAMING_SNAKE_CASE  ,					hidden_states=__SCREAMING_SNAKE_CASE  ,					attentions=__SCREAMING_SNAKE_CASE  ,					)
					return fixed_output



		def __getattr__(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					return getattr(self._default  ,					__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					**__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					return self._default.prepare_inputs_for_generation(
					    __SCREAMING_SNAKE_CASE  ,					past_key_values=__SCREAMING_SNAKE_CASE  ,					inputs_embeds=__SCREAMING_SNAKE_CASE  ,					use_cache=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )







		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple[Tuple[torch.Tensor]]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					return self._default._reorder_cache(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      argparse.ArgumentParser()
			parser.add_argument(
			    "--model_type"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()  )							,	)
			parser.add_argument(
			    "--model_name_or_path"							,	default=lowerCamelCase_							,	type=lowerCamelCase_							,	required=lowerCamelCase_							,	help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()  )							,	)

			parser.add_argument("--prompt"							,	type=lowerCamelCase_							,	default=""  )
			parser.add_argument("--length"							,	type=lowerCamelCase_							,	default=20  )
			parser.add_argument("--stop_token"							,	type=lowerCamelCase_							,	default=lowerCamelCase_							,	help="Token at which text generation is stopped"  )

			parser.add_argument(
			    "--temperature"							,	type=lowerCamelCase_							,	default=1.0							,	help="temperature of 1.0 has no effect, lower tend toward greedy sampling"							,	)
			parser.add_argument(
			    "--repetition_penalty"							,	type=lowerCamelCase_							,	default=1.0							,	help="primarily useful for CTRL model; in that case, use 1.2"  )
			parser.add_argument("--k"							,	type=lowerCamelCase_							,	default=0  )
			parser.add_argument("--p"							,	type=lowerCamelCase_							,	default=0.9  )

			parser.add_argument("--prefix"							,	type=lowerCamelCase_							,	default=""							,	help="Text added prior to input."  )
			parser.add_argument("--padding_text"							,	type=lowerCamelCase_							,	default=""							,	help="Deprecated, the use of `--prefix` is preferred."  )
			parser.add_argument("--xlm_language"							,	type=lowerCamelCase_							,	default=""							,	help="Optional language when used with the XLM model."  )

			parser.add_argument("--seed"							,	type=lowerCamelCase_							,	default=42							,	help="random seed for initialization"  )
			parser.add_argument("--no_cuda"							,	action="store_true"							,	help="Avoid using CUDA when available"  )
			parser.add_argument("--num_return_sequences"							,	type=lowerCamelCase_							,	default=1							,	help="The number of samples to generate."  )
			parser.add_argument(
			    "--fp16"							,	action="store_true"							,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"							,	)
			parser.add_argument("--jit"							,	action="store_true"							,	help="Whether or not to use jit trace to accelerate inference"  )
			_snake_case              =      parser.parse_args()

			_snake_case              =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"  )
			_snake_case              =      0 if args.no_cuda else torch.cuda.device_count()

			logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'  )

			set_seed(lowerCamelCase_  )

			# Initialize the model and tokenizer
			try:
						_snake_case              =      args.model_type.lower()
						_snake_case       , _snake_case              =      MODEL_CLASSES[args.model_type]
			except KeyError:
						raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)"  )

			_snake_case              =      tokenizer_class.from_pretrained(args.model_name_or_path  )
			if tokenizer.pad_token is None:
						_snake_case              =      tokenizer.eos_token
			_snake_case              =      model_class.from_pretrained(args.model_name_or_path  )
			model.to(args.device  )

			if args.fpaa:
						model.half()
			_snake_case              =      getattr(model.config							,	"max_position_embeddings"							,	0  )
			_snake_case              =      adjust_length_to_model(args.length							,	max_sequence_length=lowerCamelCase_  )
			logger.info(lowerCamelCase_  )

			_snake_case              =      args.prompt if args.prompt else input("Model prompt >>> "  )

			# Different models need different input formatting and/or extra arguments
			_snake_case              =      args.model_type in PREPROCESSING_FUNCTIONS.keys()
			if requires_preprocessing:
						_snake_case              =      PREPROCESSING_FUNCTIONS.get(args.model_type  )
						_snake_case              =      prepare_input(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

						if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
									_snake_case              =      {"add_space_before_punct_symbol": True}
						else:
									_snake_case              =      {}

						_snake_case              =      tokenizer.encode(
						    lowerCamelCase_							,	add_special_tokens=lowerCamelCase_							,	return_tensors="pt"							,	**lowerCamelCase_  )
			else:
						_snake_case              =      args.prefix if args.prefix else args.padding_text
						_snake_case              =      tokenizer.encode(prefix + prompt_text							,	add_special_tokens=lowerCamelCase_							,	return_tensors="pt"  )
			_snake_case              =      encoded_prompt.to(args.device  )

			if encoded_prompt.size()[-1] == 0:
						_snake_case              =      None
			else:
						_snake_case              =      encoded_prompt

			if args.jit:
						_snake_case              =      ["enable jit"]
						_snake_case              =      prepare_jit_inputs(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )
						torch._C._jit_set_texpr_fuser_enabled(lowerCamelCase_  )
						_snake_case              =      False
						if hasattr(lowerCamelCase_							,	"forward"  ):
									_snake_case              =      inspect.signature(model.forward  )
						else:
									_snake_case              =      inspect.signature(model.__call__  )
						_snake_case              =      tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowerCamelCase_							,	lowerCamelCase_  ) is not None  )
						_snake_case              =      torch.jit.trace(lowerCamelCase_							,	lowerCamelCase_							,	strict=lowerCamelCase_  )
						_snake_case              =      torch.jit.freeze(traced_model.eval()  )
						traced_model(*lowerCamelCase_  )
						traced_model(*lowerCamelCase_  )

						_snake_case              =      _ModelFallbackWrapper(lowerCamelCase_							,	lowerCamelCase_  )

			_snake_case              =      model.generate(
			    input_ids=lowerCamelCase_							,	max_length=args.length + len(encoded_prompt[0]  )							,	temperature=args.temperature							,	top_k=args.k							,	top_p=args.p							,	repetition_penalty=args.repetition_penalty							,	do_sample=lowerCamelCase_							,	num_return_sequences=args.num_return_sequences							,	)

			# Remove the batch dimension when returning multiple sequences
			if len(output_sequences.shape  ) > 2:
						output_sequences.squeeze_()

			_snake_case              =      []

			for generated_sequence_idx, generated_sequence in enumerate(lowerCamelCase_  ):
						print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='  )
						_snake_case              =      generated_sequence.tolist()

						# Decode text
						_snake_case              =      tokenizer.decode(lowerCamelCase_							,	clean_up_tokenization_spaces=lowerCamelCase_  )

						# Remove all text after the stop token
						_snake_case              =      text[: text.find(args.stop_token  ) if args.stop_token else None]

						# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
						_snake_case              =      (
						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]							,	clean_up_tokenization_spaces=lowerCamelCase_  )  ) :]
						)

						generated_sequences.append(lowerCamelCase_  )
						print(lowerCamelCase_  )

			return generated_sequences


if __name__ == "__main__":
			main()


import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      key.shape[-3:]
			_snake_case              =      value.shape[-1]
			_snake_case              =      min(lowerCamelCase_							,	lowerCamelCase_  )
			_snake_case              =      query / jnp.sqrt(lowerCamelCase_  )

			@functools.partial(jax.checkpoint							,	prevent_cse=lowerCamelCase_  )
			def summarize_chunk(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):
						_snake_case              =      jnp.einsum("...qhd,...khd->...qhk"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )

						_snake_case              =      jnp.max(lowerCamelCase_							,	axis=-1							,	keepdims=lowerCamelCase_  )
						_snake_case              =      jax.lax.stop_gradient(lowerCamelCase_  )
						_snake_case              =      jnp.exp(attn_weights - max_score  )

						_snake_case              =      jnp.einsum("...vhf,...qhv->...qhf"							,	lowerCamelCase_							,	lowerCamelCase_							,	precision=lowerCamelCase_  )
						_snake_case              =      jnp.einsum("...qhk->...qh"							,	lowerCamelCase_  )

						return (exp_values, exp_weights.sum(axis=-1  ), max_score)

			def chunk_scanner(lowerCamelCase_  ):
						# julienne key array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]							,	slice_sizes=list(key.shape[:-3]  ) + [key_chunk_size, num_heads, k_features]							,	)

						# julienne value array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]							,	slice_sizes=list(value.shape[:-3]  ) + [key_chunk_size, num_heads, v_features]							,	)

						return summarize_chunk(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			_snake_case       , _snake_case       , _snake_case              =      jax.lax.map(f=lowerCamelCase_							,	xs=jnp.arange(0							,	lowerCamelCase_							,	lowerCamelCase_  )  )

			_snake_case              =      jnp.max(lowerCamelCase_							,	axis=0							,	keepdims=lowerCamelCase_  )
			_snake_case              =      jnp.exp(chunk_max - global_max  )

			chunk_values *= jnp.expand_dims(lowerCamelCase_							,	axis=-1  )
			chunk_weights *= max_diffs

			_snake_case              =      chunk_values.sum(axis=0  )
			_snake_case              =      jnp.expand_dims(lowerCamelCase_							,	-1  ).sum(axis=0  )

			return all_values / all_weights






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=jax.lax.Precision.HIGHEST							,	lowerCamelCase_ = 1024							,	lowerCamelCase_ = 4096  ):



			"""simple docstring"""





			_snake_case       , _snake_case       , _snake_case              =      query.shape[-3:]

			def chunk_scanner(lowerCamelCase_							,	lowerCamelCase_  ):
						# julienne query array
						_snake_case              =      jax.lax.dynamic_slice(
						    operand=lowerCamelCase_							,	start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]							,	slice_sizes=list(query.shape[:-3]  ) + [min(lowerCamelCase_							,	lowerCamelCase_  ), num_heads, q_features]							,	)

						return (
						    chunk_idx + query_chunk_size,  # unused ignore it
						    _query_chunk_attention(
						        query=lowerCamelCase_							,	key=lowerCamelCase_							,	value=lowerCamelCase_							,	precision=lowerCamelCase_							,	key_chunk_size=lowerCamelCase_  ),
						)

			_snake_case       , _snake_case              =      jax.lax.scan(
			    f=lowerCamelCase_							,	init=0							,	xs=lowerCamelCase_							,	length=math.ceil(num_q / query_chunk_size  )  )

			return jnp.concatenate(lowerCamelCase_							,	axis=-3  )  # fuse the chunked result back

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      8
		A_						 =      64
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim_head * self.heads
					_snake_case              =      self.dim_head**-0.5

					# Weights were exported with old names {to_q, to_k, to_v, to_out}
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_q"   )
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_k"   )
					_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					use_bias=__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype  ,					name="to_v"   )

					_snake_case              =      nn.Dense(self.query_dim  ,					dtype=self.dtype  ,					name="to_out_0"   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )



		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size * head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim // head_size   )
					return tensor



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case              =      tensor.shape
					_snake_case              =      self.heads
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      jnp.transpose(__SCREAMING_SNAKE_CASE  ,					(0, 2, 1, 3)   )
					_snake_case              =      tensor.reshape(batch_size // head_size  ,					__SCREAMING_SNAKE_CASE  ,					dim * head_size   )
					return tensor







		def __call__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      hidden_states if context is None else context

					_snake_case              =      self.query(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.key(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.value(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE   )

					if self.use_memory_efficient_attention:
								_snake_case              =      query_states.transpose(1  ,					0  ,					2   )
								_snake_case              =      key_states.transpose(1  ,					0  ,					2   )
								_snake_case              =      value_states.transpose(1  ,					0  ,					2   )

								# this if statement create a chunk size for each layer of the unet
								# the chunk size is equal to the query_length dimension of the deepest layer of the unet

								_snake_case              =      query_states.shape[-3]
								if flatten_latent_dim % 64 == 0:
											_snake_case              =      int(flatten_latent_dim / 64   )
								elif flatten_latent_dim % 16 == 0:
											_snake_case              =      int(flatten_latent_dim / 16   )
								elif flatten_latent_dim % 4 == 0:
											_snake_case              =      int(flatten_latent_dim / 4   )
								else:
											_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

								_snake_case              =      jax_memory_efficient_attention(
								    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					query_chunk_size=__SCREAMING_SNAKE_CASE  ,					key_chunk_size=4_096 * 4   )

								_snake_case              =      hidden_states.transpose(1  ,					0  ,					2   )
					else:
								# compute attentions
								_snake_case              =      jnp.einsum("b i d, b j d->b i j"  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      attention_scores * self.scale
								_snake_case              =      nn.softmax(__SCREAMING_SNAKE_CASE  ,					axis=2   )

								# attend to values
								_snake_case              =      jnp.einsum("b i j, b j d -> b i d"  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.reshape_batch_dim_to_heads(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.proj_attn(__SCREAMING_SNAKE_CASE   )
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      0.0
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					# self attention (or cross_attention if only_cross_attention is True)
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					# cross attention
					_snake_case              =      FlaxAttention(
					    self.dim  ,					self.n_heads  ,					self.d_head  ,					self.dropout  ,					self.use_memory_efficient_attention  ,					dtype=self.dtype   )
					_snake_case              =      FlaxFeedForward(dim=self.dim  ,					dropout=self.dropout  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.LayerNorm(epsilon=1e-5  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Any=True   ):






					"""simple docstring"""
					# self attention
					_snake_case              =      hidden_states
					if self.only_cross_attention:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# cross attention
					_snake_case              =      hidden_states
					_snake_case              =      self.attna(self.norma(__SCREAMING_SNAKE_CASE   )  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					# feed forward
					_snake_case              =      hidden_states
					_snake_case              =      self.ff(self.norma(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      hidden_states + residual

					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      42
		A_						 =      1
		A_						 =      0.0
		A_						 =      False
		A_						 =      False
		A_						 =      jnp.floataa
		A_						 =      False



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      nn.GroupNorm(num_groups=32  ,					epsilon=1e-5   )

					_snake_case              =      self.n_heads * self.d_head
					if self.use_linear_projection:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

					_snake_case              =      [
					    FlaxBasicTransformerBlock(
					        __SCREAMING_SNAKE_CASE  ,					self.n_heads  ,					self.d_head  ,					dropout=self.dropout  ,					only_cross_attention=self.only_cross_attention  ,					dtype=self.dtype  ,					use_memory_efficient_attention=self.use_memory_efficient_attention  ,					)
					    for _ in range(self.depth   )
					]

					if self.use_linear_projection:
								_snake_case              =      nn.Dense(__SCREAMING_SNAKE_CASE  ,					dtype=self.dtype   )
					else:
								_snake_case              =      nn.Conv(
								    __SCREAMING_SNAKE_CASE  ,					kernel_size=(1, 1)  ,					strides=(1, 1)  ,					padding="VALID"  ,					dtype=self.dtype  ,					)

					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True   ):






					"""simple docstring"""
					_snake_case       , _snake_case       , _snake_case       , _snake_case              =      hidden_states.shape
					_snake_case              =      hidden_states
					_snake_case              =      self.norm(__SCREAMING_SNAKE_CASE   )
					if self.use_linear_projection:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.proj_in(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					height * width  ,					__SCREAMING_SNAKE_CASE   )

					for transformer_block in self.transformer_blocks:
								_snake_case              =      transformer_block(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

					if self.use_linear_projection:
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      hidden_states.reshape(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.proj_out(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      hidden_states + residual
					return self.dropout_layer(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# The second linear layer needs to be called
					# net_2 for now to match the index of the Sequential layer
					_snake_case              =      FlaxGEGLU(self.dim  ,					self.dropout  ,					self.dtype   )
					_snake_case              =      nn.Dense(self.dim  ,					dtype=self.dtype   )







		def __call__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True   ):






					"""simple docstring"""
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE  ,					deterministic=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self.net_a(__SCREAMING_SNAKE_CASE   )
					return hidden_states

class 	_SCREAMING_SNAKE_CASE							(     nn.Module							):






		"""simple docstring"""







		A_						 =      42
		A_						 =      0.0
		A_						 =      jnp.floataa



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.dim * 4
					_snake_case              =      nn.Dense(inner_dim * 2  ,					dtype=self.dtype   )
					_snake_case              =      nn.Dropout(rate=self.dropout   )







		def __call__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True   ):






					"""simple docstring"""
					_snake_case              =      self.proj(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      jnp.split(__SCREAMING_SNAKE_CASE  ,					2  ,					axis=2   )
					return self.dropout_layer(hidden_linear * nn.gelu(__SCREAMING_SNAKE_CASE   )  ,					deterministic=__SCREAMING_SNAKE_CASE   )

