'''simple docstring'''







from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


_a      :				List[str]											=					logging.get_logger(__name__)





class 					_UpperCAmelCase							(		nn.Module     ):
		a     :     HybridCLIPConfig
		a     :     jnp.dtype     						=jnp.floataa
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       self.config.text_config
					__lowerCAmelCase      		=       self.config.vision_config

					__lowerCAmelCase      		=       self.config.projection_dim
					__lowerCAmelCase      		=       text_config.hidden_size
					__lowerCAmelCase      		=       vision_config.hidden_size

					__lowerCAmelCase      		=       FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
					__lowerCAmelCase      		=       FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__,__SCREAMING_SNAKE_CASE    ).module_class

					__lowerCAmelCase      		=       text_module(__SCREAMING_SNAKE_CASE,dtype=self.dtype    )
					__lowerCAmelCase      		=       vision_module(__SCREAMING_SNAKE_CASE,dtype=self.dtype    )

					__lowerCAmelCase      		=       nn.Dense(
					    self.projection_dim,dtype=self.dtype,kernel_init=jax.nn.initializers.normal(0.02    ),use_bias=__SCREAMING_SNAKE_CASE,)
					__lowerCAmelCase      		=       nn.Dense(
					    self.projection_dim,dtype=self.dtype,kernel_init=jax.nn.initializers.normal(0.02    ),use_bias=__SCREAMING_SNAKE_CASE,)
					__lowerCAmelCase      		=       self.param("""logit_scale""",jax.nn.initializers.ones,[]    )







		def __call__(						self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):



					'''simple docstring'''

					__lowerCAmelCase      		=       return_dict if return_dict is not None else self.config.return_dict

					__lowerCAmelCase      		=       self.vision_model(
					    pixel_values=__SCREAMING_SNAKE_CASE,deterministic=__SCREAMING_SNAKE_CASE,output_attentions=__SCREAMING_SNAKE_CASE,output_hidden_states=__SCREAMING_SNAKE_CASE,return_dict=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase      		=       self.text_model(
					    input_ids=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,position_ids=__SCREAMING_SNAKE_CASE,deterministic=__SCREAMING_SNAKE_CASE,output_attentions=__SCREAMING_SNAKE_CASE,output_hidden_states=__SCREAMING_SNAKE_CASE,return_dict=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase      		=       vision_outputs[1]
					__lowerCAmelCase      		=       self.visual_projection(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       text_outputs[1]
					__lowerCAmelCase      		=       self.text_projection(__SCREAMING_SNAKE_CASE    )

					# normalized features
					__lowerCAmelCase      		=       image_embeds / jnp.linalg.norm(__SCREAMING_SNAKE_CASE,axis=-1,keepdims=__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       text_embeds / jnp.linalg.norm(__SCREAMING_SNAKE_CASE,axis=-1,keepdims=__SCREAMING_SNAKE_CASE    )

					# cosine similarity as logits
					__lowerCAmelCase      		=       jnp.exp(self.logit_scale    )
					__lowerCAmelCase      		=       jnp.matmul(__SCREAMING_SNAKE_CASE,image_embeds.T    ) * logit_scale
					__lowerCAmelCase      		=       logits_per_text.T

					if not return_dict:
								return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

					return FlaxCLIPOutput(
					    logits_per_image=__SCREAMING_SNAKE_CASE,logits_per_text=__SCREAMING_SNAKE_CASE,text_embeds=__SCREAMING_SNAKE_CASE,image_embeds=__SCREAMING_SNAKE_CASE,text_model_output=__SCREAMING_SNAKE_CASE,vision_model_output=__SCREAMING_SNAKE_CASE,)





class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		a     :     Optional[Any]     						=HybridCLIPConfig
		a     :     Optional[Any]     						=FlaxHybridCLIPModule
		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = 0,__SCREAMING_SNAKE_CASE = jnp.floataa,**__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					if input_shape is None:
								__lowerCAmelCase      		=       ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

					__lowerCAmelCase      		=       self.module_class(config=__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
					super().__init__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_shape=__SCREAMING_SNAKE_CASE,seed=__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       jnp.zeros(input_shape[0],dtype="""i4"""    )
					__lowerCAmelCase      		=       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE    ).shape[-1]    ),input_shape[0]    )
					__lowerCAmelCase      		=       jnp.ones_like(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       jnp.ones_like(__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       jax.random.normal(__SCREAMING_SNAKE_CASE,input_shape[1]    )

					__lowerCAmelCase		, __lowerCAmelCase      		=       jax.random.split(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       {"""params""": params_rng, """dropout""": dropout_rng}

					return self.module.init(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )["params"]
		def __call__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,):



					'''simple docstring'''

					__lowerCAmelCase      		=       output_attentions if output_attentions is not None else self.config.output_attentions
					__lowerCAmelCase      		=       (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__lowerCAmelCase      		=       return_dict if return_dict is not None else self.config.return_dict

					if position_ids is None:
								__lowerCAmelCase      		=       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE    ).shape[-1]    ),input_ids.shape    )

					if token_type_ids is None:
								__lowerCAmelCase      		=       jnp.zeros_like(__SCREAMING_SNAKE_CASE    )

					if attention_mask is None:
								__lowerCAmelCase      		=       jnp.ones_like(__SCREAMING_SNAKE_CASE    )

					# Handle any PRNG if needed
					__lowerCAmelCase      		=       {}
					if dropout_rng is not None:
								__lowerCAmelCase      		=       dropout_rng

					return self.module.apply(
					    {"""params""": params or self.params},jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype=jnp.floataa    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),not train,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,rngs=__SCREAMING_SNAKE_CASE,)
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE=False,):



					'''simple docstring'''

					if position_ids is None:
								__lowerCAmelCase      		=       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE    ).shape[-1]    ),input_ids.shape    )

					if token_type_ids is None:
								__lowerCAmelCase      		=       jnp.zeros_like(__SCREAMING_SNAKE_CASE    )

					if attention_mask is None:
								__lowerCAmelCase      		=       jnp.ones_like(__SCREAMING_SNAKE_CASE    )

					# Handle any PRNG if needed
					__lowerCAmelCase      		=       {}
					if dropout_rng is not None:
								__lowerCAmelCase      		=       dropout_rng

					def _get_features(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
								__lowerCAmelCase      		=       module.text_model(
								    input_ids=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,position_ids=__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,deterministic=__SCREAMING_SNAKE_CASE,)
								__lowerCAmelCase      		=       text_outputs[1]
								__lowerCAmelCase      		=       module.text_projection(__SCREAMING_SNAKE_CASE    )
								return text_features

					return self.module.apply(
					    {"""params""": params or self.params},jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),jnp.array(__SCREAMING_SNAKE_CASE,dtype="""i4"""    ),not train,method=_get_features,rngs=__SCREAMING_SNAKE_CASE,)
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE=False    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {}
					if dropout_rng is not None:
								__lowerCAmelCase      		=       dropout_rng

					def _get_features(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
								__lowerCAmelCase      		=       module.vision_model(pixel_values=__SCREAMING_SNAKE_CASE,deterministic=__SCREAMING_SNAKE_CASE    )
								__lowerCAmelCase      		=       vision_outputs[1]  # pooled_output
								__lowerCAmelCase      		=       module.visual_projection(__SCREAMING_SNAKE_CASE    )
								return image_features

					return self.module.apply(
					    {"""params""": params or self.params},jnp.array(__SCREAMING_SNAKE_CASE,dtype=jnp.floataa    ),not train,method=_get_features,rngs=__SCREAMING_SNAKE_CASE,)







		@classmethod
		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    argument[len("""text_"""    ) :]: value for argument, value in kwargs.items() if argument.startswith("""text_"""    )
					}

					__lowerCAmelCase      		=       {
					    argument[len("""vision_"""    ) :]: value for argument, value in kwargs.items() if argument.startswith("""vision_"""    )
					}

					# remove text, vision kwargs from kwargs
					for key in kwargs_text.keys():
								del kwargs["text_" + key]
					for key in kwargs_vision.keys():
								del kwargs["vision_" + key]

					# Load and initialize the text and vision model
					__lowerCAmelCase      		=       kwargs_text.pop("""model""",__SCREAMING_SNAKE_CASE    )
					if text_model is None:
								assert (
								    text_model_name_or_path is not None
								), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
								from transformers import FlaxAutoModel

								if "config" not in kwargs_text:
											from transformers import AutoConfig

											__lowerCAmelCase      		=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
											__lowerCAmelCase      		=       text_config

								__lowerCAmelCase      		=       FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       kwargs_vision.pop("""model""",__SCREAMING_SNAKE_CASE    )
					if vision_model is None:
								assert (
								    vision_model_name_or_path is not None
								), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
								from transformers import FlaxAutoModel

								if "config" not in kwargs_vision:
											from transformers import AutoConfig

											__lowerCAmelCase      		=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
											__lowerCAmelCase      		=       vision_config

								__lowerCAmelCase      		=       FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					# instantiate config with corresponding kwargs
					__lowerCAmelCase      		=       kwargs.pop("""dtype""",jnp.floataa    )
					__lowerCAmelCase      		=       HybridCLIPConfig.from_text_vision_configs(text_model.config,vision_model.config,**__SCREAMING_SNAKE_CASE    )

					# init model
					__lowerCAmelCase      		=       cls(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					if vision_config.model_type == "clip":
								__lowerCAmelCase      		=       vision_model.params["""vision_model"""]
								__lowerCAmelCase      		=       vision_model.params["""visual_projection"""]["""kernel"""]
					else:
								__lowerCAmelCase      		=       vision_model.params

					__lowerCAmelCase      		=       text_model.params

					return model








'''simple docstring'''







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,
)
_a      :				Optional[int]											=					logging.getLogger(__name__)

_a      :				int											=					int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

_a      :				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
_a      :				List[Any]											=					"""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      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			np.random.seed(args.seed			)
			torch.manual_seed(args.seed			)
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed			)





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Tuple:
			if args.temperature > 0.7:
						logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""			)

			__lowerCAmelCase      		=       tokenizer.encode(lowercase      ,			add_special_tokens=lowercase			)
			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      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
			# kwargs = {"language": None, "mask_token_id": None}

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

						__lowerCAmelCase      		=       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      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Dict:
			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			__lowerCAmelCase      		=       prefix + prompt_text
			return prompt_text





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
			__lowerCAmelCase      		=       prefix + prompt_text
			return prompt_text


_a      :				List[Any]											=					{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Union[str, Any]:
			if length < 0 and max_sequence_length > 0:
						__lowerCAmelCase      		=       max_sequence_length
			elif 0 < max_sequence_length < length:
						__lowerCAmelCase      		=       max_sequence_length  # No generation bigger than model size
			elif length < 0:
						__lowerCAmelCase      		=       MAX_LENGTH  # avoid infinite loop
			return length





def      _lowerCAmelCase		(		lowercase			)      ->      Dict:
			__lowerCAmelCase      		=       None
			if hasattr(lowercase      ,			"""hidden_size"""			):
						__lowerCAmelCase      		=       model_config.hidden_size
			elif hasattr(lowercase      ,			"""n_embed"""			):
						__lowerCAmelCase      		=       model_config.n_embed
			elif hasattr(lowercase      ,			"""n_embd"""			):
						__lowerCAmelCase      		=       model_config.n_embd

			__lowerCAmelCase      		=       None
			if hasattr(lowercase      ,			"""num_attention_heads"""			):
						__lowerCAmelCase      		=       model_config.num_attention_heads
			elif hasattr(lowercase      ,			"""n_head"""			):
						__lowerCAmelCase      		=       model_config.n_head

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

			__lowerCAmelCase      		=       int(embedding_size / num_head			)
			if hasattr(lowercase      ,			"""n_layer"""			):
						__lowerCAmelCase      		=       model_config.n_layer
			elif hasattr(lowercase      ,			"""num_hidden_layers"""			):
						__lowerCAmelCase      		=       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      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Optional[Any]:
			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       sparse_model_config(model.config			)
			if model.config.model_type == "bloom":
						__lowerCAmelCase      		=       tuple(
						    (
						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						    )
						    for _ in range(lowercase			)			)
			else:
						__lowerCAmelCase      		=       tuple(
						    (
						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
						        .to(model.dtype			)
						        .to(model.device			),
						    )
						    for _ in range(lowercase			)			)
			return past_key_values





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       len(lowercase			)
			__lowerCAmelCase      		=       tokenizer.batch_encode_plus(lowercase      ,			return_tensors="""pt"""			)
			__lowerCAmelCase      		=       dummy_input.to(model.device			)
			if model.config.use_cache:
						__lowerCAmelCase      		=       generate_past_key_values(lowercase      ,			lowercase      ,			1			)
			__lowerCAmelCase      		=       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 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		a     :     Optional[Any]     						=("""_optimized""", """_default""")
		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       optimized
					__lowerCAmelCase      		=       default
		def __call__(						self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					if kwargs["past_key_values"] is None and self._default.config.use_cache:
								__lowerCAmelCase      		=       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    )
					__lowerCAmelCase      		=       self._optimized(**__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       outputs[0]
					__lowerCAmelCase      		=       outputs[1]
					__lowerCAmelCase      		=       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,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return getattr(self._default,__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):



					'''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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return self._default._reorder_cache(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )





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

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

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

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

			parser.add_argument("""--seed"""      ,			type=lowercase      ,			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=lowercase      ,			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"""			)
			__lowerCAmelCase      		=       parser.parse_args()

			__lowerCAmelCase      		=       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
			__lowerCAmelCase      		=       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(lowercase			)

			# Initialize the model and tokenizer
			try:
						__lowerCAmelCase      		=       args.model_type.lower()
						__lowerCAmelCase		, __lowerCAmelCase      		=       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 :)"""			)

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

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

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

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

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

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

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

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

						__lowerCAmelCase      		=       _ModelFallbackWrapper(lowercase      ,			lowercase			)

			__lowerCAmelCase      		=       model.generate(
			    input_ids=lowercase      ,			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=lowercase      ,			num_return_sequences=args.num_return_sequences      ,			)

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

			__lowerCAmelCase      		=       []

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

						# Decode text
						__lowerCAmelCase      		=       tokenizer.decode(lowercase      ,			clean_up_tokenization_spaces=lowercase			)

						# Remove all text after the stop token
						__lowerCAmelCase      		=       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
						__lowerCAmelCase      		=       (
						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      ,			clean_up_tokenization_spaces=lowercase			)			) :]
						)

						generated_sequences.append(lowercase			)
						print(lowercase			)

			return generated_sequences


if __name__ == "__main__":
	main()

