

import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   Optional[int]:
	A__							:					Union[str, Any]       =	test_file.split(os.path.sep      )
	if components[0:2] != ["tests", "models"]:
		raise ValueError(
		    """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
		    f"""{test_file} instead."""      )
	A__							:					Dict       =	components[-1]
	if not test_fn.endswith("""py"""      ):
		raise ValueError(f"""`test_file` should be a python file. Got {test_fn} instead."""      )
	if not test_fn.startswith("""test_modeling_"""      ):
		raise ValueError(
		    f"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""      )

	A__							:					int       =	components[:-1] + [test_fn.replace(""".py"""					,   """"""      )]
	A__							:					Tuple       =	""".""".join(lowercase_      )

	return test_module_path

def 		UpperCamelCase      (lowercase_:				Any      )  ->   List[str]:
	A__							:					Optional[Any]       =	get_module_path(lowercase_      )
	A__							:					int       =	importlib.import_module(lowercase_      )

	return test_module

def 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   Union[str, Any]:
	A__							:					List[Any]       =	[]
	A__							:					List[str]       =	get_test_module(lowercase_      )
	for attr in dir(lowercase_      ):
		if attr.endswith("""ModelTester"""      ):
			tester_classes.append(getattr(lowercase_					,   lowercase_      )      )

    # sort with class names
	return sorted(lowercase_					,   key=lambda lowercase_      : x.__name__      )

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   List[str]:
	A__							:					List[str]       =	[]
	A__							:					Tuple       =	get_test_module(lowercase_      )
	for attr in dir(lowercase_      ):
		A__							:					List[str]       =	getattr(lowercase_					,   lowercase_      )
		# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
		# `all_model_classes` is not empty (which also excludes other special classes).
		A__							:					List[Any]       =	getattr(lowercase_					,   """all_model_classes"""					,   []      )
		if len(lowercase_      ) > 0:
			test_classes.append(lowercase_      )

    # sort with class names
	return sorted(lowercase_					,   key=lambda lowercase_      : x.__name__      )

def 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   int:
	A__							:					List[str]       =	get_test_classes(lowercase_      )
	A__							:					Optional[Any]       =	set()
	for test_class in test_classes:
		model_classes.update(test_class.all_model_classes      )

	# sort with class names
	return sorted(lowercase_					,   key=lambda lowercase_      : x.__name__      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   int:
	A__							:					Any       =	test_class()
	if hasattr(lowercase_					,   """setUp"""      ):
		test.setUp()

	A__							:					Any       =	None
	if hasattr(lowercase_					,   """model_tester"""      ):
		# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
		if test.model_tester is not None:
			A__							:					Tuple       =	test.model_tester.__class__

	return model_tester

def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				Union[str, Any]      )  ->   Union[str, Any]:
	A__							:					Any       =	get_test_classes(lowercase_      )

	A__							:					Optional[Any]       =	[]
	for test_class in test_classes:
		if model_class in test_class.all_model_classes:
			target_test_classes.append(lowercase_      )

    # sort with class names
	return sorted(lowercase_					,   key=lambda lowercase_      : x.__name__      )

def 		UpperCamelCase      (lowercase_:				List[Any]					,   lowercase_:				Dict      )  ->   int:
	A__							:					Dict       =	get_test_classes_for_model(lowercase_					,   lowercase_      )

	A__							:					Optional[Any]       =	[]
	for test_class in test_classes:
		A__							:					Union[str, Any]       =	get_model_tester_from_test_class(lowercase_      )
		if tester_class is not None:
			tester_classes.append(lowercase_      )

    # sort with class names
	return sorted(lowercase_					,   key=lambda lowercase_      : x.__name__      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   int:
	A__							:					Optional[int]       =	get_test_classes(lowercase_      )
	A__							:					Tuple       =	{test_class: get_model_tester_from_test_class(lowercase_      ) for test_class in test_classes}
	return test_tester_mapping

def 		UpperCamelCase      (lowercase_:				int      )  ->   str:
	A__							:					Union[str, Any]       =	get_model_classes(lowercase_      )
	A__							:					List[str]       =	{
	    model_class: get_test_classes_for_model(lowercase_					,   lowercase_      ) for model_class in model_classes
	}
	return model_test_mapping

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   Optional[int]:
	A__							:					Optional[int]       =	get_model_classes(lowercase_      )
	A__							:					Optional[Any]       =	{
	    model_class: get_tester_classes_for_model(lowercase_					,   lowercase_      ) for model_class in model_classes
	}
	return model_to_tester_mapping


def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   str:
	if isinstance(lowercase_					,   lowercase_      ):
		return o
	elif isinstance(lowercase_					,   lowercase_      ):
		return o.__name__
	elif isinstance(lowercase_					,   (list, tuple)      ):
		return [to_json(lowercase_      ) for x in o]
	elif isinstance(lowercase_					,   lowercase_      ):
		return {to_json(lowercase_      ): to_json(lowercase_      ) for k, v in o.items()}
	else:
		return o






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






					@register_to_config
					def __init__(				self					, A__					, A__					, A__ = None					, A__ = 5_0257					, A__ = 1024					, A__ = 768					, A__ = 12					, A__ = 12					, A__ = None					, A__ = "gelu_new"					, A__ = 0.1					, A__ = 0.1					, A__ = 0.1					, A__ = 1e-5					, A__ = 0.0_2					, A__ = True					, A__ = True					, A__ = False					, A__ = False					, ):
						super().__init__()

						A__							:					Union[str, Any]       =	prefix_length

						if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
							raise ValueError(
							    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
							    F""" `n_embd`: {n_embd} are not equal."""    )

						A__							:					str       =	prefix_inner_dim
						A__							:					Optional[Any]       =	prefix_hidden_dim

						A__							:					Tuple       =	(
						    nn.Linear(self.prefix_inner_dim					, self.prefix_hidden_dim    )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:					int       =	(
						    nn.Linear(self.prefix_hidden_dim					, A__    ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:					Tuple       =	GPTaConfig(
						    vocab_size=A__					, n_positions=A__					, n_embd=A__					, n_layer=A__					, n_head=A__					, n_inner=A__					, activation_function=A__					, resid_pdrop=A__					, embd_pdrop=A__					, attn_pdrop=A__					, layer_norm_epsilon=A__					, initializer_range=A__					, scale_attn_weights=A__					, use_cache=A__					, scale_attn_by_inverse_layer_idx=A__					, reorder_and_upcast_attn=A__					, )
						A__							:					int       =	GPTaLMHeadModel(A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, A__ = None					, ):
						A__							:					List[str]       =	self.transformer.transformer.wte(A__    )
						A__							:					int       =	self.encode_prefix(A__    )
						A__							:					int       =	self.decode_prefix(A__    )
						A__							:					Optional[Any]       =	torch.cat((prefix_embeds, embedding_text)					, dim=1    )

						if labels is not None:
							A__							:					Any       =	self.get_dummy_token(input_ids.shape[0]					, input_ids.device    )
							A__							:					List[Any]       =	torch.cat((dummy_token, input_ids)					, dim=1    )
						A__							:					List[str]       =	self.transformer(inputs_embeds=A__					, labels=A__					, attention_mask=A__    )
						if self.prefix_hidden_dim is not None:
							return out, hidden
						else:
							return out






					def 						__A    (				self					, A__					, A__    ):
						return torch.zeros(A__					, self.prefix_length					, dtype=torch.intaa					, device=A__    )






					def 						__A    (				self					, A__    ):
						return self.encode_prefix(A__    )






					@torch.no_grad()
					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	torch.split(A__					, 1					, dim=0    )
						A__							:					Optional[int]       =	[]
						A__							:					str       =	[]
						for feature in features:
							A__							:					Dict       =	self.decode_prefix(feature.to(A__    )    )  # back to the clip feature
							# Only support beam search for now
							A__     ,     A__							:					Union[str, Any]       =	self.generate_beam(
							    input_embeds=A__					, device=A__					, eos_token_id=A__    )
							generated_tokens.append(output_tokens[0]    )
							generated_seq_lengths.append(seq_lengths[0]    )
						A__							:					int       =	torch.stack(A__    )
						A__							:					List[Any]       =	torch.stack(A__    )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def 						__A    (				self					, A__=None					, A__=None					, A__=None					, A__ = 5					, A__ = 67					, A__ = 1.0					, A__ = None					, ):
						A__							:					Any       =	eos_token_id
						A__							:					Any       =	None
						A__							:					Optional[int]       =	None
						A__							:					Optional[Any]       =	torch.ones(A__					, device=A__					, dtype=torch.int    )
						A__							:					Any       =	torch.zeros(A__					, device=A__					, dtype=torch.bool    )

						if input_embeds is not None:
							A__							:					Dict       =	input_embeds
						else:
							A__							:					str       =	self.transformer.transformer.wte(A__    )

						for i in range(A__    ):
							A__							:					Dict       =	self.transformer(inputs_embeds=A__    )
							A__							:					str       =	outputs.logits
							A__							:					Union[str, Any]       =	logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
							A__							:					Any       =	logits.softmax(-1    ).log()

							if scores is None:
								A__     ,     A__							:					Optional[int]       =	logits.topk(A__					, -1    )
								A__							:					List[Any]       =	generated.expand(A__					, *generated.shape[1:]    )
								A__     ,     A__							:					List[Any]       =	next_tokens.permute(1					, 0    ), scores.squeeze(0    )
								if tokens is None:
									A__							:					Optional[Any]       =	next_tokens
								else:
									A__							:					List[Any]       =	tokens.expand(A__					, *tokens.shape[1:]    )
									A__							:					int       =	torch.cat((tokens, next_tokens)					, dim=1    )
							else:
								A__							:					Optional[int]       =	-float(np.inf    )
								A__							:					List[Any]       =	0
								A__							:					str       =	scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:					Dict       =	scores_sum / seq_lengths[:, None]
								A__     ,     A__							:					List[Any]       =	scores_sum_average.view(-1    ).topk(A__					, -1    )
								A__							:					Tuple       =	next_tokens // scores_sum.shape[1]
								A__							:					Optional[Any]       =	seq_lengths[next_tokens_source]
								A__							:					List[str]       =	next_tokens % scores_sum.shape[1]
								A__							:					Optional[int]       =	next_tokens.unsqueeze(1    )
								A__							:					int       =	tokens[next_tokens_source]
								A__							:					List[Any]       =	torch.cat((tokens, next_tokens)					, dim=1    )
								A__							:					str       =	generated[next_tokens_source]
								A__							:					Optional[Any]       =	scores_sum_average * seq_lengths
								A__							:					Union[str, Any]       =	is_stopped[next_tokens_source]

							A__							:					str       =	self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					, 1					, -1    )
							A__							:					Optional[int]       =	torch.cat((generated, next_token_embed)					, dim=1    )
							A__							:					List[str]       =	is_stopped + next_tokens.eq(A__    ).squeeze()
							if is_stopped.all():
								break

						A__							:					Dict       =	scores / seq_lengths
						A__							:					Dict       =	scores.argsort(descending=A__    )
						# tokens tensors are already padded to max_seq_length
						A__							:					Union[str, Any]       =	[tokens[i] for i in order]
						A__							:					Any       =	torch.stack(A__					, dim=0    )
						A__							:					Dict       =	torch.tensor([seq_lengths[i] for i in order]					, dtype=seq_lengths.dtype    )
						return output_texts, seq_lengths



