'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


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

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch





class        __magic_name__		:







			def __init__(      self							:    List[Any]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    Optional[int]=14							,     lowercase_							:    Optional[Any]=7							,     lowercase_							:    List[Any]=True							,     lowercase_							:    Optional[int]=True							,     lowercase_							:    Dict=False							,     lowercase_							:    Tuple=True							,     lowercase_							:    Optional[Any]=99							,     lowercase_							:    str=32							,     lowercase_							:    Tuple=4							,     lowercase_							:    Union[str, Any]=4							,     lowercase_							:    Dict=[[["global", "local"], 2]]							,     lowercase_							:    List[str]=37							,     lowercase_							:    Union[str, Any]="gelu"							,     lowercase_							:    int=0.1							,     lowercase_							:    int=0.1							,     lowercase_							:    List[str]=512							,     lowercase_							:    Optional[Any]=7							,     lowercase_							:    Any=0.02							,     ):
										lowercase_   :     List[Any]									=			parent
										lowercase_   :     str									=			batch_size
										lowercase_   :     Dict									=			seq_length
										lowercase_   :     List[Any]									=			is_training
										lowercase_   :     List[str]									=			use_input_mask
										lowercase_   :     Dict									=			use_token_type_ids
										lowercase_   :     Optional[Any]									=			use_labels
										lowercase_   :     Tuple									=			vocab_size
										lowercase_   :     Optional[Any]									=			hidden_size
										lowercase_   :     Union[str, Any]									=			num_hidden_layers
										lowercase_   :     str									=			num_attention_heads
										lowercase_   :     Tuple									=			attention_types
										lowercase_   :     Dict									=			intermediate_size
										lowercase_   :     Dict									=			hidden_act
										lowercase_   :     List[Any]									=			hidden_dropout_prob
										lowercase_   :     Optional[Any]									=			attention_probs_dropout_prob
										lowercase_   :     Dict									=			max_position_embeddings
										lowercase_   :     Optional[int]									=			window_size
										lowercase_   :     List[Any]									=			initializer_range
										lowercase_   :     List[str]									=			None
										lowercase_   :     Optional[Any]									=			vocab_size - 1
										lowercase_   :     Optional[int]									=			vocab_size - 1
										lowercase_   :     Union[str, Any]									=			vocab_size - 1







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     Dict									=			ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size       )

										lowercase_   :     List[Any]									=			None
										if self.use_input_mask:
																	lowercase_   :     List[str]									=			random_attention_mask([self.batch_size, self.seq_length]       )

										lowercase_   :     List[Any]									=			GPTNeoConfig(
										    vocab_size=self.vocab_size							,     hidden_size=self.hidden_size							,     num_layers=self.num_hidden_layers							,     num_heads=self.num_attention_heads							,     max_position_embeddings=self.max_position_embeddings							,     use_cache=__lowercase							,     bos_token_id=self.bos_token_id							,     eos_token_id=self.eos_token_id							,     pad_token_id=self.pad_token_id							,     window_size=self.window_size							,     attention_types=self.attention_types							,     )

										return (config, input_ids, input_mask)







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     Dict									=			self.prepare_config_and_inputs()
										lowercase_		,		lowercase_		,		lowercase_   :     Any									=			config_and_inputs
										lowercase_   :     List[str]									=			{"""input_ids""": input_ids, """attention_mask""": attention_mask}
										return config, inputs_dict







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    Optional[int]							,     lowercase_							:    str							,     lowercase_							:    int							,     lowercase_							:    Union[str, Any]       ):
										lowercase_   :     Union[str, Any]									=			20
										lowercase_   :     int									=			model_class_name(__lowercase       )

										lowercase_   :     List[str]									=			model.init_cache(input_ids.shape[0]							,     __lowercase       )
										lowercase_   :     Dict									=			jnp.ones((input_ids.shape[0], max_decoder_length)							,     dtype="""i4"""       )

										lowercase_   :     str									=			jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1       )[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
										lowercase_   :     int									=			model(
										    input_ids[:, :-1]							,     attention_mask=__lowercase							,     past_key_values=__lowercase							,     position_ids=__lowercase							,     )

										lowercase_   :     List[Any]									=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="""i4"""       )
										lowercase_   :     List[Any]									=			model(
										    input_ids[:, -1:]							,     attention_mask=__lowercase							,     past_key_values=outputs_cache.past_key_values							,     position_ids=__lowercase							,     )

										lowercase_   :     Optional[Any]									=			model(__lowercase       )

										lowercase_   :     str									=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1E-3							,     msg=f'''Max diff is {diff}'''       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    int							,     lowercase_							:    List[str]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    Any       ):
										lowercase_   :     Dict									=			20
										lowercase_   :     Dict									=			model_class_name(__lowercase       )

										lowercase_   :     List[str]									=			jnp.concatenate(
										    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]							,     axis=-1							,     )

										lowercase_   :     Tuple									=			model.init_cache(input_ids.shape[0]							,     __lowercase       )
										lowercase_   :     Tuple									=			jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1       )[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )

										lowercase_   :     Optional[int]									=			model(
										    input_ids[:, :-1]							,     attention_mask=__lowercase							,     past_key_values=__lowercase							,     position_ids=__lowercase							,     )
										lowercase_   :     Dict									=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="""i4"""       )
										lowercase_   :     List[str]									=			model(
										    input_ids[:, -1:]							,     past_key_values=outputs_cache.past_key_values							,     attention_mask=__lowercase							,     position_ids=__lowercase							,     )

										lowercase_   :     List[str]									=			model(__lowercase							,     attention_mask=__lowercase       )

										lowercase_   :     List[str]									=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
										self.parent.assertTrue(diff < 1E-3							,     msg=f'''Max diff is {diff}'''       )





@require_flax
class        __magic_name__		(       A_,	A_,	unittest.TestCase):
			UpperCamelCase__							=			(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			UpperCamelCase__							=			(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Any									=			FlaxGPTNeoModelTester(self       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):







										for model_class_name in self.all_model_classes:
																	lowercase_		,		lowercase_		,		lowercase_   :     List[str]									=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):







										for model_class_name in self.all_model_classes:
																	lowercase_		,		lowercase_		,		lowercase_   :     Union[str, Any]									=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     Dict									=			GPTaTokenizer.from_pretrained("""gpt2"""							,     pad_token="""<|endoftext|>"""							,     padding_side="""left"""       )
										lowercase_   :     Dict									=			tokenizer(["""Hello this is a long string""", """Hey"""]							,     return_tensors="""np"""							,     padding=__lowercase							,     truncation=__lowercase       )

										lowercase_   :     Any									=			FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""       )
										lowercase_   :     Union[str, Any]									=			False
										lowercase_   :     Tuple									=			model.config.eos_token_id

										lowercase_   :     List[str]									=			jax.jit(model.generate       )

										lowercase_   :     Tuple									=			jit_generate(
										    inputs["""input_ids"""]							,     attention_mask=inputs["""attention_mask"""]							,     pad_token_id=tokenizer.pad_token_id       ).sequences

										lowercase_   :     Union[str, Any]									=			tokenizer.batch_decode(__lowercase							,     skip_special_tokens=__lowercase       )

										lowercase_   :     int									=			[
										    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
										    """Hey, I'm a little late to the party. I'm going to""",
										]

										self.assertListEqual(__lowercase							,     __lowercase       )







			@is_pt_flax_cross_test
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_		,		lowercase_   :     Any									=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__       ):
																								# prepare inputs
																								lowercase_   :     Optional[int]									=			self._prepare_for_class(__lowercase							,     __lowercase       )
																								lowercase_   :     List[str]									=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

																								# load corresponding PyTorch class
																								lowercase_   :     Dict									=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																								lowercase_   :     int									=			getattr(__lowercase							,     __lowercase       )

																								lowercase_		,		lowercase_   :     Union[str, Any]									=			pt_inputs["""input_ids"""].shape
																								lowercase_   :     Union[str, Any]									=			np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)       )
																								for batch_idx, start_index in enumerate(__lowercase       ):
																															lowercase_   :     List[Any]									=			0
																															lowercase_   :     Union[str, Any]									=			1
																															lowercase_   :     Dict									=			0
																															lowercase_   :     Tuple									=			1
																								lowercase_   :     Optional[Any]									=			pt_model_class(__lowercase       ).eval()
																								lowercase_   :     str									=			model_class(__lowercase							,     dtype=jnp.floataa       )

																								lowercase_   :     List[Any]									=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,     __lowercase       )
																								lowercase_   :     Any									=			fx_state

																								with torch.no_grad():
																															lowercase_   :     int									=			pt_model(**__lowercase       ).to_tuple()

																								lowercase_   :     List[str]									=			fx_model(**__lowercase       ).to_tuple()
																								self.assertEqual(len(__lowercase       )							,     len(__lowercase       )							,     """Output lengths differ between Flax and PyTorch"""       )
																								for fx_output, pt_output in zip(__lowercase							,     __lowercase       ):
																															self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2       )

																								with tempfile.TemporaryDirectory() as tmpdirname:
																															pt_model.save_pretrained(__lowercase       )
																															lowercase_   :     Any									=			model_class.from_pretrained(__lowercase							,     from_pt=__lowercase       )

																								lowercase_   :     Tuple									=			fx_model_loaded(**__lowercase       ).to_tuple()
																								self.assertEqual(
																								    len(__lowercase       )							,     len(__lowercase       )							,     """Output lengths differ between Flax and PyTorch"""       )
																								for fx_output_loaded, pt_output in zip(__lowercase							,     __lowercase       ):
																															self.assert_almost_equals(fx_output_loaded[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2       )







			@is_pt_flax_cross_test
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_		,		lowercase_   :     List[Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__       ):
																								# prepare inputs
																								lowercase_   :     Tuple									=			self._prepare_for_class(__lowercase							,     __lowercase       )
																								lowercase_   :     List[str]									=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

																								# load corresponding PyTorch class
																								lowercase_   :     Any									=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																								lowercase_   :     str									=			getattr(__lowercase							,     __lowercase       )

																								lowercase_   :     List[str]									=			pt_model_class(__lowercase       ).eval()
																								lowercase_   :     Optional[Any]									=			model_class(__lowercase							,     dtype=jnp.floataa       )

																								lowercase_   :     Optional[Any]									=			load_flax_weights_in_pytorch_model(__lowercase							,     fx_model.params       )
																								lowercase_		,		lowercase_   :     List[str]									=			pt_inputs["""input_ids"""].shape
																								lowercase_   :     Union[str, Any]									=			np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)       )
																								for batch_idx, start_index in enumerate(__lowercase       ):
																															lowercase_   :     str									=			0
																															lowercase_   :     int									=			1
																															lowercase_   :     Dict									=			0
																															lowercase_   :     Optional[int]									=			1

																								# make sure weights are tied in PyTorch
																								pt_model.tie_weights()

																								with torch.no_grad():
																															lowercase_   :     Optional[int]									=			pt_model(**__lowercase       ).to_tuple()

																								lowercase_   :     List[str]									=			fx_model(**__lowercase       ).to_tuple()
																								self.assertEqual(len(__lowercase       )							,     len(__lowercase       )							,     """Output lengths differ between Flax and PyTorch"""       )
																								for fx_output, pt_output in zip(__lowercase							,     __lowercase       ):
																															self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2       )

																								with tempfile.TemporaryDirectory() as tmpdirname:
																															fx_model.save_pretrained(__lowercase       )
																															lowercase_   :     int									=			pt_model_class.from_pretrained(__lowercase							,     from_flax=__lowercase       )

																								with torch.no_grad():
																															lowercase_   :     Optional[Any]									=			pt_model_loaded(**__lowercase       ).to_tuple()

																								self.assertEqual(
																								    len(__lowercase       )							,     len(__lowercase       )							,     """Output lengths differ between Flax and PyTorch"""       )
																								for fx_output, pt_output in zip(__lowercase							,     __lowercase       ):
																															self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2       )







			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):







										for model_class_name in self.all_model_classes:
																	lowercase_   :     Optional[int]									=			model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""       )
																	lowercase_   :     List[str]									=			model(np.ones((1, 1)       )       )
																	self.assertIsNotNone(__lowercase       )












import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


UpperCamelCase__ :			Optional[int]       	=			[nn.LayerNorm]

UpperCamelCase__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Dict       	=			version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__ :			str       	=			parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase__ :			Any       	=			parsed_torch_version_base < version.parse("""1.11""")




def  A_(      A					,   A					,   A					,   A					,   self ):
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self )
	else:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self.dtype )




def  A_(      A					,   A					,   A = 0 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		    =  layer.bias.clone().detach()
		else:
			UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  nn.Linear(new_size[1]					,   new_size[0]					,   bias=layer.bias is not None ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	if layer.bias is not None:
		UpperCAmelCase_		    =  False
		new_layer.bias.copy_(b.contiguous() )
		UpperCAmelCase_		    =  True
	return new_layer







class 	_UpperCamelCase	(							nn.Module	):







					'''simple docstring'''

					def __init__(    self     :	Union[str, Any]			,       __lowercase     :	List[Any]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_		    =  nf
						UpperCAmelCase_		    =  nn.Parameter(torch.empty(__lowercase			,       __lowercase		)		)
						UpperCAmelCase_		    =  nn.Parameter(torch.zeros(__lowercase		)		)
						nn.init.normal_(self.weight			,       std=0.02		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  x.size()[:-1] + (self.nf,)
						UpperCAmelCase_		    =  torch.addmm(self.bias			,       x.view(-1			,       x.size(-1		)		)			,       self.weight		)
						UpperCAmelCase_		    =  x.view(__lowercase		)
						return x




def  A_(      A					,   A					,   A = 1 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if dim == 0:
		UpperCAmelCase_		    =  layer.bias.clone().detach()
	else:
		UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  ConvaD(new_size[1]					,   new_size[0] ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	UpperCAmelCase_		    =  False
	new_layer.bias.copy_(b.contiguous() )
	UpperCAmelCase_		    =  True
	return new_layer




def  A_(      A					,   A					,   A = None ):
	if isinstance(A					,   nn.Linear ):
		return prune_linear_layer(A					,   A					,   dim=0 if dim is None else dim )
	elif isinstance(A					,   A ):
		return prune_convad_layer(A					,   A					,   dim=1 if dim is None else dim )
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}""" )




def  A_(      A					,   A					,   A					,   *A ):
	assert len(A ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		    =  len(inspect.signature(A ).parameters )
	if num_args_in_forward_chunk_fn != len(A ):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A )} input """
		    """tensors are given""" )

	if chunk_size > 0:
		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}""" )

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}""" )

		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		    =  tuple(input_tensor.chunk(A					,   dim=A ) for input_tensor in input_tensors )
		# apply forward fn to every tuple
		UpperCAmelCase_		    =  tuple(forward_fn(*A ) for input_tensors_chunk in zip(*A ) )
		# concatenate output at same dimension
		return torch.cat(A					,   dim=A )

	return forward_fn(*A )




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  torch.ones(A					,   A )
	UpperCAmelCase_		    =  set(A ) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		    =  head - sum(1 if h < head else 0 for h in already_pruned_heads )
		UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  mask.view(-1 ).contiguous().eq(1 )
	UpperCAmelCase_		    =  torch.arange(len(A ) )[mask].long()
	return heads, index




def  A_(      *A					,   A = None ):
	return torch.meshgrid(*A					,   indexing=A )




def  A_(      A ):
	return tensor.device, storage_ptr(A ), storage_size(A )


