





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 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	Tuple,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Union[str, Any]=14,				lowerCamelCase_:	Any=7,				lowerCamelCase_:	List[Any]=True,				lowerCamelCase_:	Dict=True,				lowerCamelCase_:	Dict=False,				lowerCamelCase_:	List[str]=True,				lowerCamelCase_:	Any=99,				lowerCamelCase_:	str=32,				lowerCamelCase_:	Union[str, Any]=4,				lowerCamelCase_:	int=4,				lowerCamelCase_:	Dict=[[["global", "local"], 2]],				lowerCamelCase_:	Optional[Any]=37,				lowerCamelCase_:	Tuple="gelu",				lowerCamelCase_:	Optional[int]=0.1,				lowerCamelCase_:	int=0.1,				lowerCamelCase_:	Optional[int]=512,				lowerCamelCase_:	Union[str, Any]=7,				lowerCamelCase_:	int=0.0_2,				):
								lowercase__				:			Optional[Any]            =		parent
								lowercase__				:			Optional[int]            =		batch_size
								lowercase__				:			int            =		seq_length
								lowercase__				:			Any            =		is_training
								lowercase__				:			Tuple            =		use_input_mask
								lowercase__				:			str            =		use_token_type_ids
								lowercase__				:			Tuple            =		use_labels
								lowercase__				:			List[Any]            =		vocab_size
								lowercase__				:			str            =		hidden_size
								lowercase__				:			Dict            =		num_hidden_layers
								lowercase__				:			int            =		num_attention_heads
								lowercase__				:			Optional[int]            =		attention_types
								lowercase__				:			Optional[int]            =		intermediate_size
								lowercase__				:			List[str]            =		hidden_act
								lowercase__				:			Any            =		hidden_dropout_prob
								lowercase__				:			List[Any]            =		attention_probs_dropout_prob
								lowercase__				:			List[Any]            =		max_position_embeddings
								lowercase__				:			Tuple            =		window_size
								lowercase__				:			List[Any]            =		initializer_range
								lowercase__				:			int            =		None
								lowercase__				:			Any            =		vocab_size - 1
								lowercase__				:			Any            =		vocab_size - 1
								lowercase__				:			Dict            =		vocab_size - 1

	def       snake_case__(	self:	Dict ):
								lowercase__				:			List[str]            =		ids_tensor([self.batch_size, self.seq_length],				self.vocab_size )

								lowercase__				:			Optional[Any]            =		None
								if self.use_input_mask:
															lowercase__				:			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=lowerCamelCase_,				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       snake_case__(	self:	Optional[int] ):
								lowercase__				:			Optional[Any]            =		self.prepare_config_and_inputs()
								lowercase__				:			Dict            =		config_and_inputs
								lowercase__				:			Any            =		{'input_ids': input_ids, 'attention_mask': attention_mask}
								return config, inputs_dict

	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	Any,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Dict,				lowerCamelCase_:	List[str] ):
								lowercase__				:			List[str]            =		20
								lowercase__				:			List[Any]            =		model_class_name(lowerCamelCase_ )

								lowercase__				:			str            =		model.init_cache(input_ids.shape[0],				lowerCamelCase_ )
								lowercase__				:			List[str]            =		jnp.ones((input_ids.shape[0], max_decoder_length),				dtype='i4' )

								lowercase__				:			List[str]            =		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=lowerCamelCase_,				past_key_values=lowerCamelCase_,				position_ids=lowerCamelCase_,				)

								lowercase__				:			str            =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],				dtype='i4' )
								lowercase__				:			Optional[Any]            =		model(
								    input_ids[:, -1:],				attention_mask=lowerCamelCase_,				past_key_values=outputs_cache.past_key_values,				position_ids=lowerCamelCase_,				)

								lowercase__				:			Dict            =		model(lowerCamelCase_ )

								lowercase__				:			Any            =		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       snake_case__(	self:	int,				lowerCamelCase_:	List[str],				lowerCamelCase_:	Optional[Any],				lowerCamelCase_:	Optional[Any],				lowerCamelCase_:	int ):
								lowercase__				:			Tuple            =		20
								lowercase__				:			str            =		model_class_name(lowerCamelCase_ )

								lowercase__				:			int            =		jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )],				axis=-1,				)

								lowercase__				:			Optional[int]            =		model.init_cache(input_ids.shape[0],				lowerCamelCase_ )
								lowercase__				:			Dict            =		jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1 )[None, :],				(input_ids.shape[0], input_ids.shape[-1] - 1) )

								lowercase__				:			List[str]            =		model(
								    input_ids[:, :-1],				attention_mask=lowerCamelCase_,				past_key_values=lowerCamelCase_,				position_ids=lowerCamelCase_,				)
								lowercase__				:			int            =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],				dtype='i4' )
								lowercase__				:			Optional[Any]            =		model(
								    input_ids[:, -1:],				past_key_values=outputs_cache.past_key_values,				attention_mask=lowerCamelCase_,				position_ids=lowerCamelCase_,				)

								lowercase__				:			Optional[Any]            =		model(lowerCamelCase_,				attention_mask=lowerCamelCase_ )

								lowercase__				:			Tuple            =		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 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	_A       	=							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

	def       snake_case__(	self:	List[Any] ):
								lowercase__				:			Tuple            =		FlaxGPTNeoModelTester(self )

	def       snake_case__(	self:	Union[str, Any] ):
								for model_class_name in self.all_model_classes:
															lowercase__				:			Any            =		self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )

	def       snake_case__(	self:	Union[str, Any] ):
								for model_class_name in self.all_model_classes:
															lowercase__				:			Dict            =		self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )

	@slow
	def       snake_case__(	self:	Optional[int] ):
								lowercase__				:			List[Any]            =		GPTaTokenizer.from_pretrained('gpt2',				pad_token='<|endoftext|>',				padding_side='left' )
								lowercase__				:			List[str]            =		tokenizer(['Hello this is a long string', 'Hey'],				return_tensors='np',				padding=lowerCamelCase_,				truncation=lowerCamelCase_ )

								lowercase__				:			int            =		FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M' )
								lowercase__				:			int            =		False
								lowercase__				:			Dict            =		model.config.eos_token_id

								lowercase__				:			Optional[int]            =		jax.jit(model.generate )

								lowercase__				:			Optional[int]            =		jit_generate(
								    inputs['input_ids'],				attention_mask=inputs['attention_mask'],				pad_token_id=tokenizer.pad_token_id ).sequences

								lowercase__				:			Dict            =		tokenizer.batch_decode(lowerCamelCase_,				skip_special_tokens=lowerCamelCase_ )

								lowercase__				:			Dict            =		[
								    '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(lowerCamelCase_,				lowerCamelCase_ )

	@is_pt_flax_cross_test
	def       snake_case__(	self:	int ):
								lowercase__				:			str            =		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[Any]            =		self._prepare_for_class(lowerCamelCase_,				lowerCamelCase_ )
																						lowercase__				:			Union[str, Any]            =		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase__				:			Optional[int]            =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase__				:			Any            =		getattr(lowerCamelCase_,				lowerCamelCase_ )

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

																						lowercase__				:			List[str]            =		convert_pytorch_state_dict_to_flax(pt_model.state_dict(),				lowerCamelCase_ )
																						lowercase__				:			Optional[int]            =		fx_state

																						with torch.no_grad():
																													lowercase__				:			Tuple            =		pt_model(**lowerCamelCase_ ).to_tuple()

																						lowercase__				:			Union[str, Any]            =		fx_model(**lowerCamelCase_ ).to_tuple()
																						self.assertEqual(len(lowerCamelCase_ ),				len(lowerCamelCase_ ),				'Output lengths differ between Flax and PyTorch' )
																						for fx_output, pt_output in zip(lowerCamelCase_,				lowerCamelCase_ ):
																													self.assert_almost_equals(fx_output[:, -1],				pt_output[:, -1].numpy(),				4E-2 )

																						with tempfile.TemporaryDirectory() as tmpdirname:
																													pt_model.save_pretrained(lowerCamelCase_ )
																													lowercase__				:			Optional[Any]            =		model_class.from_pretrained(lowerCamelCase_,				from_pt=lowerCamelCase_ )

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

	@is_pt_flax_cross_test
	def       snake_case__(	self:	Any ):
								lowercase__				:			List[str]            =		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__				:			Dict            =		self._prepare_for_class(lowerCamelCase_,				lowerCamelCase_ )
																						lowercase__				:			Tuple            =		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase__				:			Tuple            =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				lowerCamelCase_ )

																						lowercase__				:			int            =		pt_model_class(lowerCamelCase_ ).eval()
																						lowercase__				:			Tuple            =		model_class(lowerCamelCase_,				dtype=jnp.floataa )

																						lowercase__				:			List[str]            =		load_flax_weights_in_pytorch_model(lowerCamelCase_,				fx_model.params )
																						lowercase__				:			int            =		pt_inputs['input_ids'].shape
																						lowercase__				:			Dict            =		np.random.randint(0,				seq_length - 1,				size=(batch_size,) )
																						for batch_idx, start_index in enumerate(lowerCamelCase_ ):
																													lowercase__				:			str            =		0
																													lowercase__				:			int            =		1
																													lowercase__				:			Tuple            =		0
																													lowercase__				:			Dict            =		1

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

																						with torch.no_grad():
																													lowercase__				:			List[Any]            =		pt_model(**lowerCamelCase_ ).to_tuple()

																						lowercase__				:			Union[str, Any]            =		fx_model(**lowerCamelCase_ ).to_tuple()
																						self.assertEqual(len(lowerCamelCase_ ),				len(lowerCamelCase_ ),				'Output lengths differ between Flax and PyTorch' )
																						for fx_output, pt_output in zip(lowerCamelCase_,				lowerCamelCase_ ):
																													self.assert_almost_equals(fx_output[:, -1],				pt_output[:, -1].numpy(),				4E-2 )

																						with tempfile.TemporaryDirectory() as tmpdirname:
																													fx_model.save_pretrained(lowerCamelCase_ )
																													lowercase__				:			Union[str, Any]            =		pt_model_class.from_pretrained(lowerCamelCase_,				from_flax=lowerCamelCase_ )

																						with torch.no_grad():
																													lowercase__				:			Dict            =		pt_model_loaded(**lowerCamelCase_ ).to_tuple()

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





	@slow
	def       snake_case__(	self:	Union[str, Any] ):
								for model_class_name in self.all_model_classes:
															lowercase__				:			Union[str, Any]            =		model_class_name.from_pretrained('EleutherAI/gpt-neo-125M' )
															lowercase__				:			List[str]            =		model(np.ones((1, 1) ) )
															self.assertIsNotNone(lowerCamelCase_ )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:  Any   =   {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetaImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[str]   =   [
			    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetaForObjectDetection""",
			    """DetaModel""",
			    """DetaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_deta import DetaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deta import (
						    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetaForObjectDetection,
						    DetaModel,
						    DetaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  int   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


