
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]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		int=1_4  ,					lowerCamelCase__      :		str=7  ,					lowerCamelCase__      :		Any=True  ,					lowerCamelCase__      :		Tuple=True  ,					lowerCamelCase__      :		List[str]=False  ,					lowerCamelCase__      :		Optional[Any]=True  ,					lowerCamelCase__      :		List[Any]=9_9  ,					lowerCamelCase__      :		Optional[int]=3_2  ,					lowerCamelCase__      :		Optional[Any]=4  ,					lowerCamelCase__      :		Union[str, Any]=4  ,					lowerCamelCase__      :		Optional[int]=[[["global", "local"], 2]]  ,					lowerCamelCase__      :		int=3_7  ,					lowerCamelCase__      :		int="gelu"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=0.1  ,					lowerCamelCase__      :		Any=5_1_2  ,					lowerCamelCase__      :		int=7  ,					lowerCamelCase__      :		List[str]=0.0_2  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       parent
							lowerCAmelCase						:   Optional[Any]							=       batch_size
							lowerCAmelCase						:   Union[str, Any]							=       seq_length
							lowerCAmelCase						:   str							=       is_training
							lowerCAmelCase						:   int							=       use_input_mask
							lowerCAmelCase						:   Optional[int]							=       use_token_type_ids
							lowerCAmelCase						:   Any							=       use_labels
							lowerCAmelCase						:   List[Any]							=       vocab_size
							lowerCAmelCase						:   Union[str, Any]							=       hidden_size
							lowerCAmelCase						:   Any							=       num_hidden_layers
							lowerCAmelCase						:   Tuple							=       num_attention_heads
							lowerCAmelCase						:   Tuple							=       attention_types
							lowerCAmelCase						:   Union[str, Any]							=       intermediate_size
							lowerCAmelCase						:   str							=       hidden_act
							lowerCAmelCase						:   Dict							=       hidden_dropout_prob
							lowerCAmelCase						:   Optional[int]							=       attention_probs_dropout_prob
							lowerCAmelCase						:   Union[str, Any]							=       max_position_embeddings
							lowerCAmelCase						:   Optional[Any]							=       window_size
							lowerCAmelCase						:   str							=       initializer_range
							lowerCAmelCase						:   Optional[int]							=       None
							lowerCAmelCase						:   str							=       vocab_size - 1
							lowerCAmelCase						:   Union[str, Any]							=       vocab_size - 1
							lowerCAmelCase						:   Any							=       vocab_size - 1





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   str							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size			)

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

							lowerCAmelCase						:   Optional[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  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   str							=       self.prepare_config_and_inputs()
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       config_and_inputs
							lowerCAmelCase						:   Optional[Any]							=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Optional[int]			):
							lowerCAmelCase						:   Optional[int]							=       2_0
							lowerCAmelCase						:   List[Any]							=       model_class_name(lowerCamelCase__			)

							lowerCAmelCase						:   str							=       model.init_cache(input_ids.shape[0]  ,					lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       jnp.ones((input_ids.shape[0], max_decoder_length)  ,					dtype='''i4'''			)

							lowerCAmelCase						:   Tuple							=       jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)			)
							lowerCAmelCase						:   List[Any]							=       model(
							    input_ids[:, :-1]  ,					attention_mask=lowerCamelCase__  ,					past_key_values=lowerCamelCase__  ,					position_ids=lowerCamelCase__  ,					)

							lowerCAmelCase						:   List[str]							=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='''i4'''			)
							lowerCAmelCase						:   List[str]							=       model(
							    input_ids[:, -1:]  ,					attention_mask=lowerCamelCase__  ,					past_key_values=outputs_cache.past_key_values  ,					position_ids=lowerCamelCase__  ,					)

							lowerCAmelCase						:   Dict							=       model(lowerCamelCase__			)

							lowerCAmelCase						:   Optional[int]							=       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  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		List[str]			):
							lowerCAmelCase						:   int							=       2_0
							lowerCAmelCase						:   List[Any]							=       model_class_name(lowerCamelCase__			)

							lowerCAmelCase						:   Union[str, Any]							=       jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]  ,					axis=-1  ,					)

							lowerCAmelCase						:   str							=       model.init_cache(input_ids.shape[0]  ,					lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)			)

							lowerCAmelCase						:   List[Any]							=       model(
							    input_ids[:, :-1]  ,					attention_mask=lowerCamelCase__  ,					past_key_values=lowerCamelCase__  ,					position_ids=lowerCamelCase__  ,					)
							lowerCAmelCase						:   Dict							=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='''i4'''			)
							lowerCAmelCase						:   List[Any]							=       model(
							    input_ids[:, -1:]  ,					past_key_values=outputs_cache.past_key_values  ,					attention_mask=lowerCamelCase__  ,					position_ids=lowerCamelCase__  ,					)

							lowerCAmelCase						:   Union[str, Any]							=       model(lowerCamelCase__  ,					attention_mask=lowerCamelCase__			)

							lowerCAmelCase						:   List[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}'''			)



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





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   List[str]							=       FlaxGPTNeoModelTester(self			)





		def  _A							(							self      :		Union[str, Any]			):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   int							=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		Dict			):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[str]							=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)





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

							lowerCAmelCase						:   Optional[Any]							=       FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
							lowerCAmelCase						:   Optional[int]							=       False
							lowerCAmelCase						:   Optional[Any]							=       model.config.eos_token_id

							lowerCAmelCase						:   Optional[Any]							=       jax.jit(model.generate			)

							lowerCAmelCase						:   Union[str, Any]							=       jit_generate(
							    inputs['''input_ids''']  ,					attention_mask=inputs['''attention_mask''']  ,					pad_token_id=tokenizer.pad_token_id			).sequences

							lowerCAmelCase						:   Tuple							=       tokenizer.batch_decode(lowerCamelCase__  ,					skip_special_tokens=lowerCamelCase__			)

							lowerCAmelCase						:   Any							=       [
							    '''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  _A							(							self      :		Tuple			):
							lowerCAmelCase		,							lowerCAmelCase						:   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
																	lowerCAmelCase						:   Union[str, Any]							=       self._prepare_for_class(lowerCamelCase__  ,					lowerCamelCase__			)
																	lowerCAmelCase						:   Any							=       {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

																	lowerCAmelCase						:   int							=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,					lowerCamelCase__			)
																	lowerCAmelCase						:   List[str]							=       fx_state

																	with torch.no_grad():
																						lowerCAmelCase						:   Dict							=       pt_model(**lowerCamelCase__			).to_tuple()

																	lowerCAmelCase						:   int							=       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__			)
																						lowerCAmelCase						:   Any							=       model_class.from_pretrained(lowerCamelCase__  ,					from_pt=lowerCamelCase__			)

																	lowerCAmelCase						:   str							=       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  _A							(							self      :		Tuple			):
							lowerCAmelCase		,							lowerCAmelCase						:   Dict							=       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
																	lowerCAmelCase						:   int							=       self._prepare_for_class(lowerCamelCase__  ,					lowerCamelCase__			)
																	lowerCAmelCase						:   Optional[Any]							=       {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

																	lowerCAmelCase						:   int							=       pt_model_class(lowerCamelCase__			).eval()
																	lowerCAmelCase						:   Tuple							=       model_class(lowerCamelCase__  ,					dtype=jnp.floataa			)

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

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

																	with torch.no_grad():
																						lowerCAmelCase						:   Union[str, Any]							=       pt_model(**lowerCamelCase__			).to_tuple()

																	lowerCAmelCase						:   Dict							=       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__			)
																						lowerCAmelCase						:   Union[str, Any]							=       pt_model_class.from_pretrained(lowerCamelCase__  ,					from_flax=lowerCamelCase__			)

																	with torch.no_grad():
																						lowerCAmelCase						:   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  _A							(							self      :		List[str]			):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase						:   List[Any]							=       model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
												lowerCAmelCase						:   Dict							=       model(np.ones((1, 1)			)			)
												self.assertIsNotNone(lowerCamelCase__			)


import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__SCREAMING_SNAKE_CASE     :				List[Any]             =       logging.get_logger(__name__)


class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					*lowerCamelCase__      :		Dict  ,					**lowerCamelCase__      :		int			):
							warnings.warn(
							    '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use ConditionalDetrImageProcessor instead.'''  ,					lowerCamelCase__  ,					)
							super().__init__(*lowerCamelCase__  ,					**lowerCamelCase__			)
