
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 							lowercase						:




							def __init__(							self  ,A__  ,A__=1_4  ,A__=7  ,A__=True  ,A__=True  ,A__=False  ,A__=True  ,A__=9_9  ,A__=3_2  ,A__=4  ,A__=4  ,A__=[[["global", "local"], 2]]  ,A__=3_7  ,A__="gelu"  ,A__=0.1  ,A__=0.1  ,A__=5_1_2  ,A__=7  ,A__=0.02  ,):
												lowercase		      =		parent
												lowercase		      =		batch_size
												lowercase		      =		seq_length
												lowercase		      =		is_training
												lowercase		      =		use_input_mask
												lowercase		      =		use_token_type_ids
												lowercase		      =		use_labels
												lowercase		      =		vocab_size
												lowercase		      =		hidden_size
												lowercase		      =		num_hidden_layers
												lowercase		      =		num_attention_heads
												lowercase		      =		attention_types
												lowercase		      =		intermediate_size
												lowercase		      =		hidden_act
												lowercase		      =		hidden_dropout_prob
												lowercase		      =		attention_probs_dropout_prob
												lowercase		      =		max_position_embeddings
												lowercase		      =		window_size
												lowercase		      =		initializer_range
												lowercase		      =		None
												lowercase		      =		vocab_size - 1
												lowercase		      =		vocab_size - 1
												lowercase		      =		vocab_size - 1




							def 					A__ (							self):
												lowercase		      =		ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size)

												lowercase		      =		None
												if self.use_input_mask:
																	lowercase		      =		random_attention_mask([self.batch_size, self.seq_length])

												lowercase		      =		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):
												lowercase		      =		self.prepare_config_and_inputs()
												lowercase      ,		lowercase      ,		lowercase		      =		config_and_inputs
												lowercase		      =		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		2_0
												lowercase		      =		model_class_name(__lowerCAmelCase)

												lowercase		      =		model.init_cache(input_ids.shape[0]  ,__lowerCAmelCase)
												lowercase		      =		jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype='''i4''')

												lowercase		      =		jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1))
												lowercase		      =		model(
												    input_ids[:, :-1]  ,attention_mask=__lowerCAmelCase  ,past_key_values=__lowerCAmelCase  ,position_ids=__lowerCAmelCase  ,)

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

												lowercase		      =		model(__lowerCAmelCase)

												lowercase		      =		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  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		2_0
												lowercase		      =		model_class_name(__lowerCAmelCase)

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

												lowercase		      =		model.init_cache(input_ids.shape[0]  ,__lowerCAmelCase)
												lowercase		      =		jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1))

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

												lowercase		      =		model(__lowerCAmelCase  ,attention_mask=__lowerCAmelCase)

												lowercase		      =		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 							lowercase						(   __lowerCamelCase			,    __lowerCamelCase			,    unittest.TestCase						):
							lowercase_			:			Tuple			   =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
							lowercase_			:			Optional[int]			   =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




							def 					A__ (							self):
												lowercase		      =		FlaxGPTNeoModelTester(self)




							def 					A__ (							self):
												for model_class_name in self.all_model_classes:
																	lowercase      ,		lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase)




							def 					A__ (							self):
												for model_class_name in self.all_model_classes:
																	lowercase      ,		lowercase      ,		lowercase		      =		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):
												lowercase		      =		GPTaTokenizer.from_pretrained('''gpt2'''  ,pad_token='''<|endoftext|>'''  ,padding_side='''left''')
												lowercase		      =		tokenizer(['''Hello this is a long string''', '''Hey''']  ,return_tensors='''np'''  ,padding=__lowerCAmelCase  ,truncation=__lowerCAmelCase)

												lowercase		      =		FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''')
												lowercase		      =		False
												lowercase		      =		model.config.eos_token_id

												lowercase		      =		jax.jit(model.generate)

												lowercase		      =		jit_generate(
												    inputs['''input_ids''']  ,attention_mask=inputs['''attention_mask''']  ,pad_token_id=tokenizer.pad_token_id).sequences

												lowercase		      =		tokenizer.batch_decode(__lowerCAmelCase  ,skip_special_tokens=__lowerCAmelCase)

												lowercase		      =		[
												    '''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):
												lowercase      ,		lowercase		      =		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		      =		self._prepare_for_class(__lowerCAmelCase  ,__lowerCAmelCase)
																						lowercase		      =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase		      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase		      =		getattr(__lowerCAmelCase  ,__lowerCAmelCase)

																						lowercase      ,		lowercase		      =		pt_inputs['''input_ids'''].shape
																						lowercase		      =		np.random.randint(0  ,seq_length - 1  ,size=(batch_size,))
																						for batch_idx, start_index in enumerate(__lowerCAmelCase):
																											lowercase		      =		0
																											lowercase		      =		1
																											lowercase		      =		0
																											lowercase		      =		1
																						lowercase		      =		pt_model_class(__lowerCAmelCase).eval()
																						lowercase		      =		model_class(__lowerCAmelCase  ,dtype=jnp.floataa)

																						lowercase		      =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,__lowerCAmelCase)
																						lowercase		      =		fx_state

																						with torch.no_grad():
																											lowercase		      =		pt_model(**__lowerCAmelCase).to_tuple()

																						lowercase		      =		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		      =		model_class.from_pretrained(__lowerCAmelCase  ,from_pt=__lowerCAmelCase)

																						lowercase		      =		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):
												lowercase      ,		lowercase		      =		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		      =		self._prepare_for_class(__lowerCAmelCase  ,__lowerCAmelCase)
																						lowercase		      =		{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						lowercase		      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						lowercase		      =		getattr(__lowerCAmelCase  ,__lowerCAmelCase)

																						lowercase		      =		pt_model_class(__lowerCAmelCase).eval()
																						lowercase		      =		model_class(__lowerCAmelCase  ,dtype=jnp.floataa)

																						lowercase		      =		load_flax_weights_in_pytorch_model(__lowerCAmelCase  ,fx_model.params)
																						lowercase      ,		lowercase		      =		pt_inputs['''input_ids'''].shape
																						lowercase		      =		np.random.randint(0  ,seq_length - 1  ,size=(batch_size,))
																						for batch_idx, start_index in enumerate(__lowerCAmelCase):
																											lowercase		      =		0
																											lowercase		      =		1
																											lowercase		      =		0
																											lowercase		      =		1

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

																						with torch.no_grad():
																											lowercase		      =		pt_model(**__lowerCAmelCase).to_tuple()

																						lowercase		      =		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		      =		pt_model_class.from_pretrained(__lowerCAmelCase  ,from_flax=__lowerCAmelCase)

																						with torch.no_grad():
																											lowercase		      =		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):
												for model_class_name in self.all_model_classes:
																	lowercase		      =		model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''')
																	lowercase		      =		model(np.ones((1, 1)))
																	self.assertIsNotNone(__lowerCAmelCase)





'''simple docstring'''

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput





@dataclass
class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =42
   lowerCamelCase_            =None
   lowerCamelCase_            =None





class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Tuple)   ->       Dict:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(x.a ,		30)
         self.assertIsNone(x.b)
         self.assertIsNone(x.c)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.d


   def __UpperCAmelCase							( self					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["b"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["c"]

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["c"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["b"]


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(list(x.values()) ,		[30])
         self.assertEqual(list(x.items()) ,		[("a", 30)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a"])

         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("b", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "b"])

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(list(x.keys()) ,		["a", "c"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("c", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "c"])

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.update({"d": 20})
         with self.assertRaises(__lowerCAmelCase):
               del x["a"]
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.pop("a")
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.setdefault("d" ,		32)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Optional[int]:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         lowercase_     			=   ModelOutputTest({"a": 30, "b": 10})
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)


   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         lowercase_     			=   ModelOutputTest([("a", 30), ("b", 10)])
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   ModelOutputTest([("a", 30), (10, 10)])

         lowercase_     			=   ModelOutputTest(a=(30, 30))
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(x.a ,		(30, 30))






