import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
     import torch



@require_flax
class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_=14					,					A_=7					,					A_=True					,					A_=True					,					A_=True					,					A_=99					,					A_=32					,					A_=5					,					A_=4					,					A_=37					,					A_="gelu"					,					A_=0.1					,					A_=0.1					,					A_=5_12					,					A_=0.02					,					A_=None					,					)			->					int:






           """simple docstring"""
           _lowerCamelCase           =					parent
           _lowerCamelCase           =					batch_size
           _lowerCamelCase           =					seq_length
           _lowerCamelCase           =					is_training
           _lowerCamelCase           =					use_input_mask
           _lowerCamelCase           =					use_labels
           _lowerCamelCase           =					vocab_size
           _lowerCamelCase           =					d_model
           _lowerCamelCase           =					num_hidden_layers
           _lowerCamelCase           =					num_attention_heads
           _lowerCamelCase           =					ffn_dim
           _lowerCamelCase           =					activation_function
           _lowerCamelCase           =					activation_dropout
           _lowerCamelCase           =					attention_dropout
           _lowerCamelCase           =					max_position_embeddings
           _lowerCamelCase           =					initializer_range
           _lowerCamelCase           =					None
           _lowerCamelCase           =					0
           _lowerCamelCase           =					2
           _lowerCamelCase           =					1



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					np.clip(ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size    )					,					3					,					self.vocab_size    )

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

           _lowerCamelCase           =					XGLMConfig(
               vocab_size=self.vocab_size					,					d_model=self.hidden_size					,					num_layers=self.num_hidden_layers					,					attention_heads=self.num_attention_heads					,					ffn_dim=self.ffn_dim					,					activation_function=self.activation_function					,					activation_dropout=self.activation_dropout					,					attention_dropout=self.attention_dropout					,					max_position_embeddings=self.max_position_embeddings					,					initializer_range=self.initializer_range					,					use_cache=lowerCamelCase_					,					bos_token_id=self.bos_token_id					,					eos_token_id=self.eos_token_id					,					pad_token_id=self.pad_token_id					,					)

           return (config, input_ids, input_mask)



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					self.prepare_config_and_inputs()
           _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					config_and_inputs
           _lowerCamelCase           =					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict



     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					self.prepare_config_and_inputs()

           _lowerCamelCase           =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
           _lowerCamelCase           =					ids_tensor([self.batch_size, self.seq_length]					,					vocab_size=2    )

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					20
           _lowerCamelCase           =					model_class_name(lowerCamelCase_    )

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

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

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

           _lowerCamelCase           =					model(lowerCamelCase_    )

           _lowerCamelCase           =					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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					20
           _lowerCamelCase           =					model_class_name(lowerCamelCase_    )

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

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

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

           _lowerCamelCase           =					model(lowerCamelCase_					,					attention_mask=lowerCamelCase_    )
           _lowerCamelCase           =					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_sentencepiece
@require_flax
class 			UpperCamelCase					(			lowerCamelCase_ ,	lowerCamelCase_ ,	unittest.TestCase			):

     '''simple docstring'''

     A_					    =	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     A_					    =	(FlaxXGLMForCausalLM,) if is_flax_available() else ()



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					FlaxXGLMModelTester(self    )



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_    )



     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_					,					lowerCamelCase_    )



     @slow
     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					XGLMTokenizer.from_pretrained('''XGLM'''					,					padding_side='''left'''    )
           _lowerCamelCase           =					tokenizer(['''Hello this is a long string''', '''Hey''']					,					return_tensors='''np'''					,					padding=lowerCamelCase_					,					truncation=lowerCamelCase_    )

           _lowerCamelCase           =					FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
           _lowerCamelCase           =					1
           _lowerCamelCase           =					False

           _lowerCamelCase           =					jax.jit(model.generate    )

           _lowerCamelCase           =					jit_generate(inputs['''input_ids''']					,					attention_mask=inputs['''attention_mask''']    ).sequences

           _lowerCamelCase           =					tokenizer.batch_decode(lowerCamelCase_					,					skip_special_tokens=lowerCamelCase_    )

           _lowerCamelCase           =					[
               '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
               '''Hey, I\'m a newbie to the forum and I\'''',
           ]

           self.assertListEqual(lowerCamelCase_					,					lowerCamelCase_    )



     @is_pt_flax_cross_test
     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					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           =					self._prepare_for_class(lowerCamelCase_					,					lowerCamelCase_    )
                       _lowerCamelCase           =					{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                       _lowerCamelCase     ,       _lowerCamelCase           =					pt_inputs['''input_ids'''].shape
                       _lowerCamelCase           =					np.random.randint(0					,					seq_length - 1					,					size=(batch_size,)    )
                       for batch_idx, start_index in enumerate(lowerCamelCase_    ):
                             _lowerCamelCase           =					0
                             _lowerCamelCase           =					1
                             _lowerCamelCase           =					0
                             _lowerCamelCase           =					1
                       _lowerCamelCase           =					pt_model_class(lowerCamelCase_    ).eval()
                       # Flax models don't use the `use_cache` option and cache is not returned as a default.
                       # So we disable `use_cache` here for PyTorch model.
                       _lowerCamelCase           =					False
                       _lowerCamelCase           =					model_class(lowerCamelCase_					,					dtype=jnp.floataa    )
                       _lowerCamelCase           =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,					lowerCamelCase_    )
                       _lowerCamelCase           =					fx_state

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

                       _lowerCamelCase           =					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           =					model_class.from_pretrained(lowerCamelCase_					,					from_pt=lowerCamelCase_    )

                       _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					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           =					self._prepare_for_class(lowerCamelCase_					,					lowerCamelCase_    )
                       _lowerCamelCase           =					{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                       _lowerCamelCase           =					pt_model_class(lowerCamelCase_    ).eval()
                       _lowerCamelCase           =					False
                       _lowerCamelCase           =					model_class(lowerCamelCase_					,					dtype=jnp.floataa    )

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

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

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

                       _lowerCamelCase           =					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           =					pt_model_class.from_pretrained(lowerCamelCase_					,					from_flax=lowerCamelCase_    )

                       with torch.no_grad():
                             _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase           =					model_class_name.from_pretrained('''facebook/xglm-564M'''    )
                 _lowerCamelCase           =					model(np.ones((1, 1)    )    )
                 self.assertIsNotNone(lowerCamelCase_    )



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

