"""simple docstring"""




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__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=14		,__UpperCAmelCase=7		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=99		,__UpperCAmelCase=32		,__UpperCAmelCase=5		,__UpperCAmelCase=4		,__UpperCAmelCase=37		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=5_12		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=None		,)       ->					Dict:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				seq_length
													A__						  =				is_training
													A__						  =				use_input_mask
													A__						  =				use_labels
													A__						  =				vocab_size
													A__						  =				d_model
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				ffn_dim
													A__						  =				activation_function
													A__						  =				activation_dropout
													A__						  =				attention_dropout
													A__						  =				max_position_embeddings
													A__						  =				initializer_range
													A__						  =				None
													A__						  =				0
													A__						  =				2
													A__						  =				1





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				np.clip(ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size  )		,3		,self.vocab_size  )

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

													A__						  =				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=a__		,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 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				self.prepare_config_and_inputs()
													A__						  =				config_and_inputs
													A__						  =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
													return config, inputs_dict





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.prepare_config_and_inputs()

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

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





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					str:
													A__						  =				20
													A__						  =				model_class_name(a__  )

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

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

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

													A__						  =				model(a__  )

													A__						  =				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		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				20
													A__						  =				model_class_name(a__  )

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

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

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

													A__						  =				model(a__		,attention_mask=a__  )
													A__						  =				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__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    List[Any]    				=		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
						lowerCAmelCase__	:    Union[str, Any]    				=		(FlaxXGLMForCausalLM,) if is_flax_available() else ()





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				FlaxXGLMModelTester(self  )





						def 					snake_case__						(							self  )       ->					int:






													for model_class_name in self.all_model_classes:
																				A__						  =				self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward(a__		,a__		,a__		,a__  )





						def 					snake_case__						(							self  )       ->					List[str]:






													for model_class_name in self.all_model_classes:
																				A__						  =				self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward_with_attn_mask(
																				    a__		,a__		,a__		,a__  )





						@slow
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XGLMTokenizer.from_pretrained('XGLM'		,padding_side='left'  )
													A__						  =				tokenizer(['Hello this is a long string', 'Hey']		,return_tensors='np'		,padding=a__		,truncation=a__  )

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

													A__						  =				jax.jit(model.generate  )

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

													A__						  =				tokenizer.batch_decode(a__		,skip_special_tokens=a__  )

													A__						  =				[
													    '''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(a__		,a__  )





						@is_pt_flax_cross_test
						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				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
																											A__						  =				self._prepare_for_class(a__		,a__  )
																											A__						  =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																											A__						  =				pt_inputs['''input_ids'''].shape
																											A__						  =				np.random.randint(0		,seq_length - 1		,size=(batch_size,)  )
																											for batch_idx, start_index in enumerate(a__  ):
																																		A__						  =				0
																																		A__						  =				1
																																		A__						  =				0
																																		A__						  =				1
																											A__						  =				pt_model_class(a__  ).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.
																											A__						  =				False
																											A__						  =				model_class(a__		,dtype=jnp.floataa  )
																											A__						  =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,a__  )
																											A__						  =				fx_state

																											with torch.no_grad():
																																		A__						  =				pt_model(**a__  ).to_tuple()

																											A__						  =				fx_model(**a__  ).to_tuple()
																											self.assertEqual(len(a__  )		,len(a__  )		,'Output lengths differ between Flax and PyTorch'  )
																											for fx_output, pt_output in zip(a__		,a__  ):
																																		self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2  )

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		pt_model.save_pretrained(a__  )
																																		A__						  =				model_class.from_pretrained(a__		,from_pt=a__  )

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





						@is_pt_flax_cross_test
						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				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
																											A__						  =				self._prepare_for_class(a__		,a__  )
																											A__						  =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																											A__						  =				pt_model_class(a__  ).eval()
																											A__						  =				False
																											A__						  =				model_class(a__		,dtype=jnp.floataa  )

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

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

																											with torch.no_grad():
																																		A__						  =				pt_model(**a__  ).to_tuple()

																											A__						  =				fx_model(**a__  ).to_tuple()
																											self.assertEqual(len(a__  )		,len(a__  )		,'Output lengths differ between Flax and PyTorch'  )
																											for fx_output, pt_output in zip(a__		,a__  ):
																																		self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2  )

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		fx_model.save_pretrained(a__  )
																																		A__						  =				pt_model_class.from_pretrained(a__		,from_flax=a__  )

																											with torch.no_grad():
																																		A__						  =				pt_model_loaded(**a__  ).to_tuple()

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





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






													for model_class_name in self.all_model_classes:
																				A__						  =				model_class_name.from_pretrained('facebook/xglm-564M'  )
																				A__						  =				model(np.ones((1, 1)  )  )
																				self.assertIsNotNone(a__  )









import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






