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 		a__						:

					def __init__(   self				:	Optional[Any],_A				:	Any,_A				:	str=14,_A				:	Tuple=7,_A				:	Tuple=True,_A				:	Optional[Any]=True,_A				:	Optional[int]=True,_A				:	str=99,_A				:	Tuple=32,_A				:	Dict=5,_A				:	List[str]=4,_A				:	Optional[Any]=37,_A				:	Union[str, Any]="gelu",_A				:	Optional[Any]=0.1,_A				:	Dict=0.1,_A				:	Dict=512,_A				:	Optional[int]=0.02,_A				:	Union[str, Any]=None,):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		Tuple    				=   parent
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   batch_size
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   seq_length
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   is_training
								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   use_input_mask
								SCREAMING_SNAKE_CASE_  :		str    				=   use_labels
								SCREAMING_SNAKE_CASE_  :		Dict    				=   vocab_size
								SCREAMING_SNAKE_CASE_  :		Any    				=   d_model
								SCREAMING_SNAKE_CASE_  :		int    				=   num_hidden_layers
								SCREAMING_SNAKE_CASE_  :		int    				=   num_attention_heads
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   ffn_dim
								SCREAMING_SNAKE_CASE_  :		List[str]    				=   activation_function
								SCREAMING_SNAKE_CASE_  :		int    				=   activation_dropout
								SCREAMING_SNAKE_CASE_  :		Tuple    				=   attention_dropout
								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   max_position_embeddings
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   initializer_range
								SCREAMING_SNAKE_CASE_  :		Dict    				=   None
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   0
								SCREAMING_SNAKE_CASE_  :		List[str]    				=   2
								SCREAMING_SNAKE_CASE_  :		Tuple    				=   1

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		List[str]    				=   np.clip(ids_tensor([self.batch_size, self.seq_length],self.vocab_size   ),3,self.vocab_size   )

								SCREAMING_SNAKE_CASE_  :		Any    				=   None
								if self.use_input_mask:
											SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   random_attention_mask([self.batch_size, self.seq_length]   )

								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   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				:	int   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   config_and_inputs
								SCREAMING_SNAKE_CASE_  :		Tuple    				=   {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict

					def     __UpperCamelCase       (   self				:	str   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		str    				=   self.prepare_config_and_inputs()

								SCREAMING_SNAKE_CASE_  :		List[str]    				=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   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				:	List[str],_A				:	List[str],_A				:	List[str],_A				:	Dict,_A				:	int   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   20
								SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model_class_name(__lowerCamelCase   )

								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model.init_cache(input_ids.shape[0],__lowerCamelCase   )
								SCREAMING_SNAKE_CASE_  :		Any    				=   jnp.ones((input_ids.shape[0], max_decoder_length),dtype="i4"   )

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

								SCREAMING_SNAKE_CASE_  :		Tuple    				=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype="i4"   )
								SCREAMING_SNAKE_CASE_  :		int    				=   model(
								    input_ids[:, -1:],attention_mask=__lowerCamelCase,past_key_values=outputs_cache.past_key_values,position_ids=__lowerCamelCase,)

								SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model(__lowerCamelCase   )

								SCREAMING_SNAKE_CASE_  :		Union[str, 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     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	Optional[int],_A				:	Tuple,_A				:	Dict,_A				:	List[str]   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		List[str]    				=   20
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   model_class_name(__lowerCamelCase   )

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

								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   model.init_cache(input_ids.shape[0],__lowerCamelCase   )
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1   )[None, :],(input_ids.shape[0], input_ids.shape[-1] - 1)   )

								SCREAMING_SNAKE_CASE_  :		Any    				=   model(
								    input_ids[:, :-1],attention_mask=__lowerCamelCase,past_key_values=__lowerCamelCase,position_ids=__lowerCamelCase,)
								SCREAMING_SNAKE_CASE_  :		Any    				=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],dtype="i4"   )
								SCREAMING_SNAKE_CASE_  :		str    				=   model(
								    input_ids[:, -1:],past_key_values=outputs_cache.past_key_values,attention_mask=__lowerCamelCase,position_ids=__lowerCamelCase,)

								SCREAMING_SNAKE_CASE_  :		int    				=   model(__lowerCamelCase,attention_mask=__lowerCamelCase   )
								SCREAMING_SNAKE_CASE_  :		Union[str, 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_sentencepiece
@require_flax
class 		a__						( UpperCamelCase__   ,		UpperCamelCase__   ,		unittest.TestCase ):
					A     	=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					A     	=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		Tuple    				=   FlaxXGLMModelTester(self   )

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







								"""simple docstring"""






								for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE_  :		int    				=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







								"""simple docstring"""






								for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   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				:	Union[str, Any]   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		int    				=   XGLMTokenizer.from_pretrained("XGLM",padding_side="left"   )
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   tokenizer(["Hello this is a long string", "Hey"],return_tensors="np",padding=__lowerCamelCase,truncation=__lowerCamelCase   )

								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"   )
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   1
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   False

								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   jax.jit(model.generate   )

								SCREAMING_SNAKE_CASE_  :		int    				=   jit_generate(inputs["input_ids"],attention_mask=inputs["attention_mask"]   ).sequences

								SCREAMING_SNAKE_CASE_  :		str    				=   tokenizer.batch_decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase   )

								SCREAMING_SNAKE_CASE_  :		int    				=   [
								    "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				:	List[str]   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		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
														SCREAMING_SNAKE_CASE_  :		Any    				=   self._prepare_for_class(__lowerCamelCase,__lowerCamelCase   )
														SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

														SCREAMING_SNAKE_CASE_  :		Any    				=   pt_inputs["input_ids"].shape
														SCREAMING_SNAKE_CASE_  :		Any    				=   np.random.randint(0,seq_length - 1,size=(batch_size,)   )
														for batch_idx, start_index in enumerate(__lowerCamelCase   ):
																	SCREAMING_SNAKE_CASE_  :		int    				=   0
																	SCREAMING_SNAKE_CASE_  :		Any    				=   1
																	SCREAMING_SNAKE_CASE_  :		int    				=   0
																	SCREAMING_SNAKE_CASE_  :		int    				=   1
														SCREAMING_SNAKE_CASE_  :		str    				=   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.
														SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   False
														SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model_class(__lowerCamelCase,dtype=jnp.floataa   )
														SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   convert_pytorch_state_dict_to_flax(pt_model.state_dict(),__lowerCamelCase   )
														SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   fx_state

														with torch.no_grad():
																	SCREAMING_SNAKE_CASE_  :		List[Any]    				=   pt_model(**__lowerCamelCase   ).to_tuple()

														SCREAMING_SNAKE_CASE_  :		Optional[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   )
																	SCREAMING_SNAKE_CASE_  :		Tuple    				=   model_class.from_pretrained(__lowerCamelCase,from_pt=__lowerCamelCase   )

														SCREAMING_SNAKE_CASE_  :		int    				=   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				:	Tuple   ):







								"""simple docstring"""






								SCREAMING_SNAKE_CASE_  :		int    				=   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
														SCREAMING_SNAKE_CASE_  :		Any    				=   self._prepare_for_class(__lowerCamelCase,__lowerCamelCase   )
														SCREAMING_SNAKE_CASE_  :		Dict    				=   {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

														SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   pt_model_class(__lowerCamelCase   ).eval()
														SCREAMING_SNAKE_CASE_  :		str    				=   False
														SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model_class(__lowerCamelCase,dtype=jnp.floataa   )

														SCREAMING_SNAKE_CASE_  :		Dict    				=   load_flax_weights_in_pytorch_model(__lowerCamelCase,fx_model.params   )
														SCREAMING_SNAKE_CASE_  :		List[Any]    				=   pt_inputs["input_ids"].shape
														SCREAMING_SNAKE_CASE_  :		List[Any]    				=   np.random.randint(0,seq_length - 1,size=(batch_size,)   )
														for batch_idx, start_index in enumerate(__lowerCamelCase   ):
																	SCREAMING_SNAKE_CASE_  :		Tuple    				=   0
																	SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   1
																	SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   0
																	SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   1

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

														with torch.no_grad():
																	SCREAMING_SNAKE_CASE_  :		Any    				=   pt_model(**__lowerCamelCase   ).to_tuple()

														SCREAMING_SNAKE_CASE_  :		List[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   )
																	SCREAMING_SNAKE_CASE_  :		List[str]    				=   pt_model_class.from_pretrained(__lowerCamelCase,from_flax=__lowerCamelCase   )

														with torch.no_grad():
																	SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   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				:	Optional[Any]   ):







								"""simple docstring"""






								for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE_  :		Any    				=   model_class_name.from_pretrained("facebook/xglm-564M"   )
											SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(np.ones((1, 1)   )   )
											self.assertIsNotNone(__lowerCamelCase   )

__lowerCamelCase :     List[Any]          =  {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'




def 				_snake_case     (       lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   hex_string.strip().lower()
		if not hex_string:
				raise ValueError("Empty string was passed to the function"     )
		SCREAMING_SNAKE_CASE_  :		str    				=   hex_string[0] == "-"
		if is_negative:
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   hex_string[1:]
		if not all(char in hex_table for char in hex_string     ):
				raise ValueError("Non-hexadecimal value was passed to the function"     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   0
		for char in hex_string:
				SCREAMING_SNAKE_CASE_  :		Tuple    				=   1_6 * decimal_number + hex_table[char]
		return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()
