



"""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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_4 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=3_2 ,				lowerCamelCase__=5 ,				lowerCamelCase__=4 ,				lowerCamelCase__=3_7 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=5_1_2 ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=None ,				):
									_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 	snake_case__					(			self      ):
									_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=__SCREAMING_SNAKE_CASE ,				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      ):
									_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 	snake_case__					(			self      ):
									_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 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(__SCREAMING_SNAKE_CASE      )

									_lowerCamelCase					     = model.init_cache(input_ids.shape[0] ,				__SCREAMING_SNAKE_CASE      )
									_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=__SCREAMING_SNAKE_CASE ,				past_key_values=__SCREAMING_SNAKE_CASE ,				position_ids=__SCREAMING_SNAKE_CASE ,				)

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

									_lowerCamelCase					     = model(__SCREAMING_SNAKE_CASE      )

									_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 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 2_0
									_lowerCamelCase					     = model_class_name(__SCREAMING_SNAKE_CASE      )

									_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] ,				__SCREAMING_SNAKE_CASE      )
									_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=__SCREAMING_SNAKE_CASE ,				past_key_values=__SCREAMING_SNAKE_CASE ,				position_ids=__SCREAMING_SNAKE_CASE ,				)
									_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=__SCREAMING_SNAKE_CASE ,				position_ids=__SCREAMING_SNAKE_CASE ,				)

									_lowerCamelCase					     = model(__SCREAMING_SNAKE_CASE ,				attention_mask=__SCREAMING_SNAKE_CASE      )
									_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 							lowerCamelCase_(      lowerCAmelCase_, lowerCAmelCase_, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				int        =       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					lowercase__      :				str        =       (FlaxXGLMForCausalLM,) if is_flax_available() else ()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = FlaxXGLMModelTester(self      )

					def 	snake_case__					(			self      ):

									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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )

					def 	snake_case__					(			self      ):

									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(
													    __SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )

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

									_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(__SCREAMING_SNAKE_CASE ,				skip_special_tokens=__SCREAMING_SNAKE_CASE      )

									_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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )

					@is_pt_flax_cross_test
					def 	snake_case__					(			self      ):
									_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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )
																	_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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )

																	_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(__SCREAMING_SNAKE_CASE      ):
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = 1
																	_lowerCamelCase					     = pt_model_class(__SCREAMING_SNAKE_CASE      ).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(__SCREAMING_SNAKE_CASE ,				dtype=jnp.floataa      )
																	_lowerCamelCase					     = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,				__SCREAMING_SNAKE_CASE      )
																	_lowerCamelCase					     = fx_state

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

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

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

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

					@is_pt_flax_cross_test
					def 	snake_case__					(			self      ):
									_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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )
																	_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(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE      )

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

																	_lowerCamelCase					     = load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE ,				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(__SCREAMING_SNAKE_CASE      ):
																					_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(**__SCREAMING_SNAKE_CASE      ).to_tuple()

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

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

																	with torch.no_grad():
																					_lowerCamelCase					     = pt_model_loaded(**__SCREAMING_SNAKE_CASE      ).to_tuple()

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





					@slow
					def 	snake_case__					(			self      ):

									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(__SCREAMING_SNAKE_CASE      )







'''simple docstring'''







# Lint as: python3

import hashlib
from typing import Union





def    _lowerCAmelCase						(						lowercase	)					->					bytes:
			if isinstance(lowercase					,	lowercase	):
						# Data already in bytes, returns as it as
						return hash_data
			elif isinstance(lowercase					,	lowercase	):
						# We keep the data as it as for it ot be later encoded to UTF-8
						# However replace `\\` with `/` for Windows compatibility
						__lowerCAmelCase											=				hash_data.replace("""\\"""					,	"""/"""	)
			elif isinstance(lowercase					,	lowercase	):
						__lowerCAmelCase											=				str(lowercase	)
			else:
						# If data is not of the required type, raise error
						raise InvalidKeyError(lowercase	)

			return hash_data.encode("""utf-8"""	)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
					__lowerCAmelCase											=				f'\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE			)}'
					__lowerCAmelCase											=				"""\nKeys should be either str, int or bytes type"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=""			):



					'''simple docstring'''

					__lowerCAmelCase											=				key
					__lowerCAmelCase											=				duplicate_key_indices
					__lowerCAmelCase											=				fix_msg
					__lowerCAmelCase											=				"""Found multiple examples generated with the same key"""
					if len(__SCREAMING_SNAKE_CASE			) <= 20:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE			)} have the key {key}'
					else:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(duplicate_key_indices[:20]			)}... ({len(__SCREAMING_SNAKE_CASE			) - 20} more) have the key {key}'
					__lowerCAmelCase											=				"""\n""" + fix_msg if fix_msg else """"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE			)			)







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				self._split_mda.copy()
					__lowerCAmelCase											=				_as_bytes(__SCREAMING_SNAKE_CASE			)
					mda.update(__SCREAMING_SNAKE_CASE			)
					# Convert to integer with hexadecimal conversion
					return int(mda.hexdigest(),16			)

