


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		:  List[Any]    ,     lowercase_		:  Tuple    ,     lowercase_		:  List[Any]=14    ,     lowercase_		:  Any=7    ,     lowercase_		:  Optional[Any]=True    ,     lowercase_		:  Optional[Any]=True    ,     lowercase_		:  int=True    ,     lowercase_		:  Tuple=99    ,     lowercase_		:  Optional[int]=32    ,     lowercase_		:  Union[str, Any]=5    ,     lowercase_		:  Optional[Any]=4    ,     lowercase_		:  Union[str, Any]=37    ,     lowercase_		:  int="gelu"    ,     lowercase_		:  Optional[Any]=0.1    ,     lowercase_		:  Tuple=0.1    ,     lowercase_		:  Any=512    ,     lowercase_		:  Any=0.02    ,     lowercase_		:  Tuple=None    ,     )      ->		List[str]:
													"""simple docstring"""




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






						def  lowerCamelCase (      self		:  List[Any]				)      ->		Union[str, Any]:
													"""simple docstring"""




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

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

													_lowerCamelCase :	Tuple						=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=__UpperCamelCase    ,     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  lowerCamelCase (      self		:  int				)      ->		Union[str, Any]:
													"""simple docstring"""




													_lowerCamelCase :	Any						=self.prepare_config_and_inputs()
													_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	Union[str, Any]						=config_and_inputs
													_lowerCamelCase :	Any						={'input_ids': input_ids, 'attention_mask': attention_mask}
													return config, inputs_dict






						def  lowerCamelCase (      self		:  Optional[int]				)      ->		Dict:
													"""simple docstring"""




													_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	Tuple						=self.prepare_config_and_inputs()

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

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






						def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  int    ,     lowercase_		:  Dict    ,     lowercase_		:  Any    ,     lowercase_		:  List[Any]				)      ->		int:
													"""simple docstring"""




													_lowerCamelCase :	List[Any]						=20
													_lowerCamelCase :	List[Any]						=model_class_name(__UpperCamelCase				)

													_lowerCamelCase :	Tuple						=model.init_cache(input_ids.shape[0]    ,     __UpperCamelCase				)
													_lowerCamelCase :	Any						=jnp.ones((input_ids.shape[0], max_decoder_length)    ,     dtype='i4'				)

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

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

													_lowerCamelCase :	List[Any]						=model(__UpperCamelCase				)

													_lowerCamelCase :	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  lowerCamelCase (      self		:  Tuple    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Union[str, Any]    ,     lowercase_		:  Optional[Any]    ,     lowercase_		:  str				)      ->		int:
													"""simple docstring"""




													_lowerCamelCase :	Optional[Any]						=20
													_lowerCamelCase :	List[str]						=model_class_name(__UpperCamelCase				)

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

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

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

													_lowerCamelCase :	Optional[int]						=model(__UpperCamelCase    ,     attention_mask=__UpperCamelCase				)
													_lowerCamelCase :	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  ):
						UpperCamelCase__							:						int   =(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
						UpperCamelCase__							:						Optional[Any]   =(FlaxXGLMForCausalLM,) if is_flax_available() else ()






						def  lowerCamelCase (      self		:  List[Any]				)      ->		Optional[int]:
													"""simple docstring"""




													_lowerCamelCase :	Union[str, Any]						=FlaxXGLMModelTester(self				)






						def  lowerCamelCase (      self		:  List[Any]				)      ->		Tuple:
													"""simple docstring"""




													for model_class_name in self.all_model_classes:
																				_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	Tuple						=self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward(__UpperCamelCase    ,     __UpperCamelCase    ,     __UpperCamelCase    ,     __UpperCamelCase				)






						def  lowerCamelCase (      self		:  str				)      ->		str:
													"""simple docstring"""




													for model_class_name in self.all_model_classes:
																				_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	Tuple						=self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward_with_attn_mask(
																				    __UpperCamelCase    ,     __UpperCamelCase    ,     __UpperCamelCase    ,     __UpperCamelCase				)






						@slow
						def  lowerCamelCase (      self		:  Dict				)      ->		Dict:
													"""simple docstring"""




													_lowerCamelCase :	Optional[Any]						=XGLMTokenizer.from_pretrained('XGLM'    ,     padding_side='left'				)
													_lowerCamelCase :	Any						=tokenizer(['Hello this is a long string', 'Hey']    ,     return_tensors='np'    ,     padding=__UpperCamelCase    ,     truncation=__UpperCamelCase				)

													_lowerCamelCase :	List[Any]						=FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
													_lowerCamelCase :	str						=1
													_lowerCamelCase :	Optional[Any]						=False

													_lowerCamelCase :	Dict						=jax.jit(model.generate				)

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

													_lowerCamelCase :	Optional[int]						=tokenizer.batch_decode(__UpperCamelCase    ,     skip_special_tokens=__UpperCamelCase				)

													_lowerCamelCase :	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(__UpperCamelCase    ,     __UpperCamelCase				)






						@is_pt_flax_cross_test
						def  lowerCamelCase (      self		:  List[str]				)      ->		List[Any]:
													"""simple docstring"""




													_lowerCamelCase			,  _lowerCamelCase :	Tuple						=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 :	Dict						=self._prepare_for_class(__UpperCamelCase    ,     __UpperCamelCase				)
																											_lowerCamelCase :	str						={k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

																											_lowerCamelCase			,  _lowerCamelCase :	str						=pt_inputs['input_ids'].shape
																											_lowerCamelCase :	Optional[int]						=np.random.randint(0    ,     seq_length - 1    ,     size=(batch_size,)				)
																											for batch_idx, start_index in enumerate(__UpperCamelCase				):
																																		_lowerCamelCase :	Optional[Any]						=0
																																		_lowerCamelCase :	Dict						=1
																																		_lowerCamelCase :	Dict						=0
																																		_lowerCamelCase :	List[Any]						=1
																											_lowerCamelCase :	List[str]						=pt_model_class(__UpperCamelCase				).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 :	Dict						=False
																											_lowerCamelCase :	str						=model_class(__UpperCamelCase    ,     dtype=jnp.floataa				)
																											_lowerCamelCase :	Optional[int]						=convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,     __UpperCamelCase				)
																											_lowerCamelCase :	Optional[int]						=fx_state

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

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

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

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






						@is_pt_flax_cross_test
						def  lowerCamelCase (      self		:  Tuple				)      ->		List[Any]:
													"""simple docstring"""




													_lowerCamelCase			,  _lowerCamelCase :	Optional[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
																											_lowerCamelCase :	str						=self._prepare_for_class(__UpperCamelCase    ,     __UpperCamelCase				)
																											_lowerCamelCase :	Union[str, Any]						={k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

																											_lowerCamelCase :	int						=pt_model_class(__UpperCamelCase				).eval()
																											_lowerCamelCase :	Any						=False
																											_lowerCamelCase :	Any						=model_class(__UpperCamelCase    ,     dtype=jnp.floataa				)

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

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

																											with torch.no_grad():
																																		_lowerCamelCase :	Optional[Any]						=pt_model(**__UpperCamelCase				).to_tuple()

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

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

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

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






						@slow
						def  lowerCamelCase (      self		:  Tuple				)      ->		List[Any]:
													"""simple docstring"""




													for model_class_name in self.all_model_classes:
																				_lowerCamelCase :	Optional[int]						=model_class_name.from_pretrained('facebook/xglm-564M'				)
																				_lowerCamelCase :	Optional[Any]						=model(np.ones((1, 1)				)				)
																				self.assertIsNotNone(__UpperCamelCase				)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline


