
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 		__magic_name__   :





		def __init__(							self    :	Optional[int]						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	Dict=1_4						,  lowerCamelCase__    :	Union[str, Any]=7						,  lowerCamelCase__    :	int=True						,  lowerCamelCase__    :	Dict=True						,  lowerCamelCase__    :	str=True						,  lowerCamelCase__    :	Any=9_9						,  lowerCamelCase__    :	Dict=3_2						,  lowerCamelCase__    :	Any=5						,  lowerCamelCase__    :	Dict=4						,  lowerCamelCase__    :	Union[str, Any]=3_7						,  lowerCamelCase__    :	int="gelu"						,  lowerCamelCase__    :	Optional[Any]=0.1						,  lowerCamelCase__    :	int=0.1						,  lowerCamelCase__    :	Optional[int]=5_1_2						,  lowerCamelCase__    :	Any=0.0_2						,  lowerCamelCase__    :	List[Any]=None						,  ):
							lowerCAmelCase		:					Tuple  	=     parent
							lowerCAmelCase		:					Optional[Any]  	=     batch_size
							lowerCAmelCase		:					List[str]  	=     seq_length
							lowerCAmelCase		:					Union[str, Any]  	=     is_training
							lowerCAmelCase		:					List[Any]  	=     use_input_mask
							lowerCAmelCase		:					int  	=     use_labels
							lowerCAmelCase		:					Any  	=     vocab_size
							lowerCAmelCase		:					Optional[Any]  	=     d_model
							lowerCAmelCase		:					Any  	=     num_hidden_layers
							lowerCAmelCase		:					Tuple  	=     num_attention_heads
							lowerCAmelCase		:					Union[str, Any]  	=     ffn_dim
							lowerCAmelCase		:					str  	=     activation_function
							lowerCAmelCase		:					Optional[Any]  	=     activation_dropout
							lowerCAmelCase		:					Dict  	=     attention_dropout
							lowerCAmelCase		:					Optional[Any]  	=     max_position_embeddings
							lowerCAmelCase		:					str  	=     initializer_range
							lowerCAmelCase		:					Optional[Any]  	=     None
							lowerCAmelCase		:					Dict  	=     0
							lowerCAmelCase		:					List[str]  	=     2
							lowerCAmelCase		:					int  	=     1





		def       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Optional[Any]  	=     np.clip(ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size				)						,  3						,  self.vocab_size				)

							lowerCAmelCase		:					List[str]  	=     None
							if self.use_input_mask:
												lowerCAmelCase		:					List[Any]  	=     random_attention_mask([self.batch_size, self.seq_length]				)

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





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase		:					str  	=     self.prepare_config_and_inputs()

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

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





		def       _A							(							self    :	int						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[int]						,  lowerCamelCase__    :	List[str]				):
							lowerCAmelCase		:					int  	=     2_0
							lowerCAmelCase		:					List[str]  	=     model_class_name(lowerCamelCase__				)

							lowerCAmelCase		:					Union[str, Any]  	=     model.init_cache(input_ids.shape[0]						,  lowerCamelCase__				)
							lowerCAmelCase		:					Dict  	=     jnp.ones((input_ids.shape[0], max_decoder_length)						,  dtype='''i4'''				)

							lowerCAmelCase		:					Union[str, Any]  	=     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)
							lowerCAmelCase		:					Optional[Any]  	=     model(
							    input_ids[:, :-1]						,  attention_mask=lowerCamelCase__						,  past_key_values=lowerCamelCase__						,  position_ids=lowerCamelCase__						,  )

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

							lowerCAmelCase		:					str  	=     model(lowerCamelCase__				)

							lowerCAmelCase		:					Dict  	=     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       _A							(							self    :	Tuple						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	Dict						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     2_0
							lowerCAmelCase		:					Optional[int]  	=     model_class_name(lowerCamelCase__				)

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

							lowerCAmelCase		:					List[str]  	=     model.init_cache(input_ids.shape[0]						,  lowerCamelCase__				)
							lowerCAmelCase		:					Tuple  	=     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)

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

							lowerCAmelCase		:					List[str]  	=     model(lowerCamelCase__						,  attention_mask=lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     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 		__magic_name__   (						snake_case,				snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
		_lowerCAmelCase		=	(FlaxXGLMForCausalLM,) if is_flax_available() else ()





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					Optional[Any]  	=     FlaxXGLMModelTester(self				)





		def       _A							(							self    :	str				):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase		:					Dict  	=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)





		def       _A							(							self    :	int				):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase		:					Optional[Any]  	=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)





		@slow
		def       _A							(							self    :	int				):
							lowerCAmelCase		:					str  	=     XGLMTokenizer.from_pretrained('''XGLM'''						,  padding_side='''left'''				)
							lowerCAmelCase		:					str  	=     tokenizer(['''Hello this is a long string''', '''Hey''']						,  return_tensors='''np'''						,  padding=lowerCamelCase__						,  truncation=lowerCamelCase__				)

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

							lowerCAmelCase		:					List[str]  	=     jax.jit(model.generate				)

							lowerCAmelCase		:					List[Any]  	=     jit_generate(inputs['''input_ids''']						,  attention_mask=inputs['''attention_mask''']				).sequences

							lowerCAmelCase		:					Optional[int]  	=     tokenizer.batch_decode(lowerCamelCase__						,  skip_special_tokens=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[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       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase	,							lowerCAmelCase		:					Dict  	=     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		:					Tuple  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)
																	lowerCAmelCase		:					Dict  	=     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

																	with torch.no_grad():
																						lowerCAmelCase		:					str  	=     pt_model(**lowerCamelCase__				).to_tuple()

																	lowerCAmelCase		:					Optional[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:
																						pt_model.save_pretrained(lowerCamelCase__				)
																						lowerCAmelCase		:					Union[str, Any]  	=     model_class.from_pretrained(lowerCamelCase__						,  from_pt=lowerCamelCase__				)

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

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

																	lowerCAmelCase		:					Tuple  	=     pt_model_class(lowerCamelCase__				).eval()
																	lowerCAmelCase		:					Union[str, Any]  	=     False
																	lowerCAmelCase		:					str  	=     model_class(lowerCamelCase__						,  dtype=jnp.floataa				)

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

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

																	with torch.no_grad():
																						lowerCAmelCase		:					Dict  	=     pt_model(**lowerCamelCase__				).to_tuple()

																	lowerCAmelCase		:					str  	=     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		:					Dict  	=     pt_model_class.from_pretrained(lowerCamelCase__						,  from_flax=lowerCamelCase__				)

																	with torch.no_grad():
																						lowerCAmelCase		:					str  	=     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       _A							(							self    :	Optional[int]				):
							for model_class_name in self.all_model_classes:
												lowerCAmelCase		:					List[Any]  	=     model_class_name.from_pretrained('''facebook/xglm-564M'''				)
												lowerCAmelCase		:					int  	=     model(np.ones((1, 1)				)				)
												self.assertIsNotNone(lowerCamelCase__				)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
