



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase		:      List[str]					={"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:      Any					=["ViTFeatureExtractor"]
		_lowercase		:      List[Any]					=["ViTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:      int					=[
		    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ViTForImageClassification",
		    "ViTForMaskedImageModeling",
		    "ViTModel",
		    "ViTPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:      int					=[
		    "TFViTForImageClassification",
		    "TFViTModel",
		    "TFViTPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:      List[Any]					=[
		    "FlaxViTForImageClassification",
		    "FlaxViTModel",
		    "FlaxViTPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vit import ViTFeatureExtractor
				from .image_processing_vit import ViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit import (
				    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTForImageClassification,
				    ViTForMaskedImageModeling,
				    ViTModel,
				    ViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
		import sys

		_lowercase		:      int					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]=14					,      __lowerCamelCase							:			str=7					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Optional[int]=False					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			List[Any]=99					,      __lowerCamelCase							:			Optional[Any]=32					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[Any]=4					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			List[Any]=37					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			Any=0.1					,      __lowerCamelCase							:			Any=512					,      __lowerCamelCase							:			Dict=0.0_2					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  parent
             lowerCamelCase__		:				Any    =  batch_size
             lowerCamelCase__		:				Tuple    =  seq_length
             lowerCamelCase__		:				List[str]    =  is_training
             lowerCamelCase__		:				List[Any]    =  use_input_mask
             lowerCamelCase__		:				Optional[Any]    =  use_token_type_ids
             lowerCamelCase__		:				Optional[Any]    =  use_labels
             lowerCamelCase__		:				List[Any]    =  vocab_size
             lowerCamelCase__		:				Any    =  hidden_size
             lowerCamelCase__		:				int    =  rotary_dim
             lowerCamelCase__		:				List[str]    =  num_hidden_layers
             lowerCamelCase__		:				int    =  num_attention_heads
             lowerCamelCase__		:				Optional[int]    =  intermediate_size
             lowerCamelCase__		:				Tuple    =  hidden_act
             lowerCamelCase__		:				Optional[int]    =  hidden_dropout_prob
             lowerCamelCase__		:				Optional[Any]    =  attention_probs_dropout_prob
             lowerCamelCase__		:				str    =  max_position_embeddings
             lowerCamelCase__		:				Tuple    =  initializer_range
             lowerCamelCase__		:				Any    =  None
             lowerCamelCase__		:				Optional[Any]    =  vocab_size - 1
             lowerCamelCase__		:				List[Any]    =  vocab_size - 1
             lowerCamelCase__		:				str    =  vocab_size - 1





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

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

             lowerCamelCase__		:				Dict    =  GPTJConfig(
                 vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=self.max_position_embeddings					,      use_cache=__lowerCamelCase					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      rotary_dim=self.rotary_dim					,      )

             return (config, input_ids, input_mask)





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  config_and_inputs
             lowerCamelCase__		:				Tuple    =  {"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  20
             lowerCamelCase__		:				Tuple    =  model_class_name(__lowerCamelCase       )

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

             lowerCamelCase__		:				str    =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1       )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )
             lowerCamelCase__		:				Tuple    =  model(
                 input_ids[:, :-1]					,      attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      )

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

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase       )

             lowerCamelCase__		:				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}"       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  20
             lowerCamelCase__		:				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__		:				Dict    =  model.init_cache(input_ids.shape[0]					,      __lowerCamelCase       )
             lowerCamelCase__		:				Any    =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1       )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

             lowerCamelCase__		:				Tuple    =  model(__lowerCamelCase					,      attention_mask=__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}"       )







@require_flax
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       A__											=			(FlaxGPTJForCausalLM,) if is_flax_available() else ()





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  FlaxGPTJModelTester(self       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward_with_attn_mask(
                       __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       @tooslow
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  GPTaTokenizer.from_pretrained("gpt2"					,      pad_token="<|endoftext|>"					,      padding_side="left"       )
             lowerCamelCase__		:				List[str]    =  tokenizer(["Hello this is a long string", "Hey"]					,      return_tensors="np"					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"       )
             lowerCamelCase__		:				Optional[Any]    =  False
             lowerCamelCase__		:				str    =  model.config.eos_token_id

             lowerCamelCase__		:				int    =  jax.jit(model.generate       )

             lowerCamelCase__		:				str    =  jit_generate(
                 inputs["input_ids"]					,      attention_mask=inputs["attention_mask"]					,      pad_token_id=tokenizer.pad_token_id       ).sequences

             lowerCamelCase__		:				Tuple    =  tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  [
                 "Hello this is a long string of text.\n\nI'm trying to get the text of the",
                 "Hey, I'm a little late to the party. I'm going to",
             ]

             self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase       )





       @is_pt_flax_cross_test
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             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__		:				int    =  {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__		:				int    =  getattr(__lowerCamelCase					,      __lowerCamelCase       )

                         lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  pt_inputs["input_ids"].shape
                         lowerCamelCase__		:				List[str]    =  np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)       )
                         for batch_idx, start_index in enumerate(__lowerCamelCase       ):
                               lowerCamelCase__		:				Tuple    =  0
                               lowerCamelCase__		:				List[str]    =  1
                               lowerCamelCase__		:				Optional[Any]    =  0
                               lowerCamelCase__		:				Any    =  1
                         lowerCamelCase__		:				List[Any]    =  pt_model_class(__lowerCamelCase       ).eval()
                         lowerCamelCase__		:				List[str]    =  model_class(__lowerCamelCase					,      dtype=jnp.floataa       )

                         lowerCamelCase__		:				Optional[int]    =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __lowerCamelCase       )
                         lowerCamelCase__		:				Optional[int]    =  fx_state

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

                         lowerCamelCase__		:				Union[str, 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__		:				List[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 						lowerCAmelCase     (   self							:			str       ):


             '''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__		:				Union[str, Any]    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )
                         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[Any]    =  getattr(__lowerCamelCase					,      __lowerCamelCase       )

                         lowerCamelCase__		:				Optional[Any]    =  pt_model_class(__lowerCamelCase       ).eval()
                         lowerCamelCase__		:				Tuple    =  model_class(__lowerCamelCase					,      dtype=jnp.floataa       )

                         lowerCamelCase__		:				Optional[Any]    =  load_flax_weights_in_pytorch_model(__lowerCamelCase					,      fx_model.params       )
                         lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  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(__lowerCamelCase       ):
                               lowerCamelCase__		:				Any    =  0
                               lowerCamelCase__		:				Any    =  1
                               lowerCamelCase__		:				Union[str, Any]    =  0
                               lowerCamelCase__		:				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__		:				List[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__		:				Any    =  pt_model_class.from_pretrained(__lowerCamelCase					,      from_flax=__lowerCamelCase       )

                         with torch.no_grad():
                               lowerCamelCase__		:				Optional[int]    =  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       )





       @tooslow
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__		:				Union[str, Any]    =  model_class_name.from_pretrained("EleutherAI/gpt-j-6B"       )
                   lowerCamelCase__		:				List[str]    =  model(np.ones((1, 1)       )       )
                   self.assertIsNotNone(__lowerCamelCase       )


