
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, 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

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
     import torch




class     _SCREAMING_SNAKE_CASE     :
      def __init__(	self						,		lowerCamelCase						,		lowerCamelCase=14						,		lowerCamelCase=7						,		lowerCamelCase=True						,		lowerCamelCase=True						,		lowerCamelCase=False						,		lowerCamelCase=True						,		lowerCamelCase=99						,		lowerCamelCase=32						,		lowerCamelCase=5						,		lowerCamelCase=4						,		lowerCamelCase=37						,		lowerCamelCase="gelu"						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=5_12						,		lowerCamelCase=0.0_2						,		):
          snake_case__  = parent
          snake_case__  = batch_size
          snake_case__  = seq_length
          snake_case__  = is_training
          snake_case__  = use_input_mask
          snake_case__  = use_token_type_ids
          snake_case__  = use_labels
          snake_case__  = vocab_size
          snake_case__  = hidden_size
          snake_case__  = num_hidden_layers
          snake_case__  = num_attention_heads
          snake_case__  = intermediate_size
          snake_case__  = hidden_act
          snake_case__  = hidden_dropout_prob
          snake_case__  = attention_probs_dropout_prob
          snake_case__  = max_position_embeddings
          snake_case__  = initializer_range
          snake_case__  = None
          snake_case__  = vocab_size - 1
          snake_case__  = vocab_size - 1
          snake_case__  = vocab_size - 1
      def A_	(	self       ):
          snake_case__  = ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size       )

          snake_case__  = None
          if self.use_input_mask:
              snake_case__  = random_attention_mask([self.batch_size, self.seq_length]       )

          snake_case__  = GPTaConfig(
              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=__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 A_	(	self       ):
          snake_case__  = self.prepare_config_and_inputs()
          snake_case__  ,    snake_case__  ,    snake_case__  = config_and_inputs
          snake_case__  = {"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict
      def A_	(	self       ):
          snake_case__  ,    snake_case__  ,    snake_case__  = self.prepare_config_and_inputs()

          snake_case__  = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
          snake_case__  = 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						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = 20
          snake_case__  = model_class_name(__SCREAMING_SNAKE_CASE       )

          snake_case__  = model.init_cache(input_ids.shape[0]						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = jnp.ones((input_ids.shape[0], max_decoder_length)						,		dtype="i4"       )

          snake_case__  = jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)       )
          snake_case__  = model(
              input_ids[:, :-1]						,		attention_mask=__SCREAMING_SNAKE_CASE						,		past_key_values=__SCREAMING_SNAKE_CASE						,		position_ids=__SCREAMING_SNAKE_CASE						,		)

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

          snake_case__  = model(__SCREAMING_SNAKE_CASE       )

          snake_case__  = 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						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = 20
          snake_case__  = model_class_name(__SCREAMING_SNAKE_CASE       )

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

          snake_case__  = model.init_cache(input_ids.shape[0]						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

          snake_case__  = model(__SCREAMING_SNAKE_CASE						,		attention_mask=__SCREAMING_SNAKE_CASE       )

          snake_case__  = 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     _SCREAMING_SNAKE_CASE     (					_A				,    _A				,    unittest.TestCase       ):
      _A  :						Any					=   (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      _A  :						List[str]					=   (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
      def A_	(	self       ):
          snake_case__  = FlaxGPTaModelTester(self       )
      def A_	(	self       ):
          for model_class_name in self.all_model_classes:
              snake_case__  ,    snake_case__  ,    snake_case__  = 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 A_	(	self       ):
          for model_class_name in self.all_model_classes:
              snake_case__  ,    snake_case__  ,    snake_case__  = 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 A_	(	self       ):
          snake_case__  = GPTaTokenizer.from_pretrained("gpt2"						,		pad_token="</s>"						,		padding_side="left"       )
          snake_case__  = tokenizer(["Hello this is a long string", "Hey"]						,		return_tensors="np"						,		padding=__SCREAMING_SNAKE_CASE						,		truncation=__SCREAMING_SNAKE_CASE       )

          snake_case__  = FlaxGPTaLMHeadModel.from_pretrained("gpt2"       )
          snake_case__  = False
          snake_case__  = model.config.eos_token_id

          snake_case__  = jax.jit(model.generate       )

          snake_case__  = jit_generate(inputs["input_ids"]						,		attention_mask=inputs["attention_mask"]       ).sequences

          snake_case__  = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE						,		skip_special_tokens=__SCREAMING_SNAKE_CASE       )

          snake_case__  = [
              "Hello this is a long string of words. I'm going to start with the first one.\n",
              "Hey, I'm not sure if I'm going to be able to do",
          ]

          self.assertListEqual(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
      @is_pt_flax_cross_test
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = 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
                  snake_case__  = self._prepare_for_class(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
                  snake_case__  = {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  snake_case__  = model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  snake_case__  = getattr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )

                  snake_case__  ,    snake_case__  = pt_inputs["input_ids"].shape
                  snake_case__  = np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)       )
                  for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE       ):
                      snake_case__  = 0
                      snake_case__  = 1
                      snake_case__  = 0
                      snake_case__  = 1
                  snake_case__  = pt_model_class(__SCREAMING_SNAKE_CASE       ).eval()
                  snake_case__  = model_class(__SCREAMING_SNAKE_CASE						,		dtype=jnp.floataa       )

                  snake_case__  = convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		__SCREAMING_SNAKE_CASE       )
                  snake_case__  = fx_state

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

                  snake_case__  = 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       )
                      snake_case__  = model_class.from_pretrained(__SCREAMING_SNAKE_CASE						,		from_pt=__SCREAMING_SNAKE_CASE       )

                  snake_case__  = 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 A_	(	self       ):
          snake_case__  ,    snake_case__  = 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
                  snake_case__  = self._prepare_for_class(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
                  snake_case__  = {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  snake_case__  = model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  snake_case__  = getattr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )

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

                  snake_case__  = load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE						,		fx_model.params       )
                  snake_case__  ,    snake_case__  = pt_inputs["input_ids"].shape
                  snake_case__  = np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)       )
                  for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE       ):
                      snake_case__  = 0
                      snake_case__  = 1
                      snake_case__  = 0
                      snake_case__  = 1

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

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

                  snake_case__  = 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       )
                      snake_case__  = pt_model_class.from_pretrained(__SCREAMING_SNAKE_CASE						,		from_flax=__SCREAMING_SNAKE_CASE       )

                  with torch.no_grad():
                      snake_case__  = 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 A_	(	self       ):
          for model_class_name in self.all_model_classes:
              snake_case__  = model_class_name.from_pretrained("gpt2"						,		from_pt=__SCREAMING_SNAKE_CASE       )
              snake_case__  = model(np.ones((1, 1)       )       )
              self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )






"""simple docstring"""




from typing import TYPE_CHECKING

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


__A     :    int    =		{'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[Any]    =		[
	    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BitForImageClassification',
	    'BitModel',
	    'BitPreTrainedModel',
	    'BitBackbone',
	]


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		['BitImageProcessor']


if TYPE_CHECKING:
	from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bit import (
		    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BitBackbone,
		    BitForImageClassification,
		    BitModel,
		    BitPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bit import BitImageProcessor

else:
	import sys

	__A     :    Union[str, Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure)





