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   A							:


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		Any ,						__UpperCAmelCase  :		List[str]=1_4 ,						__UpperCAmelCase  :		Optional[Any]=7 ,						__UpperCAmelCase  :		int=True ,						__UpperCAmelCase  :		int=True ,						__UpperCAmelCase  :		Optional[int]=False ,						__UpperCAmelCase  :		Dict=True ,						__UpperCAmelCase  :		Union[str, Any]=9_9 ,						__UpperCAmelCase  :		Optional[Any]=3_2 ,						__UpperCAmelCase  :		List[str]=5 ,						__UpperCAmelCase  :		str=4 ,						__UpperCAmelCase  :		Union[str, Any]=3_7 ,						__UpperCAmelCase  :		Optional[int]="gelu" ,						__UpperCAmelCase  :		Tuple=0.1 ,						__UpperCAmelCase  :		Union[str, Any]=0.1 ,						__UpperCAmelCase  :		Any=5_1_2 ,						__UpperCAmelCase  :		str=0.02 ,						)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						parent
         UpperCAmelCase__			=						batch_size
         UpperCAmelCase__			=						seq_length
         UpperCAmelCase__			=						is_training
         UpperCAmelCase__			=						use_input_mask
         UpperCAmelCase__			=						use_token_type_ids
         UpperCAmelCase__			=						use_labels
         UpperCAmelCase__			=						vocab_size
         UpperCAmelCase__			=						hidden_size
         UpperCAmelCase__			=						num_hidden_layers
         UpperCAmelCase__			=						num_attention_heads
         UpperCAmelCase__			=						intermediate_size
         UpperCAmelCase__			=						hidden_act
         UpperCAmelCase__			=						hidden_dropout_prob
         UpperCAmelCase__			=						attention_probs_dropout_prob
         UpperCAmelCase__			=						max_position_embeddings
         UpperCAmelCase__			=						initializer_range
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						vocab_size - 1
         UpperCAmelCase__			=						vocab_size - 1
         UpperCAmelCase__			=						vocab_size - 1


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



         UpperCAmelCase__			=						ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size	)

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

         UpperCAmelCase__			=						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=__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     lowercase_   (self  :		List[str]	)		->       Tuple:
         """simple docstring"""



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


   def     lowercase_   (self  :		Optional[int]	)		->       str:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						self.prepare_config_and_inputs()

         UpperCAmelCase__			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
         UpperCAmelCase__			=						ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2	)

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


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



         UpperCAmelCase__			=						2_0
         UpperCAmelCase__			=						model_class_name(__UpperCAmelCase	)

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

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

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

         UpperCAmelCase__			=						model(__UpperCAmelCase	)

         UpperCAmelCase__			=						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     lowercase_   (self  :		Tuple ,						__UpperCAmelCase  :		Optional[Any] ,						__UpperCAmelCase  :		Union[str, Any] ,						__UpperCAmelCase  :		int ,						__UpperCAmelCase  :		Dict	)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						2_0
         UpperCAmelCase__			=						model_class_name(__UpperCAmelCase	)

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

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

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

         UpperCAmelCase__			=						model(__UpperCAmelCase ,						attention_mask=__UpperCAmelCase	)

         UpperCAmelCase__			=						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   A							(       UpperCAmelCase_	,       UpperCAmelCase_	,       unittest.TestCase  ):
   __UpperCAmelCase				:				Optional[Any]          =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   __UpperCAmelCase				:				List[Any]          =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()


   def     lowercase_   (self  :		Dict	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						FlaxGPTaModelTester(self	)


   def     lowercase_   (self  :		List[str]	)		->       Optional[Any]:
         """simple docstring"""



         for model_class_name in self.all_model_classes:
               UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)


   def     lowercase_   (self  :		Optional[Any]	)		->       str:
         """simple docstring"""



         for model_class_name in self.all_model_classes:
               UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)


   @slow
   def     lowercase_   (self  :		str	)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						GPTaTokenizer.from_pretrained("gpt2" ,						pad_token="</s>" ,						padding_side="left"	)
         UpperCAmelCase__			=						tokenizer(["Hello this is a long string", "Hey"] ,						return_tensors="np" ,						padding=__UpperCAmelCase ,						truncation=__UpperCAmelCase	)

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

         UpperCAmelCase__			=						jax.jit(model.generate	)

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

         UpperCAmelCase__			=						tokenizer.batch_decode(__UpperCAmelCase ,						skip_special_tokens=__UpperCAmelCase	)

         UpperCAmelCase__			=						[
             "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(__UpperCAmelCase ,						__UpperCAmelCase	)


   @is_pt_flax_cross_test
   def     lowercase_   (self  :		str	)		->       Any:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						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
                     UpperCAmelCase__			=						self._prepare_for_class(__UpperCAmelCase ,						__UpperCAmelCase	)
                     UpperCAmelCase__			=						{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

                     UpperCAmelCase__			=						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,						__UpperCAmelCase	)
                     UpperCAmelCase__			=						fx_state

                     with torch.no_grad():
                           UpperCAmelCase__			=						pt_model(**__UpperCAmelCase	).to_tuple()

                     UpperCAmelCase__			=						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	)
                           UpperCAmelCase__			=						model_class.from_pretrained(__UpperCAmelCase ,						from_pt=__UpperCAmelCase	)

                     UpperCAmelCase__			=						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     lowercase_   (self  :		Optional[int]	)		->       List[str]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						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
                     UpperCAmelCase__			=						self._prepare_for_class(__UpperCAmelCase ,						__UpperCAmelCase	)
                     UpperCAmelCase__			=						{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

                     UpperCAmelCase__			=						pt_model_class(__UpperCAmelCase	).eval()
                     UpperCAmelCase__			=						model_class(__UpperCAmelCase ,						dtype=jnp.floataa	)

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

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

                     with torch.no_grad():
                           UpperCAmelCase__			=						pt_model(**__UpperCAmelCase	).to_tuple()

                     UpperCAmelCase__			=						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	)
                           UpperCAmelCase__			=						pt_model_class.from_pretrained(__UpperCAmelCase ,						from_flax=__UpperCAmelCase	)

                     with torch.no_grad():
                           UpperCAmelCase__			=						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     lowercase_   (self  :		int	)		->       Optional[Any]:
         """simple docstring"""



         for model_class_name in self.all_model_classes:
               UpperCAmelCase__			=						model_class_name.from_pretrained("gpt2" ,						from_pt=__UpperCAmelCase	)
               UpperCAmelCase__			=						model(np.ones((1, 1)	)	)
               self.assertIsNotNone(__UpperCAmelCase	)




from __future__ import annotations

UpperCamelCase__       							=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class   A							:


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		int	)		->       None:
         """simple docstring"""



         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						[-1] * length
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						None





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



         return f"""Node(min_value={self.minn} max_value={self.maxx})"""




def      lowerCAmelCase_				(  __A							)  ->       Node | None:







      '''simple docstring'''


      UpperCAmelCase__			=						Node(len(__A							)							)
      UpperCAmelCase__	,   UpperCAmelCase__			=						min(__A							), max(__A							)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      UpperCAmelCase__			=						(root.minn + root.maxx) // 2

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]

      for index, num in enumerate(__A							):
            if num <= pivot:
                  left_arr.append(__A							)
            else:
                  right_arr.append(__A							)
            UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      return root




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      UpperCAmelCase__			=						(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left,						__A,						node.map_left[index] - 1							)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right,						__A,						index - node.map_left[index]							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if start > end:
            return 0
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						__A							)
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						start - 1							)
      return rank_till_end - rank_before_start




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      UpperCAmelCase__			=						node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left,						__A,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						)
      else:
            return quantile(
                node.right,						index - num_elements_in_left_tree,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      UpperCAmelCase__			=						range_counting(
          node.left,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						__A,						__A,						)
      UpperCAmelCase__			=						range_counting(
          node.right,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						__A,						__A,						)
      return left + right


if __name__ == "__main__":
      import doctest

      doctest.testmod()



