
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=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	):
          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(lowerCamelCase	)

          snake_case__  =			model.init_cache(input_ids.shape[0]						,		lowerCamelCase	)
          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=lowerCamelCase						,		past_key_values=lowerCamelCase						,		position_ids=lowerCamelCase						,		)

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

          snake_case__  =			model(lowerCamelCase	)

          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(lowerCamelCase	)

          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]						,		lowerCamelCase	)
          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=lowerCamelCase						,		past_key_values=lowerCamelCase						,		position_ids=lowerCamelCase						,		)
          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=lowerCamelCase						,		position_ids=lowerCamelCase						,		)

          snake_case__  =			model(lowerCamelCase						,		attention_mask=lowerCamelCase	)

          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					( __UpperCamelCase    ,   __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						str      		=       (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      _A  :						Optional[Any]      		=       (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(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	)
      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(
                  lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	)
      @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=lowerCamelCase						,		truncation=lowerCamelCase	)

          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(lowerCamelCase						,		skip_special_tokens=lowerCamelCase	)

          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(lowerCamelCase						,		lowerCamelCase	)
      @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(lowerCamelCase						,		lowerCamelCase	)
                  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(lowerCamelCase						,		lowerCamelCase	)

                  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(lowerCamelCase	):
                      snake_case__  =			0
                      snake_case__  =			1
                      snake_case__  =			0
                      snake_case__  =			1
                  snake_case__  =			pt_model_class(lowerCamelCase	).eval()
                  snake_case__  =			model_class(lowerCamelCase						,		dtype=jnp.floataa	)

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

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

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

                  snake_case__  =			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	):
          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(lowerCamelCase						,		lowerCamelCase	)
                  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(lowerCamelCase						,		lowerCamelCase	)

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

                  snake_case__  =			load_flax_weights_in_pytorch_model(lowerCamelCase						,		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(lowerCamelCase	):
                      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(**lowerCamelCase	).to_tuple()

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

                  with torch.no_grad():
                      snake_case__  =			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	):



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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    if len(__lowerCAmelCase							) > 1:
        snake_case__  =			len(__lowerCAmelCase							) // 2  # Finds the middle of the array
        snake_case__  =			arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        snake_case__  =			arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        snake_case__  =			len(__lowerCAmelCase							)
        snake_case__  =			len(__lowerCAmelCase							)
        merge(__lowerCAmelCase							)  # Starts sorting the left.
        merge(__lowerCAmelCase							)  # Starts sorting the right
        snake_case__  =			0  # Left Counter
        snake_case__  =			0  # Right Counter
        snake_case__  =			0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                snake_case__  =			left_array[left_index]
                left_index += 1
            else:
                snake_case__  =			right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            snake_case__  =			left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            snake_case__  =			right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
     import doctest

     doctest.testmod()



