


"""simple docstring"""
import numpy as np
import requests







def     _a					(   )     ->					Any:
							snake_case_     							=		requests.get(
							    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
							    """master/Week1/ADRvsRating.csv"""					)
							snake_case_     							=		response.text.splitlines()
							snake_case_     							=		[]
							for item in lines:
														snake_case_     							=		item.split(""","""					)
														data.append(__lowerCamelCase					)
							data.pop(0					)  # This is for removing the labels from the list
							snake_case_     							=		np.matrix(__lowerCamelCase					)
							return dataset







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					List[Any]:
							snake_case_     							=		len_data

							snake_case_     							=		np.dot(__lowerCamelCase					,			data_x.transpose()					)
							prod -= data_y.transpose()
							snake_case_     							=		np.dot(__lowerCamelCase					,			__lowerCamelCase					)
							snake_case_     							=		theta - (alpha / n) * sum_grad
							return theta







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					List[str]:
							snake_case_     							=		np.dot(__lowerCamelCase					,			data_x.transpose()					)
							prod -= data_y.transpose()
							snake_case_     							=		np.sum(np.square(__lowerCamelCase					)					)
							snake_case_     							=		sum_elem / (2 * len_data)
							return error







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							snake_case_     							=		100_000
							snake_case_     							=		0.000_1550

							snake_case_     							=		data_x.shape[1]
							snake_case_     							=		data_x.shape[0] - 1

							snake_case_     							=		np.zeros((1, no_features)					)

							for i in range(0					,			__lowerCamelCase					):
														snake_case_     							=		run_steep_gradient_descent(__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					)
														snake_case_     							=		sum_of_square_error(__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase					)
														print(f"""At Iteration {i + 1} - Error is {error:.5f}"""					)

							return theta







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					List[str]:
							snake_case_     							=		sum(abs(y - predicted_y[i]					) for i, y in enumerate(__lowerCamelCase					)					)
							return total / len(__lowerCamelCase					)







def     _a					(   )     ->					int:
							snake_case_     							=		collect_dataset()

							snake_case_     							=		data.shape[0]
							snake_case_     							=		np.c_[np.ones(__lowerCamelCase					), data[:, :-1]].astype(__lowerCamelCase					)
							snake_case_     							=		data[:, -1].astype(__lowerCamelCase					)

							snake_case_     							=		run_linear_regression(__lowerCamelCase					,			__lowerCamelCase					)
							snake_case_     							=		theta.shape[1]
							print("""Resultant Feature vector : """					)
							for i in range(0					,			__lowerCamelCase					):
														print(f"""{theta[0, i]:.5f}"""					)


if __name__ == "__main__":
				main()







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   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__=14						,    a__=7						,    a__=True						,    a__=True						,    a__=True						,    a__=99						,    a__=32						,    a__=5						,    a__=4						,    a__=37						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=5_12						,    a__=0.02						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Any							=   parent
        snake_case :			str							=   batch_size
        snake_case :			str							=   seq_length
        snake_case :			str							=   is_training
        snake_case :			List[str]							=   use_input_mask
        snake_case :			str							=   use_labels
        snake_case :			Union[str, Any]							=   vocab_size
        snake_case :			Dict							=   d_model
        snake_case :			List[str]							=   num_hidden_layers
        snake_case :			Optional[int]							=   num_attention_heads
        snake_case :			Optional[Any]							=   ffn_dim
        snake_case :			Dict							=   activation_function
        snake_case :			List[str]							=   activation_dropout
        snake_case :			Any							=   attention_dropout
        snake_case :			str							=   max_position_embeddings
        snake_case :			Optional[int]							=   initializer_range
        snake_case :			Union[str, Any]							=   None
        snake_case :			int							=   0
        snake_case :			int							=   2
        snake_case :			Union[str, Any]							=   1

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size       )						,    3						,    self.vocab_size       )

        snake_case :			Union[str, Any]							=   None
        if self.use_input_mask:
         snake_case :			Tuple							=   random_attention_mask([self.batch_size, self.seq_length]       )

        snake_case :			Optional[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=a__						,    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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.prepare_config_and_inputs()
        snake_case	,       snake_case	,       snake_case :			List[str]							=   config_and_inputs
        snake_case :			List[str]							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case	,       snake_case :			List[str]							=   self.prepare_config_and_inputs()

        snake_case :			Tuple							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        snake_case :			Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2       )

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

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   20
        snake_case :			List[Any]							=   model_class_name(a__       )

        snake_case :			List[Any]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype='''i4'''       )

        snake_case :			Optional[Any]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )
        snake_case :			Tuple							=   model(
            input_ids[:, :-1]						,    attention_mask=a__						,    past_key_values=a__						,    position_ids=a__						,    )

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

        snake_case :			Dict							=   model(a__       )

        snake_case :			Union[str, Any]							=   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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   20
        snake_case :			Dict							=   model_class_name(a__       )

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

        snake_case :			List[str]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

        snake_case :			Optional[Any]							=   model(a__						,    attention_mask=a__       )
        snake_case :			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   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       _UpperCamelCase     		=   (FlaxXGLMForCausalLM,) if is_flax_available() else ()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   FlaxXGLMModelTester(self       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(a__						,    a__						,    a__						,    a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             a__						,    a__						,    a__						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   XGLMTokenizer.from_pretrained('''XGLM'''						,    padding_side='''left'''       )
        snake_case :			Optional[int]							=   tokenizer(['''Hello this is a long string''', '''Hey''']						,    return_tensors='''np'''						,    padding=a__						,    truncation=a__       )

        snake_case :			List[Any]							=   FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
        snake_case :			Optional[int]							=   1
        snake_case :			str							=   False

        snake_case :			str							=   jax.jit(model.generate       )

        snake_case :			Dict							=   jit_generate(inputs['''input_ids''']						,    attention_mask=inputs['''attention_mask''']       ).sequences

        snake_case :			Optional[Any]							=   tokenizer.batch_decode(a__						,    skip_special_tokens=a__       )

        snake_case :			Tuple							=   [
            '''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(a__						,    a__       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			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
          snake_case :			Union[str, Any]							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			int							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

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

          with torch.no_grad():
           snake_case :			int							=   pt_model(**a__       ).to_tuple()

          snake_case :			Optional[int]							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           pt_model.save_pretrained(a__       )
           snake_case :			Union[str, Any]							=   model_class.from_pretrained(a__						,    from_pt=a__       )

          snake_case :			List[Any]							=   fx_model_loaded(**a__       ).to_tuple()
          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output_loaded, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output_loaded[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			List[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
          snake_case :			str							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			List[Any]							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          snake_case :			Any							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
          snake_case :			Union[str, Any]							=   getattr(a__						,    a__       )

          snake_case :			List[str]							=   pt_model_class(a__       ).eval()
          snake_case :			Tuple							=   False
          snake_case :			Optional[int]							=   model_class(a__						,    dtype=jnp.floataa       )

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

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

          with torch.no_grad():
           snake_case :			Optional[int]							=   pt_model(**a__       ).to_tuple()

          snake_case :			Any							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           fx_model.save_pretrained(a__       )
           snake_case :			int							=   pt_model_class.from_pretrained(a__						,    from_flax=a__       )

          with torch.no_grad():
           snake_case :			int							=   pt_model_loaded(**a__       ).to_tuple()

          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )



       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case :			str							=   model_class_name.from_pretrained('''facebook/xglm-564M'''       )
         snake_case :			List[Any]							=   model(np.ones((1, 1)       )       )
         self.assertIsNotNone(a__       )






