
from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowerCamelCase__        =						{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """Constraint""",
					    """ConstraintListState""",
					    """DisjunctiveConstraint""",
					    """PhrasalConstraint""",
					]
					lowerCamelCase__        =						[
					    """BeamHypotheses""",
					    """BeamScorer""",
					    """BeamSearchScorer""",
					    """ConstrainedBeamSearchScorer""",
					]
					lowerCamelCase__        =						[
					    """EpsilonLogitsWarper""",
					    """EtaLogitsWarper""",
					    """ForcedBOSTokenLogitsProcessor""",
					    """ForcedEOSTokenLogitsProcessor""",
					    """HammingDiversityLogitsProcessor""",
					    """InfNanRemoveLogitsProcessor""",
					    """LogitsProcessor""",
					    """LogitsProcessorList""",
					    """LogitsWarper""",
					    """MinLengthLogitsProcessor""",
					    """MinNewTokensLengthLogitsProcessor""",
					    """NoBadWordsLogitsProcessor""",
					    """NoRepeatNGramLogitsProcessor""",
					    """PrefixConstrainedLogitsProcessor""",
					    """RepetitionPenaltyLogitsProcessor""",
					    """SequenceBiasLogitsProcessor""",
					    """EncoderRepetitionPenaltyLogitsProcessor""",
					    """TemperatureLogitsWarper""",
					    """TopKLogitsWarper""",
					    """TopPLogitsWarper""",
					    """TypicalLogitsWarper""",
					    """EncoderNoRepeatNGramLogitsProcessor""",
					    """ExponentialDecayLengthPenalty""",
					    """LogitNormalization""",
					]
					lowerCamelCase__        =						[
					    """MaxNewTokensCriteria""",
					    """MaxLengthCriteria""",
					    """MaxTimeCriteria""",
					    """StoppingCriteria""",
					    """StoppingCriteriaList""",
					    """validate_stopping_criteria""",
					]
					lowerCamelCase__        =						[
					    """GenerationMixin""",
					    """top_k_top_p_filtering""",
					    """GreedySearchEncoderDecoderOutput""",
					    """GreedySearchDecoderOnlyOutput""",
					    """SampleEncoderDecoderOutput""",
					    """SampleDecoderOnlyOutput""",
					    """BeamSearchEncoderDecoderOutput""",
					    """BeamSearchDecoderOnlyOutput""",
					    """BeamSampleEncoderDecoderOutput""",
					    """BeamSampleDecoderOnlyOutput""",
					    """ContrastiveSearchEncoderDecoderOutput""",
					    """ContrastiveSearchDecoderOnlyOutput""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TFForcedBOSTokenLogitsProcessor""",
					    """TFForcedEOSTokenLogitsProcessor""",
					    """TFLogitsProcessor""",
					    """TFLogitsProcessorList""",
					    """TFLogitsWarper""",
					    """TFMinLengthLogitsProcessor""",
					    """TFNoBadWordsLogitsProcessor""",
					    """TFNoRepeatNGramLogitsProcessor""",
					    """TFRepetitionPenaltyLogitsProcessor""",
					    """TFTemperatureLogitsWarper""",
					    """TFTopKLogitsWarper""",
					    """TFTopPLogitsWarper""",
					    """TFForceTokensLogitsProcessor""",
					    """TFSuppressTokensAtBeginLogitsProcessor""",
					    """TFSuppressTokensLogitsProcessor""",
					]
					lowerCamelCase__        =						[
					    """TFGenerationMixin""",
					    """tf_top_k_top_p_filtering""",
					    """TFGreedySearchDecoderOnlyOutput""",
					    """TFGreedySearchEncoderDecoderOutput""",
					    """TFSampleEncoderDecoderOutput""",
					    """TFSampleDecoderOnlyOutput""",
					    """TFBeamSearchEncoderDecoderOutput""",
					    """TFBeamSearchDecoderOnlyOutput""",
					    """TFBeamSampleEncoderDecoderOutput""",
					    """TFBeamSampleDecoderOnlyOutput""",
					    """TFContrastiveSearchEncoderDecoderOutput""",
					    """TFContrastiveSearchDecoderOnlyOutput""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxForcedBOSTokenLogitsProcessor""",
					    """FlaxForcedEOSTokenLogitsProcessor""",
					    """FlaxLogitsProcessor""",
					    """FlaxLogitsProcessorList""",
					    """FlaxLogitsWarper""",
					    """FlaxMinLengthLogitsProcessor""",
					    """FlaxTemperatureLogitsWarper""",
					    """FlaxTopKLogitsWarper""",
					    """FlaxTopPLogitsWarper""",
					]
					lowerCamelCase__        =						[
					    """FlaxGenerationMixin""",
					    """FlaxGreedySearchOutput""",
					    """FlaxSampleOutput""",
					    """FlaxBeamSearchOutput""",
					]

if TYPE_CHECKING:
					from .configuration_utils import GenerationConfig
					from .streamers import TextIteratorStreamer, TextStreamer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
										from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
										from .logits_process import (
										    EncoderNoRepeatNGramLogitsProcessor,
										    EncoderRepetitionPenaltyLogitsProcessor,
										    EpsilonLogitsWarper,
										    EtaLogitsWarper,
										    ExponentialDecayLengthPenalty,
										    ForcedBOSTokenLogitsProcessor,
										    ForcedEOSTokenLogitsProcessor,
										    HammingDiversityLogitsProcessor,
										    InfNanRemoveLogitsProcessor,
										    LogitNormalization,
										    LogitsProcessor,
										    LogitsProcessorList,
										    LogitsWarper,
										    MinLengthLogitsProcessor,
										    MinNewTokensLengthLogitsProcessor,
										    NoBadWordsLogitsProcessor,
										    NoRepeatNGramLogitsProcessor,
										    PrefixConstrainedLogitsProcessor,
										    RepetitionPenaltyLogitsProcessor,
										    SequenceBiasLogitsProcessor,
										    TemperatureLogitsWarper,
										    TopKLogitsWarper,
										    TopPLogitsWarper,
										    TypicalLogitsWarper,
										)
										from .stopping_criteria import (
										    MaxLengthCriteria,
										    MaxNewTokensCriteria,
										    MaxTimeCriteria,
										    StoppingCriteria,
										    StoppingCriteriaList,
										    validate_stopping_criteria,
										)
										from .utils import (
										    BeamSampleDecoderOnlyOutput,
										    BeamSampleEncoderDecoderOutput,
										    BeamSearchDecoderOnlyOutput,
										    BeamSearchEncoderDecoderOutput,
										    ContrastiveSearchDecoderOnlyOutput,
										    ContrastiveSearchEncoderDecoderOutput,
										    GenerationMixin,
										    GreedySearchDecoderOnlyOutput,
										    GreedySearchEncoderDecoderOutput,
										    SampleDecoderOnlyOutput,
										    SampleEncoderDecoderOutput,
										    top_k_top_p_filtering,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tf_logits_process import (
										    TFForcedBOSTokenLogitsProcessor,
										    TFForcedEOSTokenLogitsProcessor,
										    TFForceTokensLogitsProcessor,
										    TFLogitsProcessor,
										    TFLogitsProcessorList,
										    TFLogitsWarper,
										    TFMinLengthLogitsProcessor,
										    TFNoBadWordsLogitsProcessor,
										    TFNoRepeatNGramLogitsProcessor,
										    TFRepetitionPenaltyLogitsProcessor,
										    TFSuppressTokensAtBeginLogitsProcessor,
										    TFSuppressTokensLogitsProcessor,
										    TFTemperatureLogitsWarper,
										    TFTopKLogitsWarper,
										    TFTopPLogitsWarper,
										)
										from .tf_utils import (
										    TFBeamSampleDecoderOnlyOutput,
										    TFBeamSampleEncoderDecoderOutput,
										    TFBeamSearchDecoderOnlyOutput,
										    TFBeamSearchEncoderDecoderOutput,
										    TFContrastiveSearchDecoderOnlyOutput,
										    TFContrastiveSearchEncoderDecoderOutput,
										    TFGenerationMixin,
										    TFGreedySearchDecoderOnlyOutput,
										    TFGreedySearchEncoderDecoderOutput,
										    TFSampleDecoderOnlyOutput,
										    TFSampleEncoderDecoderOutput,
										    tf_top_k_top_p_filtering,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .flax_logits_process import (
										    FlaxForcedBOSTokenLogitsProcessor,
										    FlaxForcedEOSTokenLogitsProcessor,
										    FlaxLogitsProcessor,
										    FlaxLogitsProcessorList,
										    FlaxLogitsWarper,
										    FlaxMinLengthLogitsProcessor,
										    FlaxTemperatureLogitsWarper,
										    FlaxTopKLogitsWarper,
										    FlaxTopPLogitsWarper,
										)
										from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

from typing import Any

import numpy as np
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						bool:
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[1]

			# If matrix not symmetric, exit right away.
			if np.allclose(SCREAMING_SNAKE_CASE_			,				matrix.T	) is False:
						return False

			# Get eigenvalues and eignevectors for a symmetric matrix.
			lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						np.linalg.eigh(SCREAMING_SNAKE_CASE_	)

			# Check sign of all eigenvalues.
			# np.all returns a value of type np.bool_
			return bool(np.all(eigen_values > 0	)	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Any:
			lowerCAmelCase__ : List[Any]   =						np.random.randn(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Any   =						np.dot(SCREAMING_SNAKE_CASE_			,				random_matrix.T	)
			assert _is_matrix_spd(SCREAMING_SNAKE_CASE_	)
			return spd_matrix
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_ = 1_000			,				SCREAMING_SNAKE_CASE_ = 1e-8			,				)							->						Any:
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[1]
			assert np.shape(SCREAMING_SNAKE_CASE_	)[0] == np.shape(SCREAMING_SNAKE_CASE_	)[0]
			assert _is_matrix_spd(SCREAMING_SNAKE_CASE_	)

			# Initialize solution guess, residual, search direction.
			lowerCAmelCase__ : Optional[Any]   =						np.zeros((np.shape(SCREAMING_SNAKE_CASE_	)[0], 1)	)
			lowerCAmelCase__ : str   =						np.copy(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						np.copy(SCREAMING_SNAKE_CASE_	)

			# Set initial errors in solution guess and residual.
			lowerCAmelCase__ : Union[str, Any]   =						1e9
			lowerCAmelCase__ : int   =						1e9
			lowerCAmelCase__ : Union[str, Any]   =						1e9

			# Set iteration counter to threshold number of iterations.
			lowerCAmelCase__ : List[str]   =						0

			while error > tol:
						# Save this value so we only calculate the matrix-vector product once.
						lowerCAmelCase__ : int   =						np.dot(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

						# The main algorithm.

						# Update search direction magnitude.
						lowerCAmelCase__ : List[str]   =						np.dot(ra.T			,				SCREAMING_SNAKE_CASE_	) / np.dot(pa.T			,				SCREAMING_SNAKE_CASE_	)
						# Update solution guess.
						lowerCAmelCase__ : Dict   =						xa + alpha * pa
						# Calculate new residual.
						lowerCAmelCase__ : Tuple   =						ra - alpha * w
						# Calculate new Krylov subspace scale.
						lowerCAmelCase__ : str   =						np.dot(r.T			,				SCREAMING_SNAKE_CASE_	) / np.dot(ra.T			,				SCREAMING_SNAKE_CASE_	)
						# Calculate new A conjuage search direction.
						lowerCAmelCase__ : Dict   =						r + beta * pa

						# Calculate errors.
						lowerCAmelCase__ : List[str]   =						np.linalg.norm(r - ra	)
						lowerCAmelCase__ : Tuple   =						np.linalg.norm(x - xa	)
						lowerCAmelCase__ : str   =						np.maximum(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

						# Update variables.
						lowerCAmelCase__ : List[Any]   =						np.copy(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : Dict   =						np.copy(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : str   =						np.copy(SCREAMING_SNAKE_CASE_	)

						# Update number of iterations.
						iterations += 1
						if iterations > max_iterations:
									break

			return x
def 					lowerCAmelCase__     (    )							->						None:
			lowerCAmelCase__ : Optional[Any]   =						3
			lowerCAmelCase__ : Optional[int]   =						_create_spd_matrix(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Tuple   =						np.random.randn(SCREAMING_SNAKE_CASE_			,				1	)
			lowerCAmelCase__ : int   =						np.dot(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Numpy solution.
			lowerCAmelCase__ : Optional[Any]   =						np.linalg.solve(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Our implementation.
			lowerCAmelCase__ : Optional[int]   =						conjugate_gradient(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Ensure both solutions are close to x_true (and therefore one another).
			assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
			assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()